Mechanical Systems and Signal Processing Content/Papers/EMD...valve events, worn piston rings and liners, etc., become detectable even without the comparison to the baseline. Hence,

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Mechanical Systems and Signal Processing

Mechanical Systems and Signal Processing 24 (2010) 193–210

0888-32

doi:10.1

� Cor

E-m

journal homepage: www.elsevier.com/locate/jnlabr/ymssp

EMD-based fault diagnosis for abnormal clearance betweencontacting components in a diesel engine

Yujun Li a, Peter W. Tse a,�, Xin Yang b, Jianguo Yang b

a Smart Engineering Asset Management Laboratory (SEAM), City University of Hong Kong, Hong Kong, Chinab School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, Hubei, China

a r t i c l e i n f o

Article history:

Received 22 April 2009

Received in revised form

24 June 2009

Accepted 27 June 2009Available online 4 July 2009

Keywords:

Engine fault diagnosis

Empirical mode decomposition

Hilbert–Huang transform

Condition monitoring

Signal processing

70/$ - see front matter & 2009 Elsevier Ltd. A

016/j.ymssp.2009.06.012

responding author. Tel.: +852 27888431; fax:

ail address: [email protected] (P.W. Tse)

a b s t r a c t

The accuracy of fault diagnostic systems for diesel engine-type generators relies on a

comparison of the currently extracted sensory features with those captured during

normal operation or the so-called ‘‘baseline.’’ However, the baseline is not easily

obtained without the required expertise. Even worse, in an attempt to save costs, many

of the diesel engine generators in manufacturing plants are second hand or have been

purchased from unknown suppliers, meaning that the baseline is unknown. In this

paper, a novel vibration-based fault diagnostic method is developed to identify the vital

components of a diesel engine that have abnormal clearance. The advantage of this

method is that it does not require the comparison of current operating parameters to

those collected as the baseline. First, the nominal baseline is obtained via theoretical

modeling rather than being actually captured from the sensory signals in a healthy

condition. The abnormal clearance is then determined by inspecting the timing of

impacts created by the components that had abnormal clearance during operation. To

detect the timing of these impacts from vibration signals accurately, soft-re-sampling

and empirical mode decomposition (EMD) techniques are employed. These techniques

have integrated with our proposed ranged angle (RA) analysis to form a new ranged

angle-empirical mode decomposition method (RA-EMD). To verify the effectiveness of

the RA-EMD in detecting the impacts and their times of occurrence, their induced

vibrations are collected from a series of generators under normal and faulty engine

conditions. The results show that this method is capable of extracting the impacts

induced by vibrations and is able to determine their times of occurrence accurately even

when the impacts have been overwhelmed by other unrelated vibration signals. With

the help of the RA-EMD, clearance-related faults, such as incorrect open and closed

valve events, worn piston rings and liners, etc., become detectable even without the

comparison to the baseline. Hence, proper remedies can be applied to defective diesel

engines to ensure that valuable fuel is not wasted due to the incorrect timing of

combustion as well as unexpected fatal breakdown, which may cause loss of production

or even human casualties, can be minimized.

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Y. Li et al. / Mechanical Systems and Signal Processing 24 (2010) 193–210194

1. Introduction

During the past few decades, vibration-based techniques have been proven to be effective in detecting faults in rotatingmachinery. However, the application of vibration-based fault diagnosis to diesel engines is rare. This is essentially because thenature of the vibrations generated by diesel engines is highly transient and non-stationary. Moreover, diesel engines alwayscontain the response of multiple exciting forces. All of these factors make the application of vibration-based techniques to dieselengines very difficult, as they are more effective for vibrations that exhibit stationary and periodic behavior.

Many advanced techniques have been proposed to cope with the special nature of vibrations generated by dieselengines. Antoni et al. [1], for example, proposed a monitoring method based on a general framework of the cyclostationaryprocess, which is suited for describing cyclic-based phenomena. Other effective techniques for dealing with non-stationaryvibrations are time–frequency analyses, the most popular of which are the short-time Fourier transform, the wavelettransform, and similar types of analyses, which are capable of characterizing the vibrations in both the time and frequencydomains. Hence, they are theoretically feasible for the detection of vibrations with transient and non-stationary behavior.Geng et al. [2] and Geng and Chen [3] proposed an applicable diagnostic approach for extracting the signature of impactsusing time–frequency analysis. Another technique focuses on the identification of source signals from the collectedvibrations. Pontoppidan et al. [4] reported the application of mean field independent component analysis (ICA) for dieselengine health monitoring. Subsequent to ICA, the authors of this paper and other researchers used blind source separation(BSS) [5,6] to identify the unknown vibration sources from the collected vibrations.

The in-cylinder pressure trace is an important indicator of the combustion performance of a diesel engine. Research [7,8]has been conducted to reconstruct the trace of the cylinder pressure from vibration signals collected from the engine bodyusing the de-convolution approach. However, results of these advanced methods, such as time–frequency analysis, forpractical applications have not been satisfactory, particularly for on-line fault diagnosis. The Hilbert–Huang transform(HHT) [9,10] is self-adaptive in nature. It is able to decompose a signal on the basis of its frequency content and variation.One application of the HHT is the empirical mode decomposition (EMD) technique, which is suitable for dealing with non-stationary signals. EMD is a data-driven and self-adaptive process. The decomposed components, which are called intrinsicmode functions (IMFs), can be determined from the nature of the raw signal itself. Unlike the wavelet transform, for whichthe user has to pre-determine a mother wavelet and then the level of decomposition manually, EMD is able to performdecomposition of the raw signal and automatically determine the level of decomposition based on the nature of that rawsignal. Fault diagnosis based on the HHT or EMD has been improved and successfully applied to rotating machines, asreported in other studies by one of the authors of this paper [11,12]. However, little research has been carried out on the useof these techniques in reciprocating machines, such as diesel engines. In the study reported in this paper, the EMD methodwas investigated to verify its ability to extract the impact induced by vibrations from a number of cylinders in a dieselengine. These vibrations usually exhibit a transient nature and undergo interference from vibrations generated by othercomponents that are unrelated to the combustion process.

There are a number of contacting components that move in relation to the motion of a diesel engine, such as the valvetrain mechanism, the piston ring/liner set, and so on. These types of mechanisms usually generate transient impacts whenthey are moving and have contact with other components. Liu et al. [13] reported the detection of valve train faults via thepartial sampling and feature averaging methods. This approach needs reference data for comparison and then judges thehealthy condition of the inspected valve train. However, in practical applications, such as those for the diesel engine of anyvehicle, the reference data that denote the healthy condition of a diesel engine cannot be easily obtained due to thecomplexity contained within the external disturbance and the internal exciting forces during the vehicle’s movement onroads. In the study reported herein, the occurrence times of impacts were used as indicators of the size of contactingcomponent clearance with no need for any reference data. Therefore, this method is more versatile and practical to use forthe diagnosis of faults in diesel engines.

