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.

在这项工作中，开发了基于深度学习的诊断程序来识别2轮车辆的发动机缺陷。该过程从获取振动数据开始。其次，将时域信号转换为角域。第三，对角域信号进行随机分布以得到训练和测试数据。此外，使用小波同步压缩变换（WSST）进行训练和测试数据的处理，以形成时频图像。然后，通过在现有成本函数中引入新的基于熵的正则化函数来修改卷积神经网络（CNN）的成本函数，这可以有意义地减少CNN隐层中的激活，从而使学习真正深入。此后，使用训练样本的WSST图像进行改进的CNN训练。在下一步中 将测试数据的WSST图像应用于经过调谐的CNN，以识别缺陷。现有深度学习解决方案与最新技术中提出的方法进行了比较。比较表明，在诊断两轮车的内燃机缺陷时，所提出的方法在准确性方面比现有的缺陷诊断方法高出至少3.8％。