Industrial Large-Scale Diagnostic Model With Lightweight Customized Deployment for Distributed Multiple Non-IID Diagnostic Tasks

Industrial fault diagnosis frequently confronts the challenge that both the data and targets are not independent and identically distributed (Non-IID). Conventional fault diagnosis often requires tailoring the model to specific task scenarios, thereby suffering from high costs and limited generalization capabilities. An industrial large-scale diagnostic model (ILDM) is proposed, with lightweight learning and customized deployment for distributed multiple Non-IID fault diagnosis tasks. ILDM can acquire knowledge from large public datasets that completely irrelevant to downstream tasks, and has the advantages of low learning cost, robust task adaptability, and high quality deployment customization. Firstly, a large-scale foundation model is built utilizing time-series embedding and 1D-2D-1D variation, enhancing feature extraction capability while preserving the original temporal information. Secondly, through designed pre- and post-processors, the constructed foundation model is pre-trained on numerous public Non-IID datasets, ensuring that the model acquires generic classification capabilities. Finally, the pre-trained large-scale model is tailored for deployment at different low-computing-power terminals to conduct various process/system specific Non-IID diagnostic tasks, with lightweight customization achieved through fine-tuning and label smoothing based on terminal datasets. The performance of ILDM is evaluated across three representative Non-IID industrial diagnostic tasks in few-shot, and full-shot experiments, as well as in experiments with varying sample lengths. Additionally, the paper discusses the potential of larger-scale model and longer data transmission chain. Code is available at this repository: https://github.com/JMu-Jia/ILDM.