The remainder of the paper is organized as follows. The second section describes the experiments that were conductedto carefully gather the characteristics of the various faulty vibrations caused by improper clearance. The third sectionreports the analysis and identification of these characteristics in the time domain. Based on the instantaneous angularspeed of the diesel engine inspected, variations in vibrations from cycle to cycle were observed. Subsequent to cyclicvariation analysis of these vibrations, the fourth section presents our proposed ranged angle-empirical modedecomposition analysis (RA-EMD), which was used to detect the existence of the abnormal clearance of the contactingcomponents. The fifth section reports the results obtained from our proposed theoretical analysis of abnormal valve trainclearance and the use of our RA-EMD to detect such clearance in a real diesel engine. This method has been verifiedthrough experiments conducted on both faulty and normal diesel engines. To further verify the robustness of RA-EMD, inthe sixth section, a worn piston ring fault is analyzed using the method, with successful results.

2. Configuration of the generator and its experimental setup

2.1. Characteristics of the generator

To study the characteristics of the vibrations generated from the diesel engine of a generator, a 4108D diesel enginegenerator, which is commonly used in industry in China, was chosen for our investigation. This generator consists of two

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Y. Li et al. / Mechanical Systems and Signal Processing 24 (2010) 193–210 195

main parts, a diesel engine and an alternator for electricity generation. The diesel engine, which has two cylinder heads, isan in-line, water-cooled four-stroke engine with four cylinders using direct fuel injection (FI). Each head is shared by twocylinders. The technical parameters are shown in Table 1.

An image of the diesel engine generator under investigation and its schematic diagram for the experimental setup areshown in Fig. 1. Two accelerometers were mounted on the cylinder head of cylinder 1 in the vertical direction and on thecylinder block of cylinder 4 in the horizontal direction (pointed into Fig. 1). The top dead center (TDC) and the crank anglesignals during rotation were measured via two angular encoders attached to the flywheel. Four pressure sensors wereinstalled to each of the four cylinders to collect the individual in-cylinder pressures. All of the sensory signals werecaptured simultaneously through a multi-channel data acquisition card and then stored in a computer for further analysis.

2.2. Introduction of faults to the engine

Abnormal clearance of a valve train (larger or smaller clearance than that specified by the generator manufacturer) maybe caused by incorrect positioning of the valve’s rocket lever adjusting screw, excessive gross clearance in the valve tappet,or a worn or spalled cam or its roller. To create abnormal clearance in the intake valve train of cylinder 1, the valve rocketlever screw was turned 21 to adjust the gross valve tappet clearance from 0.4 mm (the normal value as listed in Table 1) to0.25 mm (less than the normal value) and 0.70 mm (more than the normal value). For a worn piston ring fault, the first andthird rings were worn as shown in the diagram on the right-hand side of Fig. 2. The thickness of the rings was reduced byDt ¼ 1 mm from that of the normal piston rings.

Two sets of tests were conducted. The first was designed to allow observation of the vibration characteristics when thediesel engine was running healthily at two operating conditions, that is, when generating 16 and 32 kW of power, at arotating speed of 1400 rev/min. The second set of tests was conducted for observation of the vibration characteristics whenthe diesel engine was suffering from abnormal clearance in the valve train and worn piston rings. Based on theseconditions, the experiments were conducted under six different work cases, as listed in Table 2.

3. Revealing the transitory characteristics of vibrations during combustion

During these experiments, the characteristics of vibrations generated by diesel engine were observed and then analyzedin the time domain. From the results of analysis of the transitory characteristics of the vibrations, variations in vibrationsfrom cycle to cycle have been revealed. Based on the analysis of the results, we observed that instantaneous variations wereinduced by temporal performance of the engine. Hence, we developed our newly proposed technique that utilizedtemporal variations according to each collected instantaneous angular speed of the crankshaft for detecting any anomalyoccurring in the combustion process of the inspected engine.

Table 1Specifications of the 4108D diesel engine.

Item Value Item Value

Number of cylinder 4 Intake valve opening 141 before TDC

Rating power 40 kW Intake valve closing 441 after BDC

Rating speed 1500 rev/min Exhaust valve opening 561 before BDC

Intake valve train clearance 0.40 mm Exhaust valve closing 121 after TDC

Exhaust valve train clearance 0.45 mm Fuel injection 14721 before TDC

Firing sequence 1–3–4–2

TDC—top dead center; BDC—bottom dead center.

The above opening/closing angles of intake/exhaust valve correspond only to the valve train that has normal clearance.

Pressure sensor 4

Cyl. 1

Engine Alternator

DAQCard

TDC & Crank Angle detection

Accelerometer 1

Accelerometer 4

Fig. 1. Image of the tested generator and its experimental setup.

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o x

y

�t

Cylinder wall

1st ring

3rd ring

Normal ring

Worn ring

Fig. 2. Locations of the worn piston rings and images of a normal and worn ring for comparison purposes.

Table 2Operating parameters of six different work cases that were selected for performing the required experiments.

Work case Speed (rev/min) Load (kW) Condition

1 1400 16 Normal condition

2 1400 32 Normal condition

3 1400 32 Tight clearance—0.25 mm (intake valve 1)

4 1400 32 Excessive clearance—0.70 mm (intake valve 1)

5 1400 16 Worn piston rings

6 1400 32 Worn piston rings


3.1. Analysis of vibration in the time domain

It is well known that most of the vibrations generated by a diesel engine are the result of a sequence of multiple impacts,with each impact having its own time of occurrence. Typically, there are five main impacts of valve operation and themovement of the piston set: intake valve closing (IVC), intake valve opening (IVO), exhaust valve closing (EVC), exhaustvalve opening (EVO), and the rapid rise in in-cylinder pressure during the combustion process. According to thefundamentals of internal combustion engines [14], vibrations have a close relationship with valve and injection timings.Fig. 3 schematically displays the theoretical times of occurrence of the valve events and combustion for a 4108D dieselengine. In Figs. 3(a)–(d), it can be clearly seen that the aforementioned impacts occur at different timings for a singlecylinder. The impact created by a particular valve event is distinguished from that created by another. However, if allcylinders are considered, as shown in Fig. 3(e), then the timings of these events are closely located, especially at the bandscentered at 01, 1801, 3601, and 5401.

