A method for constructing rolling bearing lifetime health indicator based on multi-scale convolutional neural networks

Abstract

The degradation of rolling bearings is complex, and traditional methods of degenerating feature degradation are highly dependent on previous research and expertise. However, the traditional methods’ ability for learning the complex relationship between degraded features and large amounts of measured data is limited. So, it is difficult to construct a single health indicator (HI) to predict the degradation state of the bearing. In order to solve this problem, a multi-scale convolutional neural network (MSCNN) is proposed, which uses the frequency signal as input to construct HI. In combination with the “bathtub curve,” the HI we proposed is constructed by the inverse hyperbolic tangent function (atanh) method which is different from the proportional HI construction method. Five kinds of processing methods, such as original vibration signal, frequency signal and three kinds of modal decomposition preprocessed signals, are tested with PHM 2012 bearing lifetime data. And the proposed method is compared with other three HI construction methods. The experimental results show that under the HI construction method of atanh, the MSCNN model with the input of frequency signal can better represent the degradation state of the bearing compared with other methods. Meanwhile, the remaining life prediction is more accurate compared.