Rotating machinery health assessment method for deep self-encoding network

The invention discloses a rotary mechanical health assessment method for a deep self-encoding network. The method comprises the steps of (1) vibration signal acquisition, (2) original feature extraction, (3) feature dimension reduction by using a deep auto-encoder (DAE) network, (4) feature selection, (5) health indicator construction by using an unsupervised SOM algorithm, and (6) health indicator evaluation by using a fusion evaluation criterion based on a genetic algorithm. According to the method, the advantages of the powerful feature extraction ability of deep learning are combined, deepself-encoding and minimum quantization error methods are combined. In addition, an evaluation criterion based on one metric often has a bias problem, and the invention provides the fusion evaluationcriterion based on the genetic algorithm. According to the method, the health state of rotary machinery can be accurately evaluated, the method can be widely applied to the health assessment of rotarymachinery in the fields of