Fig. 4 shows the temporal waveform of vibrations measured by Accelerometer 1 mounted on the cylinder head ofcylinder 1 (Fig. 1) during one combustion cycle, with the diesel engine operating under work case 1 (Table 2). The realresponse signature of each impact is not exactly consistent with the theoretical occurrence time of the valve. For example,the location of EVC1 (the exhaust valve closing in cylinder 1) in Fig. 4 is delayed by a few degrees of angle, as comparedwith the angle specified in the generator manual or that listed in Table 1. This is due to the dispersion and reverberation ofstress waves that propagate through the structure and errors between the designed and real parameters of the dieselengine tested, for example, the valve train clearances. Vibration signatures other than those of the aforementioned impactsare shown in Fig. 4, such as the vibrations around 1801 and 5401. These are due to the vibration interference derived fromthe adjacent cylinder’s impacts. Obviously, the vibration at around 5401 is derived from the combustion process of cylinder2, as this cylinder is adjacent to cylinder 1. Hence, singling out the contributions of each individual impact over a large setof overlapping vibrations is a great challenge.

3.2. Variation of vibrations from cycle to cycle

The appearance of overlapping temporal waveforms of vibrations due to the existence of multiple sources makes thedetection of the individual impact of each source or event very difficult. Another difficulty lies in the variation of cycleswhen the diesel engine pistons are operating. Cyclic variation may contribute to substantial variations in amplitude and theoccurrence of events in time over different combustion cycles even if the diesel engine is working under a steady operatingcondition. Table 3 shows the statistical results of vibrations obtained during the first 100 cycles when the diesel engine wasoperating under work case 1. Table 3 lists the variation ranges of the peak valve and the corresponding time as the valvebegan operation. These cyclic variations cannot be neglected, as they may provide incorrect information in fault diagnosis.

This cyclic variation in vibrations occurs for a number of reasons, primarily variation in the combustion process overcombustion cycles and the error derived from time-invariant sampling. The variation in the combustion process also

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Cylinder 1

Cylinder 3

Cylinder 4

Cylinder 2

+

+

+

Synthesized

Timing

=

0 720360 540

FIIVC EVCIVOEVO

FIIVC EVCIVO EVO

FIIVC EVO IVOEVC

FIIVCEVC EVOIVO

EV

C4

IVC

3

EV

O1

IVO

2 FI3

EV

C2

IVC

4

EV

O3

IVO

1 FI4

EV

C1

IVC

2

EV

O4

IVO

3 FI2

EV

C3

IVC

1

EV

O2

IVO

4 FI1

EVC-exhaust valve closing, EVO-exhaust valve opening, IVC-intake valve closing,IVO-intake valve opening, FI-fuel injection. The numerical values of 1 to 4 denote the number ofcylinder that is in action.

180

0 720360 540180

0 720360 540180

0 720360 540180

0 720360 540180

Fig. 3. Theoretical occurrences of intake/exhaust valve opening/closing and fuel injection for a 4108D diesel engine.

FI1IVC1EVC1IVO1EVO1200

-110

-50

0

50

100

150

50

0

10

20

30

40

Crank Angle (degree)

7200 60 120 180 240 300 360 420 480 540 600 660

Pressure

Acceleration

Pres

sure

(ba

r)

Acc

eler

atio

n (g

)

Fig. 4. Temporal vibration waveform collected from Accelerometer 1 (Fig. 1) for cylinder 1 during one combustion cycle when the engine is operating

under work case 1.


depends on a number of factors, including different compositions of the fuel–air mixture, the amount of recycled gasessupplied to the cylinder, and the aerodynamics of the diesel engine. This variation in the combustion process is typicallyreflected in the variation of in-cylinder pressure. The temporal waveform of in-cylinder pressure has strong periodicbehavior, especially at low-frequency bands [15]. The error derived from time-invariant sampling is caused by the variationin diesel engine speed from cycle to cycle. Fig. 5 shows the instantaneous angular speed of the crankshaft captured fromone combustion cycle of a 4108D diesel engine under the work case 1. It can be seen that the speed of the crankshaftchanges from 1393 to 1411 rev/min. If the vibrations are sampled in the time-invariant interval mode, then the amplitude of

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1410

1394

1398

1400

1402

1405

1408


7200 60 120 180 240 300 360 420 480 540 600 660

Spee

d (r

ev/m

in)

Fig. 5. Instantaneous angular speed of one combustion cycle when the engine is working under the operating conditions of work case 1.

Table 3Statistical results of vibration signals obtained from 100 cycles when engine was working under the operating condition of work case 1.

Event name Variation in peak (g) Range of the corresponding timing (ms)

Exhaust valve closing 3.52–11.14 0.18

Intake valve closing 3.15–13.68 4.59


vibration, which depends on the position of the crank angle, will be distorted. For example, the time at which a valve closeshas a close relationship to the crank angle of the crankshaft. Real vibrations generated while the valve is closing will bedistorted if the time-invariant interval sampling mode is used. To avoid such distortion, we propose the use of the RA-EMDmethod, the details of which are presented in Section 4.

4. Ranged angle-EMD analysis

As described in Section 3.2, the time-invariant interval sampling mode usually causes distortion of angle-dependentvibrations. Collection of sensory data in the angle-invariant interval is definitely more suitable than that collected in thetime-invariant interval if accurate fault diagnosis is desired. The angle-invariant sampling mode ensures that the collectionof vibrations is less affected by variations in diesel engine speed.

4.1. Soft re-sampling

There are two basic approaches for obtaining signals sampled according to the crank angle [16]. The first approach isbased on hardware and utilizes a tachometer. The sampling period in the time domain is controlled by the tachometersignal. The second approach is based on soft re-sampling. First, the vibration and tachometer signals are sampled at a fixedsampling rate. Then, the vibration signal is digitally re-sampled based on the tachometer signal into a signal with aconstant-angle interval. The soft-re-sampling approach requires neither a high-cost analog anti-aliasing filter nor atachometer. Hence, it was employed in this study to conduct vibration re-sampling in the constant-angle domain. Theprocedures and rationale for using soft re-sampling can be found in Refs. [16,17]. The main steps of this approach can besummarized as follows:

(1)
synchronize all the sensory signals collected, including the pulses generated by the encoder (for capturing the angularspeed), the tachometer (for capturing the TDC position), the vibrations, and the pressure generated by each cylinder;
(2)
determine the time instants of re-sampling with the help of these pulses; (3) calculate the vibration and/or pressure signal values for all of the determined re-sampling time instants by using an
interpolation method, such as linear, cubic, or spline interpolation.

