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

• Published in: Knowledge-based systems 2020-11, Vol.208, p.106453, Article 106453
• Main Authors: Kumar, Anil, Gandhi, C.P., Zhou, Yuqing, Vashishtha, Govind, Kumar, Rajesh, Xiang, Jiawei
• Format: Article
• Language: English
• Subjects: 2-wheeler vehicle; Artificial neural networks; Convolution; Cost function; Deep learning; Defects; Diagnosis; Diagnostic systems; Improved convolution neural network (CNN); Internal combustion engines; Medical imaging; Regularization; Signal processing; Tacholess; Training; Wavelet synchro-squeezed transform
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2020.106453

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. •An improved deep learning model is developed for the diagnosis of engine defects of 2-wheeler vehicle.•Entropy based divergence function is introduced in the existing cost function of CNN.•The proposed function measures diversion of average sparsity from desired sparsity.•Regularization amount of diversion is added to cost of misclassification.•The superiority of proposed method is validated by comparing the performance with existing state-of-art works.