An Interpretable Parallel Spatial CNN-LSTM Architecture for Fault Diagnosis in Rotating Machinery

In the evolving landscape of prognostics and health management (PHM) enhanced by the Internet of Things (IoT), diagnosing machinery system faults is critical for ensuring operational efficiency and safety across various industries. This research introduces a novel, interpretable deep learning architecture designed to overcome key limitations in existing fault detection methods, such as the high demand for extensive training data and the lack of transparency in feature extraction. Our model uniquely integrates dual branches: one processing raw time-series data through a spatially transformed convolutional neural network and another incorporating wavelet transform coefficients. This dual-branch approach not only maximizes the effective use of limited data but also significantly enhances model interpretability, eliminating the need for extensive feature engineering and manual feature selection. The significance of this research lies in its innovative methodology, which bridges the gap between advanced deep learning techniques and practical applicability in industrial settings. By leveraging IoT sensors and real-time data processing, our model exemplifies a practical application of IoT in PHM. The proposed algorithm is rigorously evaluated on experimental gearbox data and further validated on a publicly available bearing data set, demonstrating its generalizability and scalability. Through comprehensive parametric investigations, we elucidate the impact and robustness of the physics-integrated parallel architecture, showcasing its potential to significantly improve fault diagnosis accuracy in diverse operational conditions. This study not only advances the state-of-the-art in fault diagnosis but also provides a framework for developing more interpretable and efficient deep learning models for industrial applications.