After completing the process of soft re-sampling to the signals, the vibration signals are changed from time to angledomain signals. Hence, the post-processed signals in the angle domain is less dependent on the fluctuation of thecrankshaft’s rotational speed.

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4.2. Brief review of empirical mode decomposition (EMD)

As previously mentioned, the soft-re-sampling process can effectively eliminate errors induced by the time-invariantsampling mode. However, the non-stationary nature of the vibrations and the overlapping of multiple impacts still createdifficulties for the accurate identification of the temporal events during the combustion process. Huang et al. [9] recentlyproposed the EMD method, which is capable of decomposing the original signal into a collection of intrinsic modefunctions based on the local characteristic time scales of the signals. EMD is a data-driven and self-adaptive method, aseach IMF is determined by the signal itself rather than being determined a priori by the user. Therefore, using the EMDmethod to decompose vibrations generated from a diesel engine can minimize the overlapping phenomenon and enhancethe isolation of each temporal event during combustion.

This method is derived from the assumption that any signal consists of a series of simple but different intrinsic modes ofoscillation. Each linear or non-linear mode will have the same number of extreme and zero crossings [9], and there is onlyone extreme between successive zero crossings. Each mode should be independent of the others. In this way, each signalcan be decomposed into a number of IMFs, and each IMF must satisfy the following definitions [10]:

(a)
in the entire data set, the number of extrema and the number of zero crossings must either be equal or differ at most byone;
(b)
at any point, the mean values of the envelope defined by the local maxima and that defined by the local minima arezero.
An IMF represents a simple oscillatory mode compared with a common simple harmonic function. By definition, anytemporal signal, x(t), can be decomposed as follows:

(1)
Identify all of the local extrema and then connect all of the local maxima with a cubic spline line as the upper envelope. (2) Repeat step (1) for the local minima to produce the lower envelope. The upper and lower envelopes should cover all of
the data between them.
(3) The mean of the upper and lower envelope value is designated as m1, and the difference between the signal x(t) and m1
is the first component, h1, i.e.,

xðtÞ �m1 ¼ h1. (1)

Ideally, if h1 is an IMF, then h1 is the first component of x(t).
(4) If h1 is not an IMF, then it is treated as the original signal, and steps (1) to (3) are repeated; then,
h1 �m11 ¼ h11, (2)

where m11 is the mean of the upper and lower envelope values of h1. After repetitive sifting, i.e., up to k times until h1k

becomes an IMF, that is,

h1ðk�1Þ �m1k ¼ h1k. (3)

It is then designated as

c1 ¼ h1k, (4)

which is the first IMF component of the original data; c1 should contain the finest scale or the component of the signalthat has the shortest period.

(5)
Separate c1 from x(t) to obtain the residue r1 as
r1 ¼ xðtÞ � c1, (5)

where r1 is treated as the original data. By repeating the above steps, the second IMF component, r2 of x(t), can beobtained. If the above procedures are repeated n times, then n types of the IMFs of signal x(t) can be created by

r1 � c2 ¼ r2

..

.

rn�1 � cn ¼ rn

9>>=>>;

. (6)

(6)
The decomposition process is stopped when rn becomes a monotonic function from which no more IMFs can beextracted. By summing up Eqs. (5) and (6), the general form is
xðtÞ ¼Xn

j¼1

cj þ rn. (7)

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Residue rn is the mean trend of x(t). The IMFs c1, c2,y, cn include different frequency bands that range from high to low. Thefrequency components contained in each frequency band are different, and they change according to the variation of signalx(t), whereas rn represents the central tendency of signal x(t).

4.3. Proposed ranged angle-EMD analysis (RA-EMD)

Based on EMD and instantaneous variations of the crankshaft’s angular speed, the new RA-EMD analysis has beendeveloped. The procedures are briefly introduced in the flow chart presented in Fig. 6, which also illustrates the resultsobtained from each step. First, data acquisition is used to capture the required raw signal. Second, the soft-re-samplingmethod is used to rearrange the temporal signal into angle domain signals, as x(y). Third, to decrease the interference ofother impacts, a rectangular window must be applied to store the signals in one working cycle (from 01 to 7201), and thenthe signals that belong to the other crank angle are truncated. After truncation, the re-sampled signals x(y) correspond onlyto temporal impacts caused by each time event within a working cycle and are named as the ranged signals, xr(y). In thisstep, the signal filtering window should be selected so that the filtered signal appearing in the window will contain allcrank angles when the temporal impacts created by the valves appear. Fourth, the ranged signals, xr(y), are decomposedinto a number of IMFs via EMD decomposition. Fifth, the IMFs that are related to the impacts are selected from all of thedecomposed IMFs. Usually, temporal impacts will appear in higher frequency range of the frequency spectrum generatedby the raw signal. It is well known that the end effects are significant in the IMFs that decompose low-frequencycomponents. The end effects are not significant in IMFs that decompose high-frequency components. In this analysis, theIMFs that contain impacts belong to high-frequency components. Hence, the end effects are usually very small ornegligible. For detecting valves’ impacts, only the first several IMFs that contain the high-frequency impacts are needed tobe selected. Those irrelevant IMFs, which contain lower-frequency components, can be deleted in the decomposed process.Sixth, after the vital features related to the impacts or time events of combustion are extracted from the selection, faultdiagnosis of the diesel engine is carried out using the extracted features. As mentioned above, these processes are includedin the flow chart shown in Fig. 6, which also displays the expected results that were generated from each of the processesapplied to a typical working cycle of the 4108D diesel engine.

5. Detection of abnormal valve train clearance via RA-EMD

5.1. Characteristic analysis of vibration when abnormal clearance occurs in a valve train

5.1.1. Theoretical analysis of cam mechanism

Valves allow air/fuel mixture to enter into a cylinder’s compartment and the exhaust of residue to leave the cylinder’scompartment after one combustion cycle of a cylinder has been completed. As shown in Fig. 7(a), a camshaft uses a cam topush against a valve so that it can be opened when the camshaft is rotated to its specific profile. A spring located on top ofthe valve returns it to the closed position. The lifting of the valve depends on the cam profile, as shown in Fig. 7(b), and thevalve train clearance. When the clearance of the valve train increases, the time for valve opening lags and that for valveclosing advances. In contrast, when smaller valve train clearance occurs, the timing of valve opening advances and that ofvalve closing lags. Three valve lift curves are shown in Fig. 7(c). These represent situations in which the valve train has tight,normal, and excessive clearance. For instance, when the valve train has excessive clearance, the energy of vibration inducedduring valve opening and closing increases. The time of valve opening is delayed and the time of valve closing is advanced,as shown in Fig. 7(a). These time events in terms of the crank angle, which are due to excessive valve train clearance,change according to the condition of the valve train, but not the working condition of the diesel engine. However, if aninspector uses only the energy of vibration to observe the proper clearance of the valve train, then the result could bemisleading. A change in the diesel engine operating condition, such as running at different speeds or loads, also affects theamount of vibration energy. Hence, using only the change in vibration energy to inspect the clearance is unreliable. Instead,one must use the time of valve opening or closing in terms of the crank angle to assess whether there is proper valve trainclearance. Failure to detect such anomalous valve train clearance may not only reduce the efficiency of the engine but alsosubstantially waste the fuel consumed.

