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Hydraulic pump fault diagnosis method based on local mean conversion and Softmax

A local mean value, fault diagnosis technology, applied in pump testing, liquid variable capacity machinery, machine/engine, etc., can solve problems such as inapplicability of multiple logistic models, achieve high speed, improve efficiency, and run fast Effect

Inactive Publication Date: 2015-08-12
北京恒兴易康科技有限公司
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Problems solved by technology

Although multiple binary logistic models can be established to diagnose faults, since hydraulic pump faults belong to mutually exclusive categories, multiple logistic models will not be applicable in this case

Method used

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  • Hydraulic pump fault diagnosis method based on local mean conversion and Softmax
  • Hydraulic pump fault diagnosis method based on local mean conversion and Softmax
  • Hydraulic pump fault diagnosis method based on local mean conversion and Softmax

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Embodiment Construction

[0019] The present invention can acquire the real-time state of the hydraulic pump by analyzing the vibration signal collected from the hydraulic pump. Firstly, the vibration signal is decomposed into several PF (Product function PF) components by local mean decomposition (LMD). Then analyze the PF component containing fault information, and extract characteristic parameters such as energy and corresponding time-domain statistics. Afterwards, multidimensional scaling analysis (MDS) is used for feature reduction. After obtaining the reduced features, the trained logistic model is used for health assessment of hydraulic pumps. If a fault is detected during the health assessment of the hydraulic pump, the trained Softmax regression model will diagnose the possible fault modes. The analysis of the test results shows that for the fault classifier based on the Softmax regression model which is more effective for mutually exclusive fault categories, the application of the Softmax f...

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Abstract

The invention relates to a hydraulic pump fault diagnosis method based on local mean conversion and Softmax. Through analyzing vibration signals collected from a hydraulic pump, the real-time state of the hydraulic pump can be obtained. According to the method, firstly, LMD (local mean decomposition) is used for decomposing the vibration signals into a plurality of PF (product function) components; then, the PF components including fault information are analyzed; feature parameters such as energy and corresponding time domain statistics are extracted; then, MDS (multi-dimension scaling) is used for carrying out feature reduction; after the reduced features are obtained, a trained logistic model is used for carrying out health evaluation on the hydraulic pump; in the health evaluation process of the hydraulic pump, if the fault occurrence is detