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Fault diagnosis of automotive machine control

Posted: 05 Dec 2013     Print Version  Bookmark and Share

Keywords:Fault diagnosis  early fault detection  hybrid automobiles  DC starter  algorithm 

Fault diagnosis, also known as early fault detection and classification, is an emerging field of research and development. A myriad of techniques related to diagnosis have been proposed in the literature that can be grouped as model-based, signal-based, and data-based methods.

A large variety of equipment and systems have been analysed for health monitoring using these techniques. Keeping in view increased utilisation of electrical machines in electric and hybrid automobiles, early fault detection and classification methods for machines is an area which is currently being investigated.

In general, fault detection methods require additional computation strength, high resolution, large amounts of memory, and consume more power, which increases the associated cost of embedded hardware required to implement a diagnosis algorithm. Such an algorithm should be capable of producing reasonable results using cost effective hardware, but unfortunately that comes with the compromise of lesser resolution, limited memory and low power consumption. In this article, we look at fault diagnosis by describing a method for gear faults in the DC starter machine of an automobile.

Electrical machines in automobiles
In modern automobiles, such as electric or hybrid models, electrical machines ranging from a few watts to many kilowatts are being employed for a variety of applications. Small machines are being used for power windows, wiper drives, side mirrors, seat and steering wheel adjustment, and similar utilities. However, in electric or hybrid power trains and steering gears, higher power motors are used. These perform critical operations in the automobile; for example, the steering gear motor is of significant importance. Hence, timely fault diagnosis and close to realistic prediction of future operation is mandatory.

Keeping in view the increased utilisation and critical operation of electrical machines, fault diagnosis and prognosis has become an area of serious and thorough investigation during the last decade. A variety of diagnosis techniques have been presented in the literature based on both intrusive and non-intrusive methods.

Motor current signature analysis is a popular approach for machine fault diagnosis. Like any other hardware, electrical machines are prone to wear and tear. This wear and tear is reflected in the way the current and voltage is drawn into these machines. Observation, collection, and analysis of this data are the basis of fairly extensive research into fault diagnosis algorithms. This research has addressed this problem from simple classifier-based models to more involved graphical models such as Hidden Markov models. Though yielding excellent results, these models do not scale to the environments where electrical machines are deployed.

Electrical machines are, in general, deployed in environments that are constrained across memory and computational requirements. Thus, designing a model that is robust, accurate, and simple enough to deal with the variety of problems that are encountered in such environments is a significant challenge. Ali et al [1] took a significant step in this direction by training Support Vector Machines in real time on sensor networks, but their model doesn't reflect enough on how the results were achieved. In the following sections, we discuss in detail the problem, the methods employed in Fault Diagnosis research, and finally present our approach.

Detecting faults
Simple solutions built upon models that analyse sounds to distinguish between normal and faulty motors, though tempting, are not implementable in automobiles because of significant noise generation within an automobile engine. Additionally, sound recognition is a computationally expensive process that will be significantly difficult to download to an embedded machine with limited resources. Finally, different motors with faulty conditions are bound to produce a varying level of sound effects, and scaling our algorithm to arbitrary motors would be a herculean task.

Under normal conditions, the current sinking into electrical machines follows a certain pattern. However, if the machine develops a fault, the fault will appear in the machine current and produce transients. These transients can appear in the form of sharp impulses that instantly die down or appear as high frequency harmonics. In particular, as suggested by Zaidi et al [2], the transient harmonics can have a frequency of up to 12kHz. Locating and classifying these transients in a noisy environment using low memory space and with less computational power is a significant challenge. A suitable embedded system that can provide the desired results must be identified. A pictorial explanation of gear faults with varying degrees of seriousness is shown below, courtesy of Zaidi et el [2].

Figure 1: Gear faults.

Fault diagnosis models
In the field of diagnosis, fault is a condition in which equipment starts to exhibit early signs of defect. If not mitigated, these defects worsen and equipment becomes unable to perform the desired task. This condition is called the failure state. The span between fault and failure is where the opportunity timely fault diagnosis lies. In the field of Electrical Machine Fault Diagnosis, the three primary methods generally employed are Data-Based, Model-Based and Signal-Based [2].

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