Effective anomaly detection and timely fault warning are essential to ensure the continuous and safe operation of mechanical equipment and to prevent equipment deterioration. In the unsupervised modeling and detection scenario, fault detection methods based on the autoencoder framework have been widely concerned and applied. Unfortunately, such methods can only be applied to specific or constant operating conditions, and their detection performance is greatly reduced due to the different data distribution in the face of complex operating conditions. Aiming at the problem of unsupervised fault detection under complex operating conditions, this article proposes a prototype-assisted multiscale graph representation learning-based mechanical fault detection method. First, the vibration data of the equipment is fed into the multiscale decomposition module (MDM) to obtain multiscale feature maps that can express rich detail information. Then, the multiscale feature maps are fed into the graph representation learning module (GRLM) to fully learn the potential relationships and interactions between different scales and provide a more comprehensive representation of the dynamic characteristics of the equipment. Finally, multiple MDMs and GRLMs are cascaded to construct a feature extractor to map the data of each operating condition to the latent space, and the proposed prototype-assisted strategy is used to determine the real-time state of the equipment. Case studies have been carried out on two different pieces of mechanical equipment. The experimental results show that the average accuracy of the proposed method is as high as 98.44% and 98.90%, respectively, and it maintains a low missed detection rate and zero false alarm rate in the two validation processes, which is more in line with the needs of engineering applications than other comparison methods.
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