Mechanical equipment diagnosis classification method based on probability confidence convolutional neural network

Abstract

The invention discloses a mechanical equipment diagnosis classification method based on a probability confidence convolutional neural network, and relates to the field of mechanical equipment state monitoring and fault diagnosis. The method comprises the following steps: training a CNN-based diagnosis classification model by taking known state category data of mechanical equipment state monitoringas a training sample, and outputting the probability that the sample belongs to each state category; and calculating the probability confidence of each state category of the diagnosis classificationmodel, testing the diagnosis classification model by utilizing the real-time operation data of the mechanical equipment, and judging the state category of the real-time operation data of the equipmentaccording to the probability confidence of each state category. Self-learning updating of the diagnosis classification model is carried out when an unknown state category appears. Whether the to-be-detected data is in an unknown state or not is judged according to the probability that the CNN outputs each type of state. And when an unknown state occurs, the diagnosis classification model can perform self-learning updating by utilizing the state data, thereby realizing self-adaptive learning of a new state.

Description

technical field [0001] This patent relates to the field of mechanical equipment condition monitoring and fault diagnosis, and in particular to a mechanical equipment diagnosis and classification model based on probability confidence convolutional neural network. Background technique [0002] The fault diagnosis of mechanical equipment is to analyze the health status of the equipment state through its monitoring data. The traditional mechanical fault diagnosis technology mainly based on signal processing technology is widely used in practical engineering applications and achieves good diagnostic results, but it is difficult for complex equipment. and changeable operating conditions, traditional diagnostic techniques cannot achieve equipment fault diagnosis and classification. With the rapid development of artificial intelligence technology, intelligent diagnosis methods based on machine learning have received more and more attention. The deep learning algorithm represented by...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the unknown type of state cannot be diagnosed in the existing convolutional neural network-based mechanical equipment diagnostic classification method, the present invention proposes a mechanical equipment diagnostic classification model based on the probability confidence convolutional neural network, which can effectively distinguish equipment that has been Known and unknown types of states, and the model has self-learning update ability

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