Method and system for predicting residual service life of industrial equipment based on attribute self-adaption
An industrial equipment, self-adaptive technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve problems such as reduced algorithm performance, lack of research on feature extraction, etc., to achieve the effect of reliable production system models
Pending Publication Date: 2022-07-26
INST OF INFORMATION ENG CAS
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However, these methods have two problems: First, these methods lack research on feature extraction. They either simply filter features based on experience or roughly send all attributes into the model, which will reduce the performance of the algorithm; on the other hand, each Attributes contribute differently to the final prediction results, so a mechanism that can automatically adjust attribute weights is needed
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[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.
[0046] This embodiment proposes a method for predicting the remaining service life of industrial equipment based on attribute adaptation. The input of the method is a sequence whose time window size is τ Each moment contains 28 attributes, which are the A-phase voltage U A , B-phase voltage U B , C-phase voltage U C , AB line voltage U AB , BC line voltage U BC , CA line voltage U CA , A-phase current I A , Phase B current I B , C-phase current I C , A-phase active power P A , B-phase active power P B , C-phase active power P C , total active power P, A-phase ...
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The invention discloses a method and a system for predicting the residual service life of industrial equipment based on attribute self-adaption, and belongs to the field of fault diagnosis of a digital and intelligent manufacturing system.The method comprises the following steps: firstly, collecting power consumption data of the industrial equipment through a sensor; then redundant attributes are removed according to three-phase circuit knowledge; distributing higher weights for the important attributes through an attribute adaptive network; and finally, the weighted time sequence data are sent to the long short-term memory regression network for processing, and the remaining service life is predicted through the full-connection network.
Description
technical field [0001] The invention belongs to the field of fault diagnosis of digital and intelligent manufacturing systems, and in particular relates to a method and system for predicting the remaining useful life (RUL) of industrial equipment based on attribute self-adaptation. Background technique [0002] Predictive maintenance (PdM) is an equipment maintenance strategy driven by industrial big data. By predicting the health status of production equipment, bottleneck equipment can be maintained in advance before failure occurs, extending the service life of equipment. Therefore, it is widely used in the fields of drug production, equipment manufacturing and industrial control. Common predictive maintenance methods are mainly divided into statistical-based predictive maintenance and artificial intelligence-based predictive maintenance. [0003] According to the acquisition method of Condition Monitoring Data (CMD), the predictive maintenance method based on statistics ...
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Login to View More IPC IPC(8): G01R31/00G06N3/04
CPCG01R31/003G06N3/044
Inventor 操晓春李京知蒋彧琛代朋纹
Owner INST OF INFORMATION ENG CAS