MMFNet: Multisensor Data and Multiscale Feature Fusion Model for Intelligent Cross-Domain Machinery Fault Diagnosis

Abstract

Although cross-domain fault diagnosis has received much attention in intelligent mechanical fault diagnosis, most existing methods only achieve knowledge transfer within the data from a single sensor. For a complex industrial system, multiple sensors are usually required to monitor its operating conditions coordinately. In such a situation, the fault diagnosis capability of existing cross-domain methods might be impaired significantly. To address this issue, in this article, a multisensor data and multiscale feature fusion network (MMFNet) is proposed to achieve cross-domain fault diagnosis using multisensor data. More specifically, a multiscale feature extraction module based on the pyramid principle is designed to fuse both deep and shallow features of the complex raw signal. Then, a Transformer-based multiscale features fusion and domain adaptation module is developed to fuse multiscale features and achieve the domain invariant of the sensor data. Finally, the proposed transferable Transformer is used for multisensor features cross-domain fusion. With the developed MMFNet, the features from multiple sensors can be comprehensively captured so that a more accurate fault diagnosis can be achieved. A series of experiments on a complex planetary gearbox demonstrate the effectiveness and practicability of the proposed method for fault diagnosis.