An explainable neural network for fault diagnosis with a frequency activation map

Abstract

This paper constructs an explainable neural network model for fault diagnosis with a 1D vibration signal of equipment and proposes an explainable method with a frequency activation map of the proposed model. The frequency activation map visualizes the classification criteria of the time-domain-based learned model in the frequency domain. Since the 1D vibration signal for monitoring the normal and faulty states of equipment is easy to interpret in the frequency domain, the frequency activation map provides the user with a specific frequency of vibration signal where the proposed model focuses on for the classification of normal and faulty states. To generate the frequency activation map, the proposed model structure for learning the 1D vibration signals is designed to filter the frequency components of the 1D vibration signals using a 1D convolutional filter with a norm constraint. Simulation results with two open datasets demonstrate that the proposed model and explainable method can visualize the classification criteria of the model learned with vibration signals through a frequency activation map. Based on the frequency activation map, characteristic frequencies of normal and faulty states are identified.