Fault detection of engine timing belt based on vibration signals using data-mining techniques and a novel data fusion procedure

In this research, an intelligent procedure was designed and implemented based on vibration signals for detecting and classifying prevalent faults of an internal combustion engine timing belt. The vibration signals of the timing belt were captured during operation in six different states: healthy, tooth crack, back crack, wear, separated tooth, and oil pollution. These signals were processed at three domains, namely, time, frequency, and time–frequency domains. Time-domain signals were transformed into the frequency and time–frequency domains using fast Fourier transform and wavelet transform, respectively. Then, six statistical features were extracted from vibration signals at all three domains. The extracted features were used as inputs to an artificial neural network for the primary classification of timing belt defects. Classification accuracy of artificial neural network in detecting and classifying timing belt faults in the time, frequency, and time–frequency domains have obtained 71%, 78%, and 84%, respectively. Combining separate classification accuracies from time, frequency, and time–frequency domains has been implemented using Dempster–Shafer theory of evidence. Classification accuracy based on the fusion of time- and frequency-domain classifiers was 97%, from time and time–frequency results was 98%, and from frequency and time–frequency results was also 98%, whereas the combination of results for all domains led to a >99% accuracy. Results show that the proposed methodology can detect and classify timing belt defects with high precision and reliability before failure occurrence.