Abstract: A multi-task feature sharing neural network-based intelligent fault diagnosis method has the following steps: (1) separately collecting original vibration acceleration signals of rotating machinery under different experimental conditions, forming samples by means of intercepting signal data having a certain length, and performing labeling; (2) constructing a multi-task feature sharing neural network, having: an input layer, a feature extractor, a classification model and a prediction model; (3) using multi-task joint training to simultaneously train the classification model and the prediction model; and (4) inputting a vibration acceleration signal collected in an actual industrial environment into the trained models to obtain a multi-task diagnosis result.

Abstract: Disclosed by the present invention is a multi-task feature sharing neural network-based intelligent fault diagnosis method, comprising the following steps: (1) separately collecting original vibration acceleration signals of rotating machinery under different experimental conditions, forming samples by means of intercepting signal data having a certain length, and performing labeling; (2) constructing a multi-task feature sharing neural network, comprising: an input layer, a feature extractor, a classification model and a prediction model; (3) using multi-task joint training to simultaneously train the classification model and the prediction model; and (4) inputting a vibration acceleration signal collected in an actual industrial environment into the trained models to obtain a multi-task diagnosis result. The present invention may simultaneously achieve the classification of fault type and the prediction of the degree of fault, and has high practical application value.