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Analysis of Truck Compressor Failures Based on Logged Vehicle Data

Prytz, Rune, Nowaczyk, Sławomir, Rögnvaldsson, Thorsteinn, Byttner, Stefan

Konferensbidrag (Refereegranskat)


In multiple industries, including automotive one, predictive maintenance is becoming more and more important, especially since the focus shifts from product to service-based operation. It requires, among other, being able to provide customers with uptime guarantees. It is natural to investigate the use of data mining techniques, especially since the same shift of focus, as well as technological advancements in the telecommunication solutions, makes long-term data collection more widespread.

In this paper we describe our experiences in predicting compressor faults using data that is logged on-board Volvo trucks. We discuss unique challenges that are posed by the specifics of the automotive domain. We show that predictive maintenance is possible and can result in significant cost savings, despite the relatively low amount of data available. We also discuss some of the problems we have encountered by employing out-of-the-box machine learning solutions, and identify areas where our task diverges from common assumptions underlying the majority of data mining research.

Nyckelord: Data Mining; Machine Learning; Fault Prediction; Automotive Diagnostics; Logged Vehicle Data

Citera: Prytz, Rune, Nowaczyk, Sławomir, Rögnvaldsson, Thorsteinn & Byttner, Stefan, Analysis of Truck Compressor Failures Based on Logged Vehicle Data, CSREA Press, 2013http://worldcomp-proceedings.com/proc/p2013/DMI8084.pdf