5.1.2. Theoretical analysis of valve lift

To verify the time of IVC, a theoretical model is used here to sketch the time event of the valve lift curve. The theoreticalvalve lift can be calculated from the profile data of the cam, the geometrical dimension of the rocker without consideringthe effects of oil film, and the transformation of the material from normal to high temperature. Detailed methods anddescriptions for modeling the valve lift curve can be found in the thesis of the first author of this paper [18]. Fig. 7(b) showsthe relationship between the push-rod lift and the cam profile at different crank angle positions. This relationship can bedescribed by the following equations.

x ¼ ðr þ sÞ sinðdÞ þ ðds=ddÞ cosðdÞ;y ¼ ðr þ sÞ cosðdÞ þ ðds=ddÞ sinðdÞ; (8)

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100

-100

0

0.0850 0.02 0.04 0.06

560 670

100

-100

0



0 120 240 360 480 600

5

-10

-5

0

670560 580 600 620 640 660

IMF 1

IMF 2

IMF n

IMF 1

5

-5

0

560 580 600 620 640 660

Raw Signal

Soft-resampling

Partial Sample

EMD Decomposition

IMFs Selection

Feature Extraction

Time (sec)

720

670


Fig. 6. Flowchart and expected results of each process of the RA-EMD.


where x and y are the coordinates of the cam profile, d is the rotation angle of the cam shaft, r the radius of the base circle,and s the push-rod lift.

By using the cam profile, as stated in Eq. (8), the lift of the valve train can be derived based on the push-rod lift and therocker length. Fig. 8 shows the lift curve of the intake valve of cylinder 1 when there is no abnormal clearance in the valvetrain. Based on the lift curve, the lift data can be calculated when the intake valve is closing (from 5701 to 5851, as given inthe generator operating manual). The amount of valve lift at each crank angle (from 5701 to 5851 and from 5881 to 6031) istabulated in Table 4. Based on these data, the theoretical timing of IVC is 5851 for the case in which the intake valve has anormal clearance of 0.40 mm. Based on the parameters listed in Table 1, the timing of IVC corresponds to 441 after themovement of the cylinder has reached the bottom dead center (BDC). Therefore, the calculated valve lift data coincide withthe parameters of the valve timings for the diesel engine. In addition, the theoretical timing of IVC corresponds to around5971 for the case in which the intake valve has a tight clearance of 0.25 mm.

5.2. Detection of abnormal clearance in the valve train

5.2.1. RA-EMD analysis of vibration

As discussed in Section 5.1, the time in terms of the crank angle provides more suitable features for detecting abnormalclearance in the valve train. Hence, our proposed method, the RA-EMD, was used to extract the exact time events when the

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Spring

Rocker

Lifter

ValveCam

Pushrod

Crank angle

Vib

rati

on

VOn

VOe VCe

VCn

Val

ve li

ft

VOs VCs

Normal clearance

Excessive clearance

Tight clearance

Clearance between valveand rocker

Fig. 7. Schematic drawing of the valve train, theoretical model of the cam, and the variation in the valve lift curves due to the difference in valve train

clearance. (a) The schematic diagram of cam mechanism, (b) the relationship of pushrod lift and the cam profile and (c) the schematic diagram of valve lift

and vibration.

9

7

5

3

1

-10 60 120 180 240 300 360 420 480 540 600 660 720


Val

ue L

ift (

mm

)

IVO1IVC1

Fig. 8. Theoretical model of the intake valve lift curve of cylinder 1.


intake valve was closing. Fig. 9 shows the comparison of the original signal and the extracted signals of the intake valve viaRA-EMD when it was closing. The intake valve belongs to cylinder 1, with the diesel engine operating under work case 3,which is tight clearance in the valve train. Tight clearance causes the time of valve closing to lag behind that of normalclearance. Moreover, the energy of the induced vibration decreases compared with that of normal clearance.

Fig. 9(a) shows the vibration in a working cycle (0–7201) with the angular locations of the exhaust valve closing, intakevalve closing, and fuel injection of cylinder 1 specified after applying soft re-sampling to the original signal. Fig. 9(b) showsthe zoomed signal at a selected range band of 570–6801. This range was selected because it covers the time event of theclosing of the intake valve. From this figure it can be observed that the vibration induced by valve closing is immerged inthe captured signals and can barely be identified from the corresponding vibration peak amplitude. As previously

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Table 4The valve lift at the range of intake valve closing.

Crank Angle (deg) Lift (mm) Crank Angle (deg) Lift (mm)

570 0.86 588 0.35

573 0.74 591 0.31

576 0.63 594 0.28

579 0.54 597 0.25582 0.46 600 0.22

585 0.40 603 0.19

FIEVC IVC

680570100

-100

-50

0

50

7200 120 240 360 480 600

4

-6

-4

-2

0

2

680570 580 600 620 640 660

6023

-3-2

0

2

680570 580 600 620 640 660

6023

-3-2

0

2

680570 580 600 620 640 660

6023

-3-2

0

2

680570 580 600 620 640 660

6023

-3-2

0

2

680570 580 600 620 640 660

Acc

eler

atio

n (g

)A

ccel

erat

ion

(g)

Acc

eler

atio

n (g

)

Crank Angle (degree) Crank Angle (degree)

Crank Angle (degree)Crank Angle (degree)


Fig. 9. Comparison of the original signal and extracted signals using RA-EMD to reveal the time event of the intake valve of cylinder 1 during closing. (a)

Original signal in angle domain, (b) the selected range signal from 5701 to 6801. (c) decomposed IMF 1 signal, (d) Decomposed IMF 2 signal, (e)

decomposed IMF 3 signal, (f) Decomposed IMF 4 signal.


mentioned, tight clearance in the valve train decreases its induced vibration energy, thus making its revelation even moredifficult. To recover the immerged time event, RA-EMD was applied to decompose the signal in crank angle domain. Figs.9(c)–(f) show the decomposed IMFs of the signal in Fig. 9(b) after applying RA-EMD to the selected angular range(570–6801). The signal shown in Fig. 9(b) was decomposed into four IMFs, as shown in Figs. 9(c)–(f). Obviously, IMF 1,which is shown in Fig. 9(c), clearly denotes the time event of IVC. From this IMF 1 temporal signal, the time of this closingcan be clearly identified at around 6021. This angle, which reveals the real time of valve closing, is lagging compared withthe theoretical time of valve closing (5971). Such a slight deviation may be due to the existence of an oil film in theconjunction part and the expansion of the valve train at high temperature. By using our RA-EMD, it becomes possible torecover the real time of the valve activity that occurs when the time event is overwhelmed by other signals as shown in theoriginal waveform of Fig. 9(a).

