Existing anomaly detection models of mechanical systems often face challenges for the equipment under multiple working conditions: the learning model under a single working condition is challenging to adapt to new working conditions, and centralized learning of multicondition samples leads to too low detection accuracy. A multiworking condition variational auto-encoder (MW-CVAE) is proposed to solve the problem. Based on the variational auto-encoder model, the working conditions of the equipment are regarded as the input. The anomaly evaluation threshold of each independent working condition is established by the centralized learning of normal multiworking condition samples. At the same time, it is found that the representation of each working condition sample in the space of the hidden layer forms a distribution close to the prior probability, providing a theoretical basis for the separation and evaluation of working conditions. By comparing and verifying the CWRU, JNU, and PU datasets, the new method significantly improves anomaly detection (the F1-score value is increased by 18-19%) and can be widely used in anomaly detection mechanical systems with various discrete working conditions.

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