MSTKernel Net: a rolling bearing intelligent diagnosis framework based on short-time time–frequency convolution

Abstract Monitoring rotating machinery is a key task in modern production processes. The emergence of deep learning technology has significantly improved the performance of intelligent diagnosis systems for such machinery. However, despite the commendable performance of many existing frameworks, they lack transparency, which hinders their interpretability in fault diagnosis based on directional signals. This study addresses this challenge by delving into the fault features present in vibration signals and designing a convolutional module specifically tailored to these characteristics, modularized short time–frequency kernel (MSTKernel). This innovative framework, MSTKernel Network, employs convolutional neural networks for feature extraction, simulating the time–frequency sliding process through convolutional properties while preserving temporal features and enriching fault diagnosis information. Through experimental data testing and visualization of convolutional kernel characteristics, we evaluate the potential of this framework to significantly enhance the fault diagnosis capability of rolling bearings, demonstrating its practicality and effectiveness in real-world applications.