Fault diagnosis in electric motors using multi-mode time series and ensemble transformers network.

Induction motors are essential in industrial production, and their fault diagnosis is vital for ensuring continuous and efficient equipment operation. Minimizing downtime losses and optimizing maintenance costs are key to maintaining smooth production and enhancing economic efficiency. This paper presents a novel diagnostic approach for diverse motor faults, integrating time series analysis, Transformer-based networks, and multi-modal data fusion. Firstly, multiple signals such as three-phase current, vibration, device sound, and ambient sound are collected to form a multi-modal dataset. Subsequently, a Transformer network for single time series classification is developed, and multiple instances are concatenated in parallel to create an ensemble Transformer network. The self-attention mechanism is then utilized to dynamically integrate features from different modal data for accurate motor fault identification. During network training, the chaotic WOA optimizes the ensemble Transformer network's hyper-parameters. Finally, the proposed method is trained and tested on a motor measurement multi-modal dataset. Experimental results show that it performs outstandingly on multi-modal datasets, attaining a high diagnostic accuracy of 99.10%. Compared with single-mode data and state-of-the-art methods, it demonstrates superior diagnostic accuracy and reliability.