Condition monitoring and diagnostics for automotive applications

Classification of signals acquired by condition monitoring systems for automotive application is becoming increasingly important. The work presented in this paper is motivated by a real-life classification challenge organized by Ford. Data samples from an automotive subsystem were collected. A classifier is designed to robustly isolate the different types of problems, by analyzing the acquired signals. In this paper, the wavelet transform is used as data reduction and feature selection tool. The proper input feature is then classified by neural network as a nonlinear classifier tool. Results show significant accuracy with reduced amount of false positives.