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5.2.2. Feature extraction for valve closing

Ideally, each decomposed IMF should contain the simple oscillatory mode of an individual impact or the single timeevent of a valve during operation. However, because of the nature of an engine with multiple cylinders, a number of timeevents can affect one another or overlap with the other vibrations in the crank angle and frequency domains. For instance,in Fig. 9(c), the impact caused by the closing of the intake valve of cylinder 1 (IVC1) has been identified at around 6021;however, it can be seen that another impact appears between 5701 and 5801. Based on the time events listed in Fig. 3(e),this impact is probably the result of the closing of the exhaust valve of cylinder 3 (EVC3), because this time event occursadjacent to that of IVC1. The appearance of more than one impact or time event may be due to the filtering features of EMDand the influence of noise. EMD is by nature an adaptive band-pass filter bank, whose bandwidth is self-adaptivelydetermined by the decomposed signal. Flandrin et al. [19] conducted numerical experiments based on fractional Gaussiannoise and discovered that EMD acts essentially as a dyadic filter bank that resembles those involved in waveletdecomposition. Yang [20] also pointed out that the IMFs obtained via EMD are not strictly ‘‘monocomponents’’ due to theinterference of induced noise. Hence, the blending of more than one oscillatory mode or decaying impact in an IMF isinevitable.

To locate a particular time event of a valve accurately, we used RA-EMD so that the clearance can be determined bychecking its angle range after decomposition via EMD. The influence of other impulse signals and noise is expected to beminimized. However, as aforementioned, the decomposed plot in Fig. 9(c) shows not only the temporal event of IVC1, butalso EVC3. Hence, further analysis may be employed to enhance RA-EMD in clearly identifying each temporal valve event.

As the closing of a valve generates an impact, its amplitude decreases gradually due to the existence of structuralimpedance; the energy content per increment of the crank angle may be used to verify the commencement of a time eventof a valve. We further enhanced the RA-EMD for calculating the vibration energy in each increment of crank angle to verifythe location of the valve closing response. It can be calculated with the following equation:

ei ¼

1i

Pi

1

a2i i � L;

1L

Pi

i�L

a2i ; i4L;

8>>>><>>>>:

(9)

where ai denotes the amplitude of each sampling point in the selected IMF and L denotes the number of samples that theselected IMF contains in one crank angle.

By using Eq. (9), the calculated unitary energy of the signal of IMF 1, which has been displayed in Fig. 9(c), is shown inFig. 10. It can be concluded that detecting the time of IVC of IVC1 can be confirmed by using the energy waveform shown inFig. 10 and used to crosscheck the results found in Fig. 9(c).

5.2.3. Diagnosing the existence of abnormal clearance in a valve train

To confirm the ability of the enhanced RA-EMD to diagnose the existence of types of abnormal clearance other thantight clearance in the valve train, the time events of the closing of cylinder 1 intake valve (IVC1) with different types ofclearance are shown in Fig. 11. The ranges of the crank angle that show normal clearance (work case 2 with 0.40 mmclearance), tight clearance (work case 3 with 0.25 mm clearance), and excessive clearance (work case 4 with 0.70 mmclearance) are displayed against 100 combustion cycles in Fig. 11. The fluctuations of the angle ranges when intake valve 1is closing are also displayed. Note that the angle range and mean value of each work case (work cases 2–4, as shown inTable 2 for normal, tight, and excessive clearance, respectively) are different. Their mean values and ranges are tabulated inTable 5. It can be seen that both the mean value and angle range of IVC1 increase when its clearance decreases or becomestighter than that in the normal case. In contrast, when the clearance of IVC1 grows larger, both its mean value and anglerange decrease. Hence, the results match closely with the theoretical model as shown in Fig. 7(c). It is also worth notinghere that the variation in the angle range decreases with an increase in valve train clearance.

IVC11

0

0.25

0.5

0.75

680570 580 600 620 640 660

IMF

1


Fig. 10. Unitary energy waveform of the decomposed signal using IMF 1 clearly reveals the location of IVC1.

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615

610

605

600

595

590

585

580

575

570

5650 10 20 30 40 50 60 70 80 90 100 110

Cycles

Cra

ck A

ngle

(de

gree

)

Clearance 0.25 mm

Clearance 0.40 mm

Clearance 0.70 mm

Fig. 11. Fluctuation of the angles and ranges in 100 combustion cycles when intake valve 1 closes with different clearances.

Table 5Comparison of the mean angle and the angle range of IVC1 in three different kinds of clearance when the engine had run 100 combustion cycles.

Work case 3 (tight) 2 (normal) 4 (excess)

Variation range (deg) 11.0 6.1 2.9

Mean value (deg) 604.5 583.3 570.2


5.3. Robust analysis of RA-EMD

100

80

60

40

20

0590 595 600 605 610 615 620 625


Prob

abili

ty D

ensi

ty (

%)

Conventional

RA-EMD

Fig. 12. Probability density calculated with the conventional method and RA-EMD to identify the time at which IVC1 occurred in 100 combustion cycles.

To compare the starting angle and occurrence range of the time event of IVC1 for three different types of clearance, theaccuracies in revealing these time events using conventional time-based sampling method and our proposed RA-EMD werefurther investigated. The vibration energy created during the closing of intake valve 1 was calculated using Eq. (9) for 100combustion cycles. The conventional method considered the signals in the time domain as units of seconds. To ensure thatthe parameters used for comparison were the same, we converted the signals in the time domain to the crank angledomain. Then, the probability density of the time event for IVC1 when the valve has tight clearance (work case 3) wascalculated and compared with that of the signals extracted using RA-EMD. The distributions of the probability density forboth methods are plotted in Fig. 12. The results of the conventional method show that the times of IVC are distributed inthree zones, that is, the [595–6001], [601–6131], and [616–6201] zones. Our RA-EMD has only one zone, [599–6131]. Zone[616–6201], which is highlighted by a circle in Fig. 12, is created only by the conventional method. This zone contains morethan 10% of the 100 combustion cycles tested. However, the angular range of this zone is not specified in the generatormanual. Hence it can be concluded that the conventional method fails to provide an accurate confirmation of the timeevent of IVC1, and the results may therefore be unstable. In contrast, the time events of IVC1 for 100 combustion cyclesproduced by our RA-EMD have a normal distribution centered around 604.51 in the case of tight clearance. Therefore, ourmethod is more accurate and stable in the identification of the time or crank angle of IVC1. Such superior performance isimportant, as we have to depend on it to inspect any clearance problems in the valve train of an engine.

