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A Centrifugal Pump Fault Diagnosis Method Based on Complete Total Empirical Mode Decomposition and Random Forest

A random forest, fault diagnosis technology, applied in pump control, mechanical equipment, non-variable-capacity pumps, etc., can solve the problem of less application in the field of fault diagnosis, avoid modal aliasing and end-point effects, and improve classification accuracy , the effect of a small amount of calculation

Inactive Publication Date: 2018-03-02
BEIHANG UNIV
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Problems solved by technology

However, there are relatively few applications in the field of fault diagnosis.

Method used

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  • A Centrifugal Pump Fault Diagnosis Method Based on Complete Total Empirical Mode Decomposition and Random Forest
  • A Centrifugal Pump Fault Diagnosis Method Based on Complete Total Empirical Mode Decomposition and Random Forest
  • A Centrifugal Pump Fault Diagnosis Method Based on Complete Total Empirical Mode Decomposition and Random Forest

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Embodiment Construction

[0021] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0022] Such as figure 1 Shown, a kind of centrifugal pump fault diagnosis method flow process based on the random forest of CEEMD-SampEn of the present invention is introduced as follows:

[0023] The method is mainly divided into three parts: (1) use CEEMD to decompose the centrifugal pump vibration signal obtained by the sensor into a series of IMF classifications; (2) use the sample entropy of the IMF classification as the feature vector of the signal; (3) use CEEMD- The feature vector obtained from the sample entropy is used as the input of the random forest classifier for fault diagnosis. The method flow is as follows:

[0024] 1. Feature extraction process based on CEEMD-SampEn

[0025] 1.1 CEEMD method

[0026] CEEMD is an improved method based on the EMD algorithm. EMD is to use the local characteristics of the signal to adaptively...

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Abstract

The invention discloses a centrifugal pump fault diagnosis method based on complete ensemble empirical mode decomposition and random forest. The method comprises the following steps: (1) decomposing a centrifugal pump vibrating signal obtained by a sensor into a series of IMF categories by utilizing CEEMD; (2) taking the sample entropy of the IMF categories as characteristic vector of the signal; and (3) carrying out fault diagnosis by taking the characteristic vector obtained by the CEEMD-sample entropy as input of a random forest classifier. According to the invention, the CEEMD and the sample entropy are used for characteristic extraction of the centrifugal pump vibrating signal, on one hand, the phenomena of modal aliasing and end effect occurring in EMD decomposition are avoided as far as possible; and on the other hand, characteristic extraction is relatively convenient and simple, calculated amount is small, and the characteristic extraction is not sensitive to data length and noise, thus being high in applicability. According to the invention, the random forest classifier is used for fault mode identification of the centrifugal pump, thus avoiding the phenomenon of overfitting caused by the fact that conventional classifiers depend too much on training samples, and improving the classification accuracy as far as possible.

Description

technical field [0001] The invention relates to the technical field of centrifugal pump fault diagnosis, in particular to a centrifugal pump fault diagnosis method based on complete ensemble empirical mode decomposition (CEEMD) and random forest. Background technique [0002] As a key equipment, centrifugal pumps are widely used in various industrial fields. In rotating machinery, the equipment status information is hidden in the rotor vibration signal, which contains information about various abnormalities or failures of the equipment. Therefore, vibration analysis is widely used in the field of fault diagnosis of rotating machinery. Generally speaking, the analysis steps of diagnostic signals include the following aspects: (1) Acquisition of fault diagnostic signals (2) Extraction of fault features (3) Status confirmation and fault mode recognition. [0003] The Empirical Mode Decomposition (EMD) proposed by Huang can be adaptively used to deal with such non-stationary n...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): F04D15/00
CPCF04D15/0088F05D2260/80
Inventor 吕琛王洋秦维力周博赵万琳
Owner BEIHANG UNIV
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