CONDITION MONITORING, DIESEL ENGINES, AND INTELLIGENT SENSOR PROCESSING1

Amanda J.C. Sharkey2

1 Introduction The aim of this presentation will be to provide an account of a condition monitoring project in which sensor data corresponding to the manifesta- tion of certain known, but subtle faults in a diesel engine were used to train Artificial Neural Nets. Mechanical systems in general, and diesel engines in particular, can be expected to develop faults if operated for any length of time. These faults need to be detected and identified, prefer- ably at an early stage before they have a chance to become compounded. There would be clear advantages to an automatic system that monitored the performance of a diesel engine and provided early warning of fault conditions. Such a system could provide a useful supplement or alterna- tive to occasional checks or scheduled maintenance by skilled engineers. It would provide a complimentary alternative to that of novelty detection, in which the aim is to identify unexpected departures from the normal operation of a machine, or system, but which may be less likely to identify subtle faults at an early stage. The approach taken here was to physically induce faults, one at a time, in a diesel engine; to collect data corresponding to these fault conditions by means of sensors attached to the engine; and to use these data to train ANNs to recognise the fault conditions. Data corresponding to to both normal and four fault conditions were obtained by creating (subtle) faults in the engine, and through the use of of three different types of sensor: (i) acoustic emission (ii) vibration and (iii) in-cylinder pressure.

2 The engine, the sensors, and the data collec- tion system

A 4 stroke, twin cylinder, air cooled, naturally aspirated diesel engine was the subject of all the experiments. It had a maximum power output of 10.4 kW at 3000 revolutions per minute (RPM) with a speed range

2We would like to thank the EPSRC Grant No.GR/K84257 for funding this research. 2Department of Computer Science, University of Sheffield

0 2001 The Institution of Electrical Engineers. Printed and published by the IEE, Savoy Place, London WCPR OBL, UK. 1/1

of 1000 to 3000 RPM. The data acquisition system did not depend on a timebase; instead the crank angular position was used as an external clocking signal to digitise the signals (see [8] for further details). Data was acquired under 5 conditions which included 4 fault states and the normal operating state. Engine speed was varied from 1800 to 2800 R P M during the data collection stage. The faults induced were

0 Leaking exhaust valve (E) (A fine cut across the seat of the valve lid)

0 Leaking air inlet valve (I) (Fault induced as above in air inlet valve)

0 Blocked fuel injector (B) (An injector nozzle with 1 out of 4 holes blocked in service)

0 Poor fuel atomisation (L) (An injector nozzle with imperfect atom- isation pattern.)

Cylinder pressure was sensed with a Kistler 6125A quartz pressure sen- sor, while engine vibration was sensed with a Bruel & Kjaer Delatron miniature accelerometer with an upper frequency limit of 25 kHz. The vibration signal was band pass filtered from 20 Hz to 18 kHz using an analogue filter. The acoustic emission was sensed by means of a wide band sensor with an operational frequency range of 100 to 1000 kHz and peak reference sensitivity of 55 V/m/s mounted on unit no.1 cylinder head. The output of the sensor was amplified by a preamplifier with a gain of 40 DB and an band pass filter of 100 kHz to 1024 kHz. The faults induced in the engine were all combustion-related. As such they would be expected to affect the in-cylinder pressure, which in turn would affect the vibration patter of the engine. Changes in in-cylinder pressure would in turn affect the vibration signal. The use of an ~ C O U S -

tic emission sensor was more unusual. Acoustic emission, or stress wave emission has been predominantly used in the detection of microscopic changes in material where the atomic rearrangement within a material during deformation and cracking produce elastic waves. However, work on railway signalling equipment (Fararooy and Allen, 1995) in monitoring rate of compressed air leakage from damaged signal arm control equip- ment implies any gas pressure leak would result in a high frequency hissing noise, to which the acoustic emission signal would be sensitive.

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3 Decision Fusion and Diversity There are two questions of particular interest here. The first, and more obvious one, is whether it is possible to train ANNs on the basis of the sensor data to recognise the induced faults, and to distinguish them from normal operation. The second, concerns the extent to which the perfor- mance of such a neural net based system could be improved by means of decision fusion, or, in other words, through the formation of some form of multinet system.

3.1

Most existing machinery diagnostic systems use information provided by several sensors, which makes their implementation in a practical environ- ment difEicult. One of the concerns of our work was to explore the use of single sensors for diagnostic purposes, and to this end we developed neural net fault classifiers based on data from unitary sensor information. All the 3 signal types (cylinder pressure, vibration, and acoustic emission) were processed independently of each other, and neural net classifiers trained and tested. The results of this research shows that each signal type could be used to identify the faults in question, although some are more ef- fective than others. In [3], the best generalisation results for the best single net trained on each type of sensor data were respectively 90.1% for cylinder pressure, 97.4% for vibration and 98.3% for acoustic emission. In achieving these results, a number of different preprocessing methods (e.g. wavelet decomposition, and principal component analysis) were explored; further details can be found in the relevant papers (e.g. [4],[5],[6], [7]).

