Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 26-29 August 2004

DEVELOPMENT OF ON-LINE VIBRATION CONDITION MONITORING SYSTEM OF HYDRO GENERATORS

YANG WANG, FU-XIONG SUN, TUN-SHU HUANG

Institute of Electronic Information, Wuhan University, Wuhan 430079, China E-MAIL: shxu@wuhee.edu.cn

Abstract: Mechanical vibration information Is critical to diagnosing

the health of a generator. Existing vibration monitoring systems have poor mistihility against the strong electromagnetic interference in field and difficult to extend. In order to solve these problems, an on-line monitoring system based on LonWorks control network has been developed. In this paper, the structure of hardware and sohare, the functions and characteristics of system have been described in detail. The analysis result of a vibration signal has proved that this monitoring system can detect fault of generators efficiently. The system has been employed to monitor the vibration of hydro generators operation in a water power plant in Hubei province, China.

Keywords:

vibration; wavelet

1. Introduction

online monitoring; hydro generator; LonWorks,

The hydro generator is one of the most important equipments in power systems. Both mechanical and electromagnetic forces will induce the vibration of generator, which will affect the efficiency and safety of generator and lead to enormous economic losses. However, the reduction of operational and maintenance costs is continuous requirement of modem hydro generators, thus it is requires to increase reliability and production time, simultaneously with an increase of service interval. An important method enabling this is to realize the change from "schedule inspection" to "condition inspection" of hydro generators, which will provide hydro generators with advanced condition monitoring systems and monitoring the generator during operation. This will help to improve the reliability of operation, and to detect incipient faults in the generator and actuator faults.

The technology of on-line vibration monitoring is developing rapidly, but existing systems have the following disadvantages: poor resistihility against the strong electromagnetic interference in field, difficult for extension.

In this paper, an online vibration monitoring system based on LonWorks control network has been developed and presented. Because the system measures signals by field-nodes near the generators and transmits result through LonWorks field bus, which avoids transmitting analog signal to measurement devices by long cable, it has strong resistibility against interference and is easy to extend by simply adding more nodes into the system. The system has been employed in a power plant in Hubei province, China and contributes to increasing the time in which the generator will produce energy, by reducing the time spent in operating in undesirable load or fault conditions.

2. Hardware of system

The on-line monitoring system is composed of uansducers, field-nodes, an industry computer, LonWorks bus cable, and a LonWorks-RS232 adapter, as. can be seen in figure I .

lndurtry Complter

SlgndP

Figure 1. The suucture of the system A field-node is near to a particular part of a generator.

All the field-nodes compose a LonWorks control network together with the adapter. The nodes send the measurement result to the adapter near to industry computer at the monitoring center through LonWorks bus; this can resist the electromagnetic interference in field and improve the

0-7803-8403-2/04/$20.00 02004 IEEE 1025

Proceedings of the “bird International Conference on Machine Learning and Cyhemetics, Shanghai, 26-29 August 2004

efficiency of data transmitting, because it samples signal at field and sends result to the control center by digital signal. The adapter receives the data from field-nodes and then sends them to the industry computer.

Each node is composed of one module of signal conditioning, one data acquisition module and one communication module. The vibration or pressure pulsation signal from generator is picked up by transducers, and then converted to 4-20 mA current sienal or 0-10 V voltaEe

I Icommmcabml I Module I ‘ Module I I _ _ _ _ _ _ _ - _ _ - _ _ _ 2 I _ - - _ _ _ _ -

I ~ -

signal according to different transducer. After the signal has been preprocessed firstly by the module of conditioning, it is acquired by DAQ module.

Figure 2. DAQ module and Communication Module

2.4. LonWorks-E232 adapter 2.1. Transducer

Because the low frequency band of vibration signal may include fault information of generator, we use DP type low frequency vibration transducer for converting mechanical vibration signals into electrical signals. Its frequency bandwidth is down to 0.5 Hz. Pressure pulsation signals are converted to electrical signals by 841GCM type pressure transducer.

2.2. Signal conditioning

The main purpose of signal conditioning is to provide the interface between transducers and DAQ module. It firstly gates the signals from transducers by multiplexer and filters them. Then it amplifies or attenuates the signals to meet the input range of DAQ module. It also provides the functions of sensor conditioning, offsetting and noise reduction. At last the signals are acquired by DAQ module.

2.3. DAQ module and Communication Module

The main part for the data acquisition module is composed by an 8-channel analog multiplexer followed by: an AD for successive approximation of 12 bits and 100 kHz with sample-hold in the same chip and an uc812 MCU, as can be seen in figure 2.

The MCU selects channels and control the data sampling. The sampling time may be adjusted by command from industry computer or an extemal signal. After the data sampling, the MPU sends the result to communication module through parallel bus by “virtual ring protocol” [l].

The communication module is composed of Neuron chip and FIT-1OA receiver. It packets the bytes from DAQ module into Lontalk protocol [l] and then transmits those packets to the adapter through LonWorks network. The packet APIs and transmit APIs are provided by firmware in neuron chip.

