Improved CNN for the diagnosis of engine defects of 2-wheeler vehicle using wavelet synchro-squeezed transform (WSST)

Abstract In this work, deep learning based diagnostic procedure is developed for the identification of engine defects of 2-wheeler vehicle. The process starts with acquisition of vibration data. Second, time domain signals are converted into angular domain. Third, random distribution of angular domain signals is done to have training and test data. Further, processing of training and test data is carried out using wavelet synchro-squeezed transform (WSST) to form time-frequency images. Then, cost function of convolution neural network (CNN) is modified by introducing a new entropy-based regularization function in the existing cost function which can meaningfully reduce the activation in the hidden layer of CNN so as to make the learning really deep. Thereafter, training of improved CNN is carried out using WSST images of training samples. In the next step, WSST images of test data are applied to tuned CNN for the identification of defects. A comparison of proposed method has been carried by existing deep learning solutions and the method proposed in the state-of-artwork. The comparison shows that the proposed method is at least 3.8 % more superior in terms of accuracy than the existing defect diagnosis methods while diagnosing defects of internal combustion (IC) engine of 2-wheeler vehicle.