Mechanical equipment fault diagnosis method based on deep learning

A technology of fault diagnosis and deep learning, applied in neural learning methods, computer components, program control, etc., can solve the problems of extracted features, insufficient accuracy, and failure to consider operating parameter data, inspection data, etc.
CN111813084AActive Publication Date: 2020-10-23CHONGQING UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING UNIV
Publication Date
2020-10-23

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Abstract

The invention discloses a mechanical equipment fault diagnosis method based on deep learning. The method specifically comprises the following steps of S1, carrying out the data collection and preprocessing of a main data source and a secondary data source of mechanical equipment, and obtaining a data set; S2, a five-fold cross validation method being adopted to divide the data set into a trainingset, a validation set and a test set; and S3, establishing a fault diagnosis model based on the CNN and the BD-LSTM, inputting the training set into the fault diagnosis model, extracting hidden features, performing training, and outputting a diagnosis result. According to the method, BD-LSTM is adopted to perform smooth tracking and result prediction, and uncertainty caused by operation and environmental interference is processed, sensor monitoring data adopts CNN and BD-LSTM to extract hidden features in parallel, output of two irrelevant paths can influence prediction, and each parameter inthe network can be corrected according to predicted errors.
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Description

technical field

[0001] The invention relates to the technical field of system fault diagnosis, in particular to a method for fault diagnosis of mechanical equipment based on deep learning. Background technique

[0002] In intelligent manufacturing, the intelligent operation and maintenance and health management of equipment will inevitably penetrate directly into the operation management of the enterprise and even the entire life cycle of the product, thereby reducing the loss of the enterprise and affecting the decision-making of the enterprise. One of the key elements of the new model of intelligent manufacturing is "remote operation and maintenance service". (Products) Remote unmanned control, early warning of working environment, monitoring of operating status, fault diagnosis and self-repair, etc.

[0003] Fault diagnosis technology plays a vital role in large-scale systems, especially industrial systems. It can obtain the fault model of the diagnostic object through d...

Claims

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