关键词: 变分自编码器, 机械, 故障预警, 统计分布

Abstract: Due to vibration signals’ non-stationary characteristic of complex machinery such as reciprocating compressor and aeroengine, the single feature threshold alarm method frequently utilized at present has shortage of inaccurate alarm. For this reason, a variational auto-encoder-based method for fault early warning is proposed according to the response signals’ characteristics of mechanical response point. The method is based on high-dimensional feature parameters of the mechanical vibration signals. It is used to self-learn the statistical distribution model of high-dimensional features by variational auto-encoder, and the normal working condition model is used as a benchmark model. The fault early warning is realized by comparing the difference between real-time working condition model and benchmark model with the adaptive early warning threshold. The advantages of proposed method are verified by comparing with the single feature threshold alarm method and the state subspace-based early warning method. The results demonstrate that the method can improve the accuracy of mechanical fault early warning and greatly advance the alarm time point of the fault. It has high timeliness and strong adaptability.

Key words: variational auto-encoder, machinery, fault early warning, statistical distribution