A fault diagnosis method using Interval coded deep belief network

The rolling bearing, though a widely used component in rotating machinery, is also failure-prone. There are some difficulties in extracting valuable fault features from the vibration signal generated by the rolling bearing operation. Based on the interval coded deep belief network, an improved method for fault diagnosis is introduced to deal with the existing problems. Interval code is an encoding method to characterize fault features by using bearing vibration amplitude distribution. This method can also reduce the complexity of the original data and integrate input dimension. The deep belief network can remove the dependence from manual feature extraction and quickly extract the fault features from coding results. This proposed method is employed in the fault diagnosis experiments of rolling bearing to evaluate the performance. From the obtained experiment results, it can be concluded that the interval coded deep belief network boosts performance, which can eliminate the limitation of the input data dimension in the traditional deep learning model.