Fault indentification for I.C. engines using artifical nerual network

Manthan Shah, Vijay Gaikwad, Vishwakarma Institute of Technology

Pune, India manthan.shah03@gmail.com, vdg_3@rediffmail.com

Shashikant Lokhande, Sanket Borhade Sinhgad College of Engineering,

Vishwakarma Institute Of Technology Pune, India

l_shashikant@yahoo.co.in ,sanket.borhade@gmail.com

Abstract— Due to progress in the vehicular technology, vehicles have gradually become a popular form of transportation in people’s daily life. The stability and the performance of the vehicles has been the subject of much attraction. Road vehicle engines are controlled by engine management system (EMS) in which fault identification & diagnosis is the vital part. The pressure of the engine intake system always demonstrates the engine condition and affects the volumetric efficiency, fuel consumption and performance of internal combustion engines. Conventional engine diagnostic technology already exists through analyzing the differences between the signals and depends on the experience of the technician. Obviously the conventional detection is not a precise approach for pressure detection when the engine in operating condition. In this paper, a system is consisted of pressure signal feature extraction using discrete wavelet transform (DWT) and fault recognition using the neural network technique. To verify the effect of the proposed system for identification, the radial basis function network (RBFN) is used. It has been observed that the training procedure can be accomplished in short time. Also, the conventional flaw of too much reliance on the experience of technicians can be reduced. Keywords- IC engine, Discrete wavelet transform (DWT), radial basis Function neural network (RBFN) and feature extraction.

I. INTRODUCTION Owing to progress in the vehicular technology, vehicles have gradually become a popular form of transportation in people’s daily life. The stability and the performance of the vehicles has been the subject of much attraction. Road vehicle engines are controlled by engine management system in which fault identification & diagnosis is the vital part. Many papers developed the fault diagnosis and identification (FDI) during recent years. Neural networks have been used in FDI problems for model approximation and pattern recognition as well. For signal feature extraction in recent years, many useful techniques for signal analysis have been proposed, such as fast Fourier transform (FFT) (Corinthos, 1971), short-time Fourier transform (STFT) (Portnoff, 1980) and wavelet transform (WT). Nonetheless, the different analysis techniques did not adapt the signal of intake manifold pressure.

The STFT provides a fixed-size window in the time-frequency domain. The wavelet transform technique was used for both time and frequency resolutions and it could have an auto adjusted scale to adapt to the signal. The wavelet transform could be divided into continuous wavelet transform

(CWT) and discrete wavelet transform (DWT). In 2008, Senet. Al published a wavelet analysis of cycle-to-cycle pressure variations in an internal combustion engines (Sen, Litak, Taccani, & Radu, 2008). The study is a useful example analyzing the pressure in internal combustion engines with WT. Unfortunately, the CWT took a lot of time for the calculation part and the amount of data was huge. (Wu & Chen, 2006).

Therefore, the DWT was developed to improve on CWT. The original signal was decomposed into several resolutions by the DWT, so it was easy to examine the instantaneous variations of pressure in all frequency band. The DWT technique is used in feature extraction for the internal combustion engine fault diagnosis system. Radial basis function network (RBFN) is used for fault identification. Wavelet transform is relatively novel technique of signal processing. Similar to windowed Fourier transform, a wavelet transform can measure the time-frequency variations in spectral components. It provides a more flexible time-frequency resolution and multi-resolution representation. So, by implementing fault identification & diagnosis using neural network fast learning rate & good recognition rate can be achieved.

II. PRINCIPLES OF SIGANL PROCESSING AND MULTI-RESOLUTION ANALYSIS

A. Principle of wavelet transform Wavelet transform is a relatively novel technique of signal processing. Similar to a windowed Fourier transform can measure the time-frequency variations in spectral components. It provides a more flexible time-frequency resolution and multi-resolution representation. The wavelet transform is defined as the inner product of a signal ( )x t with the mother wavelet ( )tψ .

*1( , ) ( ) ,t bCWT a b x t dtaa

ψ∞

−∞

−⎛ ⎞= ⎜ ⎟⎝ ⎠∫ (1)

978-1-61284-764-1/11/$26.00 ©2011 IEEE

Where a represents the scale parameter, ‘b’ rshifting (or translation) parameters and *'ψ' 'ψ .

Figure 1. Decmoposition and Reconstructio

The user can dilate or translate the mother and ‘b’. during CWT analysis, the wavelet ifull domain of the analyzed signal. It thwavelet coefficient at every scale, generatinof data. Due to the huge amount of data gCWT, training classifiers based on its coeffiscales can often become cumbersome. Tdefined as

*1 2( , ) ( )22

j

jj

t kDWT a b x t dψ∞

−∞

⎛ ⎞−= ⎜ ⎟⎝ ⎠

∫Where ' 'a and ‘ ' 'b are replaced by '2 'j

wavelet technique has particular advantages signals at different location levels in time as domains. The discrete wavelet transformfunctions (Graps, 1995) that can be shownlow-pass filters. The two-filters are called th' ( ) 'tϕ and scaling function ' ( ) 'tφ as follow

