Diagnosis and Classification of Diesel Engine Components Faults Using Time–Frequency and Machine Learning Approach

Diagnosis of engine component faults is a challenging task for every researcher due to the complexity involved in the engine operations. The developed faults on the engine components subsequently reduce their performance and cause higher maintenance costs. Hence, an effective condition monitoring technique should be implemented to diagnose engine component faults. Therefore, in this work, potential fault diagnosis techniques are presented to detect and diagnose the scuffing faults developed on the diesel engine components. Condition monitoring techniques such as vibration and acoustic emission analyses were employed to acquire the fault-related signals. These signals were analyzed in the time-domain, frequency-domain, and time–frequency domain using signal processing methods viz. fast Fourier transform (FFT) and short-time Fourier transform (STFT). The statistical feature parameters were also extracted from the acquired signals to diagnose the severity of the faults. Further, the artificial neural network (ANN) models were developed to predict and classify the scuffing faults developed on the engine components. The results showed that the FFT and STFT techniques provide better fault diagnostic information. The developed neural network models have effectively classified the scuffing faults on engine components with an accuracy of 100%.