Application of Statistical Techniques and Neural Networks in Condition-Based Maintenance

Authors


Correspondence to: Dr. Ashok Prajapati, Department of Electrical and Computer Engineering, Oakland University, Rochester MI 48309–4401.

E-mail: ashokkp@ieee.org

Abstract

In this paper, we have evaluated five prediction approaches from two disciplines for condition-based maintenance. It also includes a case study for vehicle tire pressure monitoring as an example application. Main focus of this paper is on two widely used areas in prediction: (i) statistics, (ii) neural networks. It is well known that these two areas have wide applications in forecasting. Statistical and neural network techniques are very powerful for predicting the future states based on current and previous states of the system or subsystem. Application of ARAR and Holt-Winters (HW) in CBM has been presented from the statistics point of view. On the other hand, application of focused time delay, linear predictor, and backpropagation neural network has also been presented to prove the robustness of statistical approaches. Paper presents detailed comparative simulation study to show the suitability and feasibility of all the techniques. We assumed that the sensors are directly mounted on tires externally and report the current tire pressure to control or analysis. The control unit performs tire pressure analysis and reports the decision to operator or intended group about current pressure as well as the impending pressure conditions. Finally, investigation ends with conclusion that HW is best suited among these five approaches for tire pressure prediction and could be useful to design a CBM application for any system. Copyright © 2012 John Wiley & Sons, Ltd.

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