Data-Driven Rotary Machine Fault Diagnosis Using Multisensor Vibration Data with Bandpass Filtering and Convolutional Neural Network for Signal-to-Image Recognition

Abstract

This paper proposes a novel data-driven method for machine fault diagnosis, named multisensor-BPF-Signal2Image-CNN2D. This method uses multisensor data, bandpass filtering (BPF), and a 2D convolutional neural network (CNN2D) for signal-to-image recognition. The proposed method is particularly suitable for scenarios where traditional time-domain analysis might be insufficient due to the complexity or similarity of the data. The results demonstrate that the multisensor-BPF-Signal2Image-CNN2D method achieves high accuracy in fault classification across the three datasets (constant-velocity fan imbalance, variable-velocity fan imbalance, Case Western Reserve University Bearing Data Center). In particular, the proposed multisensor method exhibits a significantly faster training speed compared to the reference IMU6DoF-Time2GrayscaleGrid-CNN, IMU6DoF-Time2RGBbyType-CNN, and IMU6DoF-Time2RGBbyAxis-CNN methods, which use the signal-to-image approach, requiring fewer iterations to achieve the desired level of accuracy. The interpretability of the model is also explored. This research demonstrates the potential of bandpass filters in the signal-to-image approach with a CNN2D to be robust and interpretable in selected frequency bandwidth machine fault diagnosis using multiple sensor data.