A New Multisensor Data‐Level Fusion Method for Deep Learning–Based Fault Diagnosis of Rotating Machines: Considering Varying Sampling Frequencies and Different Sensor Mounting Directions in Vibration Signal Analysis

Abstract

Vibration signals are the most widely used condition monitoring data in deep learning–based fault diagnosis for rotating machines. However, relying solely on data from a single vibration sensor often limits the diagnostic accuracy of the diagnosis models. To overcome this challenge, researchers have explored multisensor data fusion techniques. Nevertheless, existing fusion approaches face challenges when dealing with variations in sampling frequencies and different sensor mounting orientations. In this paper, therefore, we propose a new data‐level fusion method, compensated synchronized resampling and weighted averaging fusion (CSR‐WAF), to enhance the accuracy of deep learning–based fault diagnosis in rotating machines. In this method, the CSR component first synchronizes the sampling frequencies of vibration data and compensates for sensor orientation. Subsequently, the WAF technique fuses the multisensor vibration data. The fused data are then processed using a one‐dimensional convolutional neural network (1DCNN) for fault diagnosis. Experiments conducted using motor bearing vibration signals sampled at 12 and 48 kHz show that the proposed CSR‐WAF‐1DCNN method achieves an accuracy of 99.87%. Furthermore, the proposed method is applied to gearbox fault diagnosis, accounting for different sensor mounting directions, and achieves an accuracy of 97.91%. These results confirm the reliable performance and practical applicability of CSR‐WAF‐1DCNN across diverse data acquisition scenarios.