/ 2,

2( ) 2 , ,2

jj

j k j

t kt J K Iϕ ϕ− ⎛ ⎞−= ∈⎜ ⎟⎝ ⎠

/ 2,

2( ) 2 , ,2

jj

j k j

t kt j k Iφ φ− ⎛ ⎞−= ∈⎜ ⎟⎝ ⎠

Here, the time shift k=1,2,...,N/ 2 j and the is the maximum level of wavelet transform. transform of ' ( ) 'x t can be obtained by Equa

, ( ) ( ) ,j k jkD x t t dtϕ= ∫

, ( ) ( ) ,j k jkA x t t dtφ= ∫

Where ,' 'j kD and ,' 'j kA are called theapproximation/smooth coefficients respecspeaking ,' 'j kA mainly represents the ta

represents the time ' is the complex of

on of DWT

wavelet using ‘a’ is shifted over the

hus calculates the ng a huge amount generated through

ficients at different he DWT can be

,dt⎞⎟⎠

(2)

' and '2 'j k . The for characterizing well as frequency

m performs two n as high-pass and e wavelet function

ws:

(3)

(4)

level j=1,2,…,J. J Thus, the wavelet

ations (5) and (6),

(5)

(6)

e detailed and ctively. Roughly

ardy variation of

' ( ) 'x t at the low-frequency bdetailed part at the high-frequeis possible to resolve high frequ

DWT uses the fact frequency components within low frequency components nbecause a low frequency compand a high frequency componeshorter interval. Therefore, sloidentified over short intervals.

Figure 2. Architecture of r

Figure 3. Flow chart of Radial Baproc

band and ,' 'j kD represents the ency band. DWT uses the fact it uency band.

it is possible to resolve high a small time window, and only eed large time windows. This pletes a cycle in a large interval ent completes a cycle in a much ow varying components can be

radial basis function network

asis function neural network training cedure

Figure 4. Experimental setup of engine fault diagnosis

B. Multi-resolution analysis Muti-Resolution analysis was first developed by Mallat.

The mathematical model of MRA (Mallet, 1989; Yang & Leu, 2008) can be defined as follows:

1 1 1.... ,j j J j j kV W W W V+ − − −= ⊕ ⊕ ⊕ ⊕ (7)

Where ' 'jV is the approximate version which decomposed the signal with original signal at scale ‘j’;

(a)

Figure 5. Approximate and detailed coefficients which are decomposed to five levels using DWT with various faults in ideal condition (a) normal condition (b) one injector fault (c) two injector fault (d) intake air leak (e) intake plugged

' 'jW is the detailed version showing the transient appearance

of the signal at scale ‘j’; ' '⊕ is a summation of two decomposed level. The multi-resolution analysis can be applied to decompose the signal ' ( ) 'x t into various scales of the

orthogonal signal section. They are the detailed sub-signal ' ( ) 'jD t and the approximation sub-signal. ' ( ) 'jA t , which

represents the components of ' ( ) 'x t at different resolutions, calculated as follows

0 500 1000-1

0

1Normal Condition - D1

0 500 1000-1

0

1D2

Mag

nitd

e

0 500 1000-1

0

1D3

Sample Number

0 500 1000-1

0

1D4

0 500 1000-1

0

1D5

0 500 10000

1

2A5

, ,( ) ( ) , ,j j k j kk

D t d t j k Iϕ= ∈∑ (8)

, ,( ) ( ) , ,j j k j kk

A t A t J k Iφ= ∈∑ (9)

The ' ( ) 'jD t contents an approximate frequency band of

( ) j jj J

x t A D≤

= +∑ (10)

Where ' ( ) 'jA t and ' ( ) 'jD t express the approximation and

the detailed signal of the ' 'thJ level.

1/ 2 / 2j js sf f+⎡ ⎤−⎣ ⎦ and ' ( ) 'jA t contents frequency band of

10 / 2 jsf

+⎡ ⎤−⎣ ⎦ , here ' 'sf is the sampling frequency. The

signal ' ( ) 'x t can be recovered in terms of terms of these sub-signals with different scales:

III. PRINCIPLES OF ARTIFICAL NEURAL METWORK MODELS

A. Radial basis function network The RBNN (Pulido, Ruissanchez, & Rius 1999; Stubbings & Hunter, 1999) is a feed- forward network, which is comprised of three different layers; one input layer, one

hidden layer and one output layer. The network architecture is shown in Figure 2. The third layer is the output layer, and the transfer functions of the neurons are the linear units. The transformations from the inputs space to the hidden space are non-linear. Connections between the input and the hidden layers have unit weights. The hidden layer of the RFNN has several forms of nonzero activation functions. The node calculates the Euclidean distance between the center and the network input vector and then passes the result to the radial basis function. The basis function for the ' 'thj hidden node is often defined by a Gaussian exponential function shown as follows:

2

2( ) exp2

jj j

j

va a v

σ⎛ ⎞

= = −⎜ ⎟⎜ ⎟⎝ ⎠

(11)

Where ' 'jσ is the width of the ' 'thj neuron, ' 'jv is presented by the Euclidean norm of the distance between the input vector and the neuron center calculated as follows:

