Bayesian optimized deep convolutional network for bearing diagnosis

Abstract

The successful diagnosis of the faulty signal in rolling element bearings relies on the accurate evaluation of the early fault present within the components of bearings. Because of system imperfection and interference between the data acquisition devices, the fault signal is heavily masked by noise. Hence, to extract the fault information, signal processing techniques are widely used in bearing diagnosis. Although numerous research have dedicated on finding representative features to indicate the damage of the bearing elements, the correlation between defect size and acquired vibration signal has not been properly established. In the recent few years, deep learning has been widely used in bearing diagnosis. In general, the unprocessed signal is directly input into the deep learning model and the neural network extracts useful features during the optimization process. Until now, the selection of features from signals is arbitrary which does not yield much insights into the diagnosis and prognosis. In addition, the features could contain noise information, which could possibly deteriorate diagnostic or prognostic results, while the effect of using preprocessed data has not been fully explored. In this paper, we present an innovative diagnosis model using the deep convolutional network with Bayesian optimization to diagnose the defect severity of bearings. The acquired signal is initially processed using the complementary ensemble empirical mode decomposition method to extract the frequency band containing the fault signature. An experimental based defect size estimation equation is implemented to calculate the defect size based on the signal and experimental setup. After the estimated defect size is obtained, the deep convolutional neural network is implemented to categorize the defect severity. The parameters of the network are optimized by the Bayesian algorithm. The proposed algorithm can be used for diagnosis of the health condition of various rotating machinery.