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A fault diagnosis method of mechanical equipment based on fault mechanism and statistical model on-line learning

A technology of mechanical equipment and failure mechanism, which is applied in character and pattern recognition, calculation, computer parts and other directions, and can solve problems such as training is easy to overfit and the model is difficult to generalize

Active Publication Date: 2018-12-18
BEIJING UNIV OF CHEM TECH
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Problems solved by technology

The invention combines the equipment failure mechanism with real-time state monitoring data, effectively solving the problems of less failure case data in the statistical model training process, easy over-fitting in training, and difficult generalization of the model

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  • A fault diagnosis method of mechanical equipment based on fault mechanism and statistical model on-line learning
  • A fault diagnosis method of mechanical equipment based on fault mechanism and statistical model on-line learning
  • A fault diagnosis method of mechanical equipment based on fault mechanism and statistical model on-line learning

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Embodiment Construction

[0056] In order to illustrate the present invention more clearly, the present invention will be further described below in conjunction with specific examples.

[0057] The method proposed in this patent utilizes the working condition characteristics of the equipment and the collected real-time status monitoring data of the equipment, adopts the mode of combining parametric and non-parametric methods, and realizes the modeling of various operating states of the equipment through a generative statistical model It is expressed that this method not only effectively reduces the structural error between devices that is not considered in building a pure mechanism model based on the fault mechanism, but also solves the problem of insufficient sample data in building a pure mathematical model based on big data learning theory. The model generated by this method is only for a single specific device, and there is no need for model generalization. At the same time, the diagnostic model is ...

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Abstract

A fault diagnosis method of mechanical equipment based on fault mechanism and statistical model on-line learning belongs to the fault diagnosis field of mechanical equipment. The specific steps are asfollows: (1) constructing a health data generation model based on working condition characteristics and real-time state monitoring data; (2) constructing diagnosis model based on fault mechanism andhealth data generation model; (3) distinguishing the possible fault types of the equipment; (4) determining the failure probability of each type of equipment. This method combines equipment fault mechanism with real-time operation data, and constructs a fault diagnosis model for specific equipment, which effectively solves the problems of insufficient model learning fault case data and poor modelgeneralization ability in the existing methods.

Description

technical field [0001] This patent belongs to the field of mechanical equipment fault diagnosis and relates to a mechanical equipment fault diagnosis method based on online learning of equipment fault mechanism and generative statistical model. Background technique [0002] After long-term development of mechanical equipment fault diagnosis, some progress has been made in the identification of equipment status. It has realized the distinction between the healthy state and the fault state of mechanical equipment, but there are still difficulties in the identification of fault types. The mathematical essence of fault identification is the problem of pattern classification, and its development mainly includes two stages: the stage of traditional diagnosis technology based on signal processing technology and the stage of intelligent diagnosis technology with artificial intelligence technology such as expert system and neural network as the core. At present, traditional diagnosti...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/12G06F18/217G06F18/24155G06F18/214
Inventor 马波蔡伟东赵大力高金吉江志农
Owner BEIJING UNIV OF CHEM TECH
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