Equipment fault diagnosis method with adaptive learning rate based on deep learning

An adaptive learning rate and deep learning technology, which is applied in the field of equipment fault diagnosis with adaptive learning rate, can solve the problem of distinguishing between two parameters of deep learning model weight and bias, reduce model iteration rate, increase model training time, etc. problem, to achieve the effect of weakening dependence, increasing training speed, and increasing training time

Pending Publication Date: 2020-04-28
TONGJI UNIV
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Problems solved by technology

[0006] Some of the above achievements provide some feasible methods, but these methods do not distinguish between the weight and bias parameters in the deep learning model, but use an adaptive learning rate strategy uniformly, which

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  • Equipment fault diagnosis method with adaptive learning rate based on deep learning
  • Equipment fault diagnosis method with adaptive learning rate based on deep learning
  • Equipment fault diagnosis method with adaptive learning rate based on deep learning

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[0041] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0042] The present invention provides a device fault diagnosis method with an adaptive learning rate based on deep learning. The method utilizes a trained deep learning model to process real-time collected data to be diagnosed to obtain a fault diagnosis result of the device. The deep learning The model uses an adaptive learning rate for iterative calculations. The adaptive learning rate specifically refers to using the current gradient value to adaptively adjust the learning rate of the current round based on the learning rate of the previous round, so as to describe the current state...

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Abstract

The invention relates to an equipment fault diagnosis method with an adaptive learning rate based on deep learning. According to the method, a trained fault diagnosis model based on deep learning is used for processing to-be-diagnosed data collected in real time, wherein the fault diagnosis model adopts an adaptive learning rate to carry out iterative computation. The adaptive learning rate is specifically as follows: on the basis of the last round of learning rate, the current gradient value is used for adaptively adjusting the current round of learning rate. Compared with the prior art, themethod has the advantages of short model training time, high classification accuracy and the like.

Description

technical field [0001] The present invention relates to a method for diagnosing equipment faults, in particular to a method for diagnosing equipment faults with an adaptive learning rate based on deep learning. Background technique [0002] The performance of the equipment will gradually decline with the increase of service time, and the effective diagnosis of the fault type of the equipment is of great significance for the timely maintenance of the equipment. Fault diagnosis refers to excavating the internal evolution law of equipment fault signals to realize the classification of equipment fault types, and to perform fault diagnosis to facilitate equipment maintenance and management. [0003] Traditional fault diagnosis methods include methods based on analytical models, methods based on signal processing, methods based on knowledge, and methods based on data. However, the first three methods are often limited by knowledge such as professional technology and expert experi...

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Application Information

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IPC IPC(8): G06N3/08G06N3/04G06K9/62
CPCG06N3/088G06N3/084G06N3/045G06F18/24G06F18/214
Inventor 乔非翟晓东
Owner TONGJI UNIV
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