A wavelet-based approach to abrupt fault detection and diagnosis of sensors

A Wavelet-Based Approach to Abrupt Fault Detection

and Diagnosis of Sensors

Jian Qiu Zhang,Senior Member,IEEE,and Yong Yan

Abstract—This paper describes a novel wavelet-based approach to the abrupt fault detection and diagnosis of sensors.By the use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains,the occurring instants of abnormal status of a sensor in the output signal can be identified by the multiscale representation of the signal.Once the instants are detected,the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults. Synthetic data simulated by means of a computer using real-word data from a general-purpose pressure sensor have verified the ef-fectiveness of the proposed method.

Index Terms—Abrupt faults of sensors,fault detection and diag-nosis,wavelet transform.

I.I NTRODUCTION

D URING the last two decades,a number of approaches con-

cerned with fault detection and diagnosis(FDD)have been reported.Most of them are based on hardware,functional,or analytical redundancy[1]–[11].The approaches based on hard-ware redundancy correspond to constructing redundant phys-ical subsystems(for example,multiple sensors)into the system under development[1],[4].The output signals of each redun-dant subsystem are then compared for consistency:in the case of a failure,a subsystem backup is switched on.However,often the additional cost,space,and/or complexity of incorporating redundant hardware make this approach unattractive.

The idea behind the methods of functional and analytical redundancy is based on the fact that the outputs of sensors measuring different quantities on the same system depend on a unique system“dynamic status,”which can be represented by a suitable mathematical model[1]–[4].The dependence among the different sensor outputs is called“analytical”(or“func-tional”)redundancy and can be used to detect faults that may occur in sensors.However,most of the functional or analytical methods tend to focus on system level FDD by exploiting the analytical models of systems[1]–[3],expert systems[1],fuzzy logic[5]–[7],and neural network approaches[8]–[11].One of the disadvantages of these methods is that the fault detection of an individual sensor is disregarded,making the operational fault detection schemes of the system level incomplete or

Manuscript received September27,1999;revised April16,2001.This work was supported by the Leverhulme Trust(UK).

The authors are with the Advanced Instrumentation and Control Re-search Center,The School of Engineering,University of Greenwich, Medway Campus,Chatham Maritime,Chatham,Kent,U.K.(e-mail: J.Q.Zhang@;Y.Yan@).

Publisher Item Identifier S0018-9456(01)08759-9.unreliable to some extent.In addition,the system level FDD schemes suffer from a number of drawbacks:

1)The methods built on the dependence on the outputs of

different sensors may not be economically justified or technically feasible because there are many cases where the functional relationships among the sensors are not al-ways available to form the analytical redundancy.For in-stance,the individual sensors for temperature,pressure, level,and pumped flow in the batch control of a simple process loop may not be functionally related[12].

2)There are also many situations in practice where an accu-

rate mathematical model is either unavailable or too com-plex,and the task of building an I/O model is impractical.

3)Since the models are“tailor-made”and the FDD al-

gorithms are“well-tuned”for each process,a slight modification of the monitoring system or the introduc-tion of the same schemes to a new application could be extremely expensive and could demand an enormous amount of expertise.This shortcoming is particularly evident in those schemes where the redundant models are constructed by learning the I/O measurement data of systems such as neural networks,fuzzy logic,and neuro-fuzzy approaches.

4)Including the combined problem of detecting process and

sensor faults increases the combinatorial dimension and hence the cost.The monitoring process has to be carried out on system levels and competes with computer control, scheduling and optimization activities.

In recent years,several local sensor FDD schemes have been proposed to overcome some of the above limitations by using so-called decentralized and hierarchical approaches[13],[14], where faults in the sensors themselves are detected or diagnosed at a lower level in an overall detection and diagnosis scheme (Fig.1).The input of a sensor is,however,generally unknown due to the fact that sensors are used to convert some quanti-ties that are inconvenient or cannot be explicitly converted into measurable parameters.Therefore,the information for the local sensor FDD scheme is limited to the unprocessed sensor output without any external support.In view of this,the methods avail-able now for sensor FDD schemes are based on the autoregres-sive time series model[13]and the power spectral analysis of sensor output signals[15].The method proposed by Yung et al.

[13]assumes that the output of a sensor is stationary,ergod-icit,and zero-mean while the approach of Amadi-Echendu et al.[15]assumes that the nature of excitation at the input of a sensor is unknown but a wide-sense stationary signal with a ra-tional power spectrum in the discrete time domain.However,a sensor fault implies that its operation deviates from its normal

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