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6. Detection of worn piston rings via RA-EMD

To further demonstrate the ability of RA-EMD in diesel engine fault diagnosis, real vibrations and angular data werecollected from the same generator but this time from a worn piston ring in its cylinder 4. Piston slap is a common impactphenomenon in diesel engines and is one of the main sources of transverse vibration in such engines. Geng and Chen [3]analyzed the characteristics of piston slap-induced vibration for monitoring a diesel engine piston. The vibration responseof piston slap has a strong relationship with the clearance between the piston ring and the piston liner as well as with themagnitude of the side-thrust force that acts against the piston and cylinder. The different sizes of clearance between thepiston ring and piston liner create different time events and energy content in the induced vibration. However, theamplitude of transverse vibration is rather small and is usually immerged in the larger vibrations induced by othercomponents. For example, the combustion process of a cylinder can create large vibrations, which may overwhelm thevibration induced by piston slap. To detect the occurrence of a worn piston ring or piston liner, the RA-EMD was againemployed to extract the vibrations induced by piston slap.

6.1. Dynamic analysis of the crank mechanism

The direct consequence of a worn piston ring is an increase in the frequency and time of occurrence, as well as themagnitude, of piston slap. Prior to conducting any experiment on piston slap, one must understand the characteristics ofvibrations induced by it and the model of a worn piston and liner. Fig. 13 shows a simple schematic diagram of the forcesgenerated by a single-crank mechanism. In this figure, the piston moves along the cylinder as a result of in-cylinderpressure, p; meanwhile, the piston assembly moves from one side of centerline, y, to the other side. Therefore, as shown inFig. 13, the force exerted on the piston assembly can be decomposed into forces in x and y directions. The force in the x

direction, fx, is the side-thrust force, fs, and can be expressed as

f x ¼ f s ¼ ðpþ f iÞtanb, (10)

where, as refered to in Fig. 13, sinb ¼ l sin y , l ¼ r/l, r is the crank radius and l the length of the connecting rod; fi is theinertial force of the moving piston and can be expressed as

f i ¼ �m1rco2ðcosyþ l cos 2yÞ: (11)

Substitute Eq. (11) into Eq. (10), the force in the x direction, fx, is

f x ¼ ½p�m1rco2ðcosyþ l cos 2yÞ�l siny=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1þ ðl sinyÞ2

q. (12)

Because the perpendicular angle of the connecting rod, b, changes periodically, the side-thrust force, fs, also periodicallychanges. Based on Eq. (12), the side-thrust force, fs, can be calculated after introducing the in-cylinder pressure, p.

Fig. 14 shows the measured in-cylinder pressure, p, when the diesel engine is working at a load of 16 kW and running at1400 rev/min, and the calculated temporal waveforms of the side-thrust force, fs, of cylinder 4. From Fig. 14, one can seethat there are three distinct peaks of the side-thrust force in one working cycle. These are marked as zones I–III. One canalso observe that the side-thrust force at zone II is mainly affected by the in-cylinder pressure, as compared with the valuesof side-thrust force and pressure in zones I and III. This is because the pressure peaks at zone II for cylinder 4 (cylinder 4

�

�

�

fs

fi

l

r

Cylinder wall

Piston Piston assembly

x

y

p

Piston ring

Fig. 13. Schematic diagram of the decomposed forces of a single-crank mechanism.

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IIIIII

100

0

20

40

60

80

1500

-1500

-1000

-500

0

500

1000

7200 60 120 180 240 300 360 420 480 540 600 660

Pressure

Side-thrust force

Side

-thr

ust f

orce

(N

)

Pres

sure

(ba

r)

Crank angle (degree)

Fig. 14. Waveforms of the in-cylinder pressure and calculated side-thrust force (fs) of cylinder 4 when the generator is working at 16 kW and running at

1400 rev/min.


was being blown down during this zone). The side-thrust force at zones I and III is dominated by the inertia force. Referringto Fig. 13, in theory, the vibration collected from the cylinder block is a response to the in-cylinder pressure, p, in the x

direction and the side-thrust force, fs. To extract the vibration feature induced by the side-thrust force accurately, thecontribution of in-cylinder pressure to the collected vibrations should not be included. Hence, it is more appropriate toselect zone I or III to inspect the vibration effect caused by the side-thrust force. In light of this, the vibration at zone I from701 to 1601 was extracted to analyze the vibration characteristics related to a worn piston ring fault.

6.2. Use of RA-EMD to analyze the vibration induced by piston slap

Our RA-EMD was again used to detect the vibrations induced by piston slap. Fig. 15(a) shows the original vibrationsignal in one working cycle, with cylinder 4 suffering from a worn piston ring. As mentioned in Section 6.1, the vibrationsignal that corresponds to zone I was selected to minimize the effect of in-cylinder pressure. Fig. 15(b) shows the waveformof this signal in the selected range of angles (RA). Even with the help of RA, the impacts caused by piston slap are not easyto identify. Hence, the RA signal was extracted and decomposed via RA-EMD to diagnose the wear to the piston ring. Fig.15(c) shows the results after application of RA-EMD. The impacts of piston slap can be clearly revealed by the resultantwaveform and marked as zone IV in Fig. 15(c).

6.3. Fault diagnosis of worn piston ring

Based on the results presented in Section 6.2 and Fig. 15, the RA-EMD has been proved effective in extracting thevibration features related to the occurrence of piston slap. Because a worn piston ring increases the clearance between thepiston ring and the piston liner, again the use of ranged angle does help to distinguish a normal piston ring from a wornpiston ring. The time of piston slap lags when the clearance between the worn piston ring and the cylinder liner increases.The timing of piston slap was calculated by applying the parameter of unitary energy, as described in Eq. (9), to theextracted signal. Fig. 16 shows the detected ranges of the crank angle of piston slap over 100 working cycles for a normaland worn piston ring when the generator was working at a load of 16 kW and running at 1400 rev/min. The results showthat the time of the piston slap created by the worn piston ring obviously lags behind that created by a normal piston ring.This difference in lag can be as high as 301. Hence, the results prove that our RA-EMD is capable of clearly distinguishingthe operating status of a normal and a worn piston ring.

