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Equipment health state early warning method and system

A technology of health status and equipment, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of inaccurate results, insufficient consideration of fault diagnosis and prediction of industrial equipment, etc. The effect of stabilizing factors and improving rationality

Active Publication Date: 2019-05-21
ZHENGZHOU UNIV +2
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method and system for early warning of equipment health status, which is used to solve the problem of insufficient consideration and inaccurate results in the diagnosis and prediction of industrial equipment failures in the prior art

Method used

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  • Equipment health state early warning method and system
  • Equipment health state early warning method and system
  • Equipment health state early warning method and system

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Embodiment Construction

[0028] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0029] Such as figure 1 As shown in the flow chart of the HD-SVR big data equipment health warning method, the method of the present invention includes the following four steps:

[0030] Model building. The industrial data generated during the healthy operation of the equipment under various working conditions constitutes the health state vector set. Among them, the column vector reflects the health status at different times. For example, the column vector of the status of the device at j time is:

[0031]

[0032] in Respectively represent the value of a certain signal or index of the equipment at time j, for example, there are n items including temperature, flow, power, current, vibration of different measuring points, as well as equipment startup and shutdown, and equipment maintenance quality. The m generated during the operation of the equipment ...

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Abstract

The invention relates to an equipment health state early warning method and an equipment health state early warning system, and particularly discloses a big data equipment early warning method based on a support vector regression (SVR) technology based on hyper dimension (HD). The method includes the steps of establishing a health set of the equipment according to the historical health data of equipment operation; and collecting operation data of the current state of the equipment as an observation vector, obtaining a prediction vector of the corresponding state according to the health set, and judging the health degree of the operation state of the current equipment through a residual vector, i.e., the difference between the observation vector and the prediction vector, so as to realize real-time high-dimensional data monitoring and abnormal working condition early warning. And meanwhile, a support vector regression machine is used for carrying out fault prediction on equipment.

Description

technical field [0001] The invention relates to an equipment health state early warning method and system thereof, belonging to the field of industrial equipment safety and fault prediction and diagnosis. Background technique [0002] The signals that reflect the health status of equipment are often multi-dimensional, and the signals that reflect the health of equipment are often different under different loads and working conditions. This creates a certain complexity in the identification, prediction and diagnosis of equipment health. The current equipment fault diagnosis methods are mostly one-dimensional, and the quantitative relationship between each dimension is not considered sufficiently, and the different health indicators of the equipment under different loads and working conditions are not considered. [0003] In addition, when considering each detection signal of the equipment independently, the equipment is often healthy, but when these signals are considered co...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 雷文平陈磊陈宏李凌均王丽雅韩捷吴小龙李康乐王凯付晗郝旺身
Owner ZHENGZHOU UNIV
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