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

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 unkno