Data transmitted in LonWorks network are packeted in Lontalk protocol. In order to receive and store the data in industry computer at control center, we provide a LonWorks-RS232 adapter, which is composed of Neuron chip, FIT-10A receiver, MCU 89‘31 and MAX232 receiver. The block diagram of it can be seen in figure 3.

, _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ I

FIT-lOA ILmWorks Receiver Neb&

I

Neuron 3150

Parallel bus Neuron I

I I LonWorkcRS232 I I LonWorkcRS232 I Adapter I

Figure 3. The block diagram of adapter The adapter is responsible to send the data form

field-nodes to the industry computer and send the command from the industry computer to the field-nodes. It converts the format of data transmitted between LmWorks network and RS232.

After received the data packets from LonWorks network, the Neuron chip decodes the packets and extracts the data out of them. Then it sends the data to the MCU through parallel port. Finally, the MCU transmits the data to the serial port of the industry computer by the MAX232 receiver. , ..

3. Softwareofsystem .

. . . . . . . . . . . . . . . . . . . . .

The main function of system software is to control field-nodes and analyze the vibration data. Figure 4 shows the flow chart of the system software.

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Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 26-29 August 2004

Frame type Node-ID Channel-ID (2B) (W (IB)

Data acquisition Q

Sample data CRC code (128B) (2B)

Data acquisition 11

e3 End Figure 4. The software flow chart

The configuration of hardware, remote communication, data storage, and analysis of data are implemented in the software. According to the running procedure of software, the software can be divided into five parts: initialization of system, data acquisition, data process, data storage and result display.

When the system software starts to run, it sends commands to initialize parameters of all the field nodes. Then it controls the nodes to sample vibration signals periodically and the nodes transmit the data to the industry computer. After the vibration data are received at industry computer, they are stored into SQL-Server database for the need of inquiry in the future. Then the data are processed in various ways’such as digital filter, FFI analysis, Wavelet analysis and so on. FFT analysis can extract the spectrum information of the signals. Wavelet analysis can locate the fault points accurately in time-scale panel. The result of analysis is immediately stored into database. The real-time waveform of the vibration signal and the result of analysis can be displayed by selecting system menu. The warming parameters can he preset in software; warning messages will be triggered whenever the any signal exceeds the pre-determined limits. The status of the generators is reported every day.

3.1. Software of data sampling and transmitting

After receiving the command of data sampling, the filed nodes to sample the vibration signals and transmit the sampled data to monitoring center. The flow chart of field node software is shown in figure 5.

4 I

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Proceedqs of the Third International Conference on Machine Learning and Cybemetics, Shanghai, 26-29 August 2004

3.2 Software of fault diagnosis

The software of industry computer diagnoses fault of generaton by processing vibration signal with continuous wavelet transformation (CWT). The important fault information is usually involved in instantaneous signal component changing sharply, especially in discrete break points which are called isolated singularity points. The feature information of these points includes the position and the singularity degree of instantaneous components. With property of continuous scales and constant time-shift, CWT is fitter for the feature extraction of all kinds of transient or unsteady signals with desired results than classic Fourier transformation [3].

3.2.1. The WTMM lines

WT decomposes time-domain raw signals into Wavelet inner-product space. The definition is as the following:

I - x--7 -~ W,(s,x)=f.v,,. >=Is1 I f (Ms)dx (1)

Wavelet local maximum module is defined as: suppose x belongs to a neighborhood union of xo. if I Wflsax) I < I Wflsaxo) I ,then IWflsaxo) I is defined as the local maximum point of I ~ f l s a x ) I . the scale-space plane, suppose there exists a line r,let's define line r as WTMM, if the points of the line r are all the maximum points of I wflsSax) I in the scale-space plane.

WT coefficients describe the similarity between signal and the wavelet base, the larger IWfls.x)l is, the more similar signal local component is to wavelet base. With proper wavelet base, when the fault information of the signal (the isolated singularity points) is similar to wavelet base, the feature information concentrates on the larger WT modules as high-energy block; On the contrary, the wavelet coefiicients are very small, signal energy is absorbed by WT and so as to realize feature extraction and fault detection.

3.2.2. The position of isolated singularity points

According to the mathematical meaning of varnishing moment, if wavelet with p-order (p>l) varnishing moment, the definition of CWT is:

This expression is validity for Lipscbitz exponent

Theorem [4]: Let f (x) EL2 (RJ. An function flxJ is greater than expression 1.

defined in ( a , 8 ) , XO E( 0, B).If there exists scale S& and constant C, for any x E( U , 8) and .%So, all the maxima of Wf(s,x) belong to a cone defined by(As can be seen in figure 7):

So that 1.For any x E (a,@. x f %the f l x ) is uniformly

Lipschitz n in a neighborhood union of XO.

