Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed

Abstract

Automatic identification of bearing and rotor defects, when operated at varying speed is challenging. To make this challenging task possible, a tacho-less deep learning model is developed which can effectively learn, even from small data set. For accurate learning from small data set, existing CNN is made sparse. Sparsity is incorporated in the CNN by adding newly developed sparsity cost in the existing cost function of CNN to enhance the learning capability of CNN. The method works in the following steps. First, vibration signals are processed with Fourier synchro squeezed transform (FSST) to obtain tachometer information. The extracted tachometer information is used to change the time domain signal to angular domain signal. Second, wavelet transform of angular domain signals is carried out to produce time–frequency images. Third, time–frequency images of angular domain signals are applied to the improved version of CNN. After learning, time–frequency images obtained from angular domain signals of defective bearings and rotor are applied to detect defects. The defect identification accuracy attained by the proposed method is 96.6 %. This accuracy is higher as compared to the accuracy achieved by the methods used in existing works. This has been made possible due to sparsity cost functions assimilated in the cost function of CNN that evade avoidable activation of neurons in the feature extraction layers of CNN, which makes the learning of modified CNN becomes deeper in comparison to existing CNN.