2

1( ) ( ) , 1, 2,..., ,

r

j j i jii

v x x c x c i r=

= − = − =∑ (12)

Where ' 'jc is a center of the ' 'thj RBFN unit and the output Value is defined as

1

s

k jk jj

y d a=

=∑ (13)

Where ' 'ky , the ' 'thk element of the ‘y’, is the output of the

' 'thk node in the output layer, ' 'jkd is the weight from the

' 'thj hidden layer neuron to the ' 'thk output layer neuron, and

' 'ja is the output of the ' 'thj node in the hidden layer. The output layer simply consists of linear summation units with a linear activation function. In the training process, the output target of the hidden node associated with the cluster “1” for most samples. For the other samples, this target is “0”. The flow chart of the RBNN training procedure is shown in Figure ‘3’. The RBFN could automatically determine the number of neurons in the hidden layer during the procedure. If the result does not contain the convergence conditions, the network system will add a neuron to the hidden layer and the feed-forward stage is repeated until convergences are contained

IV. EXPERIMENTAL STUDY AND ANALYSIS

A. Experimental work In the experiment investigation, the intake manifold pressure signals of engine were recorded and analyzed to

prove fault identification system. The pressure signals were transmitted using pressure sensors and recorded using a data acquisition system. Afterward, we analyzed and classified the signals using DWT and an artificial neural network. In the signal analysis can be decomposed into 5 levels by DWT and can be used to detect faults. The purpose of neural network is to find the same engine fault condition under different operation conditions. The experimental flowchart of the engine fault diagnosis system is Figure. 4. The experiment for the experiment included an internal combustion engine (Ford L type, four-stroke, four cylinders, 1.6-L injection engine), pressure sensor and a data acquisition system (NI-9215) with 10KHz sampling frequency. The experiment comprises five engine fault conditions are five engine rotating speeds. The five fault conditions are the normal engine condition, one injector fault, two injectors fault, intake air-leak and intake air-plugged. The engine is run in an idle condition, 1000 rpm, 1600 rpm, 2500 rpm and 3000 rpm.

B. Results and discussions In the domain signal, it is very difficult to detect the transient signal in the engine fault condition. In the proposed system, after recording pressure signal, they could be decomposed into five levels by the DWT analysis technique. The five levels were composed of five high frequency band and one low frequency band. Unfortunately, there is still no easy way to point out faults from the person’s viewpoint and it is not easy way to detect faults from the information. Therefore, the feature vectors of engine faults are summarized as inputs of the neural network which is the mean square and established as database. However the selection of the feature vector was adapted to the engine speeds. There are 160 data sets for each fault condition. The 120 data sets are used for training 40 data sets are used to test the recognition rate of the proposed network. The performance of the fault diagnosis system is evaluated by the recognition rate which is defined as

TABLE I. CALCULATION OF MEAN SQUARE ( 310− ) AT IDEAL CONDITION

Parameter D1 D2 D3 D4 D5 A5

Normal condition 49.737 53.007 72.032 72.589 72.589 313.5

1-injecter fault 111.90 119.26 162.07 163.32 163.32 705.5

2-injecter fault 71.621 76.331 103.72 104.52 104.52 451.5

Intake air leak 198.94 212.02 288.12 290.35 290.35 254.3

Intake air plugged 310.85 331.29 450.20 453.68 453.68 959.8

TABLE II. CALCULATION OF MEAN SQUARE ( 310− ) AT 2500 RPM

Parameter D1

D2 D3 D4 D5 A5

Normal condition 53.0 51.8 72.5 73.0 73.0 327

1-injecter fault 119 116 163 164. 164 737

2-injecter fault 89.6 87.5 122 124 123 554

Intake air leak 171 167 235 236 236 62.2

Intake air plugged 256 250 351 353 353 586

Recognition rate = Correct classification samples×100% (14) Total testing of samples As summarized in Table 3, all the evaluation results had over 96% recognition rates in various engine operation conditions. The experimental results show the DWT fault diagnosis system with RBFN can be effectively used in engine fault diagnosis of various faults through measuring the engine intake manifold pressure signal.

TABLE III. PERFORMANCE OF FAULT RECONIZATION USING RBFN IN VARIOUS FAULTS AND OPERATION CONDITIONS.

Parameters

ideal

1000

1600

2500

3000

Normal (%) 100 100 100 100 100 One injector fault (%)

100 100 100 100 100

Two injector fault (%)

100 100 100 100 100

Intake air- leak (%)

100 100 100 100 100

Intake plugged (%)

100 82 100 100 100

Average recognition Rate (%)

99

Training time (%)

7.4

V. CONCLUSION A fault diagnosis system of the internal combustion engine based on the intake manifold pressure is proposed. The diagnosis procedure consists of feature extraction using discrete wavelet transform and classification using artificial neural networks. The approach improves the conventional flaw of much reliance on the experience of technicians. In this system, the features of intake manifold pressure signal at different resolution analysis without losing their original properties. The experimental results indicated the proposed fault diagnosis system with RBFN neural network can be effectively used in fault diagnosis. So, training procedure can be accomplished in a short time and a satisfactory recognition rate can be achieved in the experimental platform.

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