2. The functinnflx) is Lipschitz a at xo. 0 < n 5 n , if and only if there exists constant A, such that in the cone defined by (4) expression, for each maxima module line:

Ix -xo15 cs (3)

IWf(s,x)I S AS' (4)

log,pf(s,x)( S log, A + alog, s

Equation (4) is equivalent to: (5)

In scale-space plane, signal's WTMM limes whose behavior are inside a cone will point to signal's singularity points along with the decrease of scale (As can be seen in figure 8).

3.23. The singularity degree estimation of isolated singularity points

In the equation 5, let us denote 10g21wnsJ'1 the logarithm value of wavelet module; a stands for Lipschitz exponent. Estimate of a by LMS algorithm representing the singularity degree of break point, namely the slope of WTMM lines (Illustrated as Figure 7).

I;=,(log2 IWf(s.x) 1) -log, A t al0g2 SI' (6)

Singnlarity points feature extraction of hydro-generator main shaft run-nut signals

Singularity point feature information includes singularity point position and singularity degree.

Algorithms: 1.With Bi-orthogonal spline wavelet, the signal is

2. Plot WTMM line in scale-space plane. 3.De-noise by proper threshold, and extract WTMhl

lines of signal's singularity points. 4.Locate singularity points according to cone

distribution of WTMM. 5.Estimate singularity point's singularity degree based

on LMS algorithm. In fault condition, because of abrasion, flaw, loosening

and place changed of contact surface, the main shaft of Hydro-Generator may endure local impact and sliding friction. Because feature information is usually submerged in vibrating signals, extracting feature from mechanical

3.2.4.

decomposing by CWT in the scales of [1:1:481.

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Proceedqv of the Third International Conference on Machine Learning and Cybemetics, Shanghai, 26-29 August 2004

n n i n t l I I I I I I I I I Lipschiu 11.221 1.521 1.791 1.491 1.061 1.361 1.991 1.521 1.281 1.49 Time(ms1 II 36511 5701 I88 1i1983h 983122Od24761258 11258 112748

vibrating signals is an effective method to judge whether components are fault. The rotating frequency of Hydro-Generator is 1.667 HZ and the fault frequency is usually not high, we choose 341HZ as sampling frequency so as to satisfy the condition of wavelet analysis. In normal condition, the shaft run-out signals are very smooth without isolated singularity points; however, the signals will have local singularity points when local sliding friction takes place.

Figure6 shows the main shaft run-out signal of Y coordinate. With Bird.4 basis, WT detects perfectly the singularity point. The 3D chart of CWT coefficients of the shaft run-out signal of Y coordinate is illustrated in figure 7, which obviously embodies the periodicity of four WTMM lines. And the third line's slope to scale is relatively small, and furthermore, the cone distribution of WTMM lines is obvious.

MO 400 600 800 1000 Figure 6. The main shaft run-out signal of Y coordinate

0 200 400 600 800 1000 Figure 7. The 3D chart of CWT coefficients

With wavelet shrinkage, the singularity points' Whl lines are separated from those of noise, as showed in Figure 8. From theorem above, at the singularity points, WTMM lines point to singularity point inside cone along with the scale decrease, namely, the crossing point of two WTMM lines. So the fault points can be located accurately in time-scale panel.

The expression 6 is used to estimate the singularity degree of singularity points. Namely, the Lipschitz exponent of WTMM lines is the singularity degree of singularity points.

Shown as table 3, the fault points of shaft run-out signal are respectively located at 173ms. 771ms. 1365111s and 2581ms, whose Lipschiz exponent is relatively small, with 1.667 HZ frequency equal to the rotating frequency of Hydro-Generator. The slope of the third WTMM line is small that is identical with the computation result, namely this points with high singularity degree caused by the shaft fiction in fact.

WTMM I I I I 12 I 13 I 14 I I5 I 16 1 17 I 18 I 19 I 2 0 Samples I466 I536 I642 I677 I677 I 751 I 845 I 881 I881 I938

4. Conclusions

The objective of this paper is to develop an online vibration monitoring system of hydro generator, which has the advantages of strong anti-interference and good extensibility. The results presented have shown that the objective is achieved. The field-nodes are distributed near to generators and sample signals at field, which avoids transmitting analog signal to measurement devices by long cable. The sampled result is transmitted to monitoring center by LonWorks network. The system can be extended by simply added more field-nodes. The software, which contmls both the acquisition and analysis of the signals, has been developed as a suh-part of the monitoring system. It can display the real-time waveform of vibration signals, the result of data process and detect fault information included in vibration signals.

References

[l] Echelon Corp. Neuron Chip Data Book. Feh, 1995 [ Z ] Motorola Corp. Lonworks Data Book. 1997. [31 L, Jing. "Feature detection and fault diagnosis based

on continuous wavelet transform," Mechanical engineenngjoumal. Vol36, No 12 pp. 95-100,2000.

[41 S. Mallat and W.L. Hwang. Singularity detection and processing with wavelets. Courant Institute of Mathematical Sciences New York University. March, 1992.

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