To investigate the influence of working load on the diagnostic results, the occurrences of the times or crank angles ofpiston slap were measured with two different operating loads at 16 and 32 kW. As shown in Fig. 17, the results show thatthe crank angles when piston slap occurs are roughly located in the same angle range even when the operating load hasbeen doubled. Such a phenomenon remains the same for 100 working cycles. Hence, the results prove that the delay in thecrank angle caused by a worn piston ring is not affected by a variation in the working load. The average crank angle valuesin the cases of normal and worn piston rings at 16 and 32 kW are tabulated in Table 6. Again, the mean angles prove thatthe increase in operating load does not affect the timing or range of angle over which piston slap occurred. However, thedifference in angles of a healthy piston and a worn piston is significantly distinguishable. Hence, our RA-EMD has beenproven to be a good indicator of piston wear, including the ring and liner, as it is sensitive to changes in clearance.

7. Conclusions

Conventional machine fault diagnostic methods use the increase in vibration energy at the broad frequency spectrum orat a selective high-frequency spectrum as a vital indicator in detecting the occurrence of the impacts caused by faultycomponents. These methods usually require a baseline of vibration that is obtained from a normally running machine so

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110

100

90

80

70

600 20 40 60 80 100

Cycles

Cra

nk a

ngle

(de

gree

)

Normal

Worn piston ring

Fig. 16. Comparison of the in-crank angle when piston slap occurs in a normal and worn piston ring, with the generator operating at a load of 16 kW and

running at 1400 rev/min.

III

80

-20

0

20

40

60

80

-40

-20

0

20

40

60

7200 60 120 180 240 300 360 420 480 540 600 660

Pressure

Acceleration

3

-3-2

0

2

16070 80 90 100 110 120 130 140 150

3

-3-2

0

2

16070 80 90 100 110 120 130 140 150

IV

Acc

eler

atio

n

Pres

sure

(ba

r)

Acc

eler

atio

n (g

)

Acc

eler

atio

n (g

)


Crank angle (degree)

Fig. 15. Raw vibration signal and ranged signal in zone I after application of the RA method and the results after application of the EMD method. (a) The

original vibration signal collected from cylinder 4’s wall in x direction in one combustion cycle, (b) the ranged signal extracted from zone I and (c) the

results after applying RA-EMD.

110

100

90

80

70

600 20 40 60 80 100

Cycles

Worn piston ring 16 kW

Worn piston ring 32 kW

Cra

nk a

ngle

(de

gree

)

Fig. 17. Comparison of the in-crank angle when piston slap occurs under different working loads.


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Table 6Average angles at which piston slap occurred over 100 combustion cycles at different health and loading conditions.

Working condition Normal 16 kW,

1400 rev/min

Worn piston ring 16 kW,

1400 rev/min

Worn piston ring 32 kW,

1400 rev/min

Average value (deg) 75.5 99.6 100.5


that the features extracted from the baseline can be used to compare those obtained from an anomalous machine.However, such baseline may not be easily obtained from engines used for power generation due to a lack of propermaintenance staff, poor record keeping, and the use of second-hand generator, where it is impossible to recover itsbaseline. This is particularly true for manufacturing plants, which often use second-hand generators as they are cheaperand easier to purchase than new ones, which have a long waiting list of buyers. To overcome this deficiency, we haveproposed a new diagnostic method for diesel engine-type generators that requires no baseline but still achieves the sameor even better accuracy in fault detection than those that have the baseline.

More specifically, this paper presents a novel vibration-based fault diagnostic method to check for abnormal clearancebetween the contacting components of any diesel engine. The proposed method employs the timing of impacts caused bytwo contacting components as the prime diagnostic feature. To extract the features that distinguish the components withabnormal clearance from those with normal clearance, the characteristics of the vibrations generated by a diesel enginetypically used in manufacturing were analyzed in this research. Soft-re-sampling and EMD techniques were employed andintegrated with a newly proposed RA technique to form a novel RA-EMD, which is specially designed to diagnose faults thatmay occur in diesel engines. The RA technique is to extract the instantaneous variations of crankshaft angular speed causedby improper time events induced by various engine faults. The EMD is to reveal the signals that have been overwhelmed byother signals and noise. An energy analysis has been coupled with RA-EMD to crosscheck and confirm the results obtainedfrom RA-EMD so that its accuracy in engine fault diagnosis can be further enhanced.

To verify the effectiveness of our enhanced RA-EMD, valve trains suffering from tight and excessive clearance weretested. The vibration signals and the time of occurrence in terms of the crank angle of valve operation were captured andanalyzed using RA-EMD. Although there is no baseline, RA-EMD can distinguish normal and faulty components. Even whenthe faulty impacts have been overwhelmed by other larger vibrations, RA-EMD still exhibits a strong ability to detect thesefaulty impacts from the collected vibrations. Furthermore, RA-EMD is able to distinguish whether the valve train clearanceis too loose or too tight by detecting the time events of each valve train. To further verify the abilities of RA-EMD, pistonslap, which is a side effect caused by worn piston ring, was investigated theoretically and experimentally. The resultsshowed that it is very sensitive to the clearance created by a worn piston ring and the wall of the liner. This ability can befurther developed in future to determine the severity of wear that often occurs in piston-related components. The resultsobtained by RA-EMD also show that the accuracy in detecting piston slap is independent of the variations in operating load.

Through theoretical analysis, this paper provides fundamental models to calculate the valve lift curves, side-thrust force,and expected unitary vibration energy caused by piston slap. It also declares the ability of RA-EMD in detecting any dieselengine faults that are related to a change in time or in the sequence of crank angle. This method has been proven superiorto conventional methods that must have a baseline for comparison purpose. For hardware requirements, RA-EMD employsonly accelerometers for collecting vibrations and a low-cost encoder to obtain the necessary information on crank angularspeed and TDC positions. Further research will be carried out to enhance the application of RA-EMD for other types of faultsthat are commonly found in internal combustion engines. Using RA-EMD, expensive fuel leakage through worn piston ringand liner can be minimized and poor combustion efficiency due to improper open and closed valves can be avoided. Mostimportantly, the life and reliability of engine can be enhanced by identifying destructive vibration sources, such as pistonslap, and then performing remedies to return the engine back to normal combustion process.

Acknowledgements

The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong KongSpecial Administrative Region, China (Project no. CityU 120506) and partially funded by an Applied Research Grant fromthe City University of Hong Kong (Project no. 9667020).

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Mechanical Systems and Signal Processing Content/Papers/EMD...valve events, worn piston rings and liners, etc., become detectable even without the comparison to the baseline. Hence,