Fault diagnosis with single sensor data

3.2

The research on single sensor data confirms the feasibility of training ANNs to recognise known, but subtle faults, using single nets, on the basis of data from a single sensor. And the data acquisition system, and the resulting set of data, have considerable value themselves. However, by combining nets to form multi-net systems, it was possible to improve on their generalisation performance, and at the same time, to investigate the determinants of effective multi-net systems. The motivation behind the notion of combining nets is that of improving performance. There would be little advantage to combining nets that were identical, in the sense that they show the same patterns of generalisation. However, where a set of nets show different patterns of generalisation,

Combining nets for fault diagnosis

their combination into an ensemble can lead to improved performance. To give a simple intuitive example: In Table 1, it is apparent that each of the nets in question makes one error. However, since each shows a different pattern of errors, if they were combined by means of a majority vote they would produce a correct response to all of the inputs.

net1 net2 net3 11 II input C I 1 I O I 1 II

Table 1: No coincident failures

An important issue in ensemble research is how best to achieve the sit- uation in which a set of separately trained nets, or estimators, make as few coincident errors. A considerable number of different methods have been proposed (see [9] for an overview). In addition to well-known algo- rithms such as Bagging and Adaboost, these include the application of different preprocessing methods to the data used to train different nets, and the use of data from different sources. This last method is particu- larly appropriate for the present application. Since different sensors were used to collect the data from the diesel engine, and since each of these data sources could be used in isolation to perform the fault classification, it would make sense to explore the effect of combining nets based on different data sources. Accordingly, experiments were conducted to compare the effect of decision fusion, where the combined nets were each trained on merent types of data, to other ensemble creation methods (see [l] for a fuller account). As expected, better generalisation results were obtained when ensemble combinations were used, as compared to choosing the best performing single net trained on data from a single sensor(90.6%). In addition, better generalisation results were obtained from ensembles of three nets where each was based on a different type of data (96.4%) than when all three nets were based on data from the same sensor (95.8%).

4 Conclusions

The research briefly outlined here indicates the feasibility of a condition monitoring system in which ANNs are trained to recognise certain known

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faults. Such a system, as discussed earlier, could provide useful early warning of known fault conditions before they have a chance to develop and become compounded. Data from any one of the three sensors could be used for fault classification, but better performance was obtained as a result of decision fusion in which a set of differently trained nets were combined to form a multinet system. These findings provide further con- firmation of the benefits of multiple classifier systems that have been found in a number of other application domains.

5 References

Fararooy, S., and Allan, J. (1995) Condition monitoring and fault diag- nosis of railway signalling mechanical equipment using acoustic emission sensors, Insight, 4, 294297.

6 Publications related to diesel engine project

Also see our web page: http: //www .dcs .shef. ac.uk/ amanda/enginepage. html, where on-line versions of several of these papers can be found.

[l] Sharkey, A.J.C., Sharkey, N.E., Gerecke, U., and Chandroth, G.O. (2000) . The “test and select” approach to ensemble combination. Pro- ceedings of First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, 1857, 30-44, ISBN 3-540-677046.

[2] Sharkey, A.J.C., Chandroth, G.0, and Sharkey, N.E. (2000) A Multi- Net System for the Fault Diagnosis of a Diesel Engine. Neural Computing and Applications, 9: 152-160.

[3] Sharkey, A.J.C., Chandroth, G.O. and Sharkey, N.E. (2000) Acoustic Emisssion, Cylinder Pressure and Vibration: A Multisensor Approach to Robust Fault Diagnosis. In Proceedings, of IJCNN2000, Como, Italy, July.

[4] Sharkey, A.J.C. (2000) Combining Artificial Neural Nets. In (Ed) A. Kent and J.G. Williams, The Encyclopedia of Computer Science and Technology, Vol 42, Supplement 27, Marcel Dekker Inc, New York, 1-21,

[5] Chandroth, G.0, Sharkey, A.J.C. and Sharkey, N.E. (1999) Vibration signatures, wavelets and principal components in diesel engine diagnos- tics. Marine Technology ODRA 1999, 11-13 October, Poland.

ISBN: 0-8247-2295-7

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[6] Chandroth, G.O. and Staszewski, W.J. (1999) Fault detection in inter- nal combustion engines using wavelet analysis. Proceedings ’Comadem 99’, Sunderland, UK, July, 1999

[7] Chandroth,G.O., Sharkey, A.J.C. and Sharkey, N.E. (1999) Cylinder pressures and vibration in internal combustion engine condition monitor- ing. Proceedings ’Comadem 99’, Sunderland, UK, July, 1999

[8] Chandroth, G and Sharkey, A.J.C. (1999) Utilising the rotational mo- tion of machinery in a high resolution data acquisition system. In Pro- ceedings of Computers and Ships, Organised by The Institute of Marine Engineers in association with The Royal Institution of Naval Architects.

[9] Sharkey, A.J.C. (1999) (Ed) Combining Artificial Neurd Nets: En- semble and Modular Multi-Net Systems, Springer-Verlag.

[lo] Sharkey, A.J.C. (1999) Multi-Net Systems. In (Ed) A.J.C. Sharkey Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Sys- tems, Springer-Verlag, pp 1-30.

[ll] Chandroth, G., Sharkey, A.J.C. & Sharkey, N.E (1998) Artificial Neural Nets and Cylinder Pressures in Diesel Engine Fault Diagnosis. In Proceedings of INMARC098 (Institute of Marine Engineers (India)), Mumbai, India.

[12] Sharkey, A.J.C., Sharkey, N.E., and Cross, S.S. (1998) Adapting an Ensemble Approach for the Diagnosis of Breast Cancer. In Proceedings of ICANN 98, Springer-Verlag, pp 281-286.

[13] Sharkey, A.J.C. and Sharkey, N.E. (1997) Combining diverse neural nets The Knowledge Engineering Review 12:3, 231,247

[14] Sharkey, A.J.C. and Sharkey, N.E. (1997) Diversity, Selection, and Ensembles of Artificial Neural Nets. In Proceedings of NEURAP’97, Neu- ral Networks and their Applications, 205-212.

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