Composite fault diagnosis method and device based on deep decoupling convolutional neural network

Abstract

The invention discloses a composite fault diagnosis method and device based on a deep decoupling convolutional neural network. The method comprises the following steps: 1, data set collection and calibration: respectively collecting vibration acceleration signals under single and composite fault experiment conditions of a rotary machine to obtain a plurality of samples, respectively forming a training set and a test set, and respectively setting category labels; 2, building a one-dimensional deep convolutional neural network model; 3, constructing a decoupling classifier with a multi-label output characteristic under a keras framework; 4, training a network model, and finally obtaining an optimal deep decoupling convolutional neural network model; and 5, performing intelligent diagnosis and classification on the composite fault, and outputting to obtain a real-time diagnosis result of the composite fault. According to the method, on the premise that only a single fault signal is used for training the deep decoupling network model, each single fault feature in the composite fault signal is extracted, and decoupling and classification of the composite faults are achieved through a decoupling classifier.

Description

technical field [0001] The invention belongs to the technical field of mechanical manufacturing and relates to a mechanical fault diagnosis technology, in particular to a composite fault diagnosis method based on a deep decoupling convolutional neural network. Background technique [0002] Rotating parts such as bearings and gears are an essential general-purpose component in mechanical equipment and play an important role in modern industrial equipment. The fault diagnosis of rotating machinery is an important part of the preventive maintenance system, which is of great significance to prolong the service life of mechanical equipment, reduce maintenance costs and increase the safety of equipment operation. [0003] The fault diagnosis method based on artificial intelligence has been widely used in the fault diagnosis of rotating machinery and achieved good results. Generally, fault diagnosis of rotating machinery can be completed through vibration signal collection, featur...

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Problems solved by technology

[0006] In order to solve the problems existing in the prior art, the present invention designs a composite fault diagnosis method and device based on a deep decoupling convolutional neural network, and uses the feature learning ability of the deep convolutional neural network to deeply dig out the characteristics of each component in the composite fault. The single fault feature, combined with the multi-label output characteristics of the decoupling classifier, enables the constructed deep decoupling network model to achieve decoupling and classification of compound faults when only a single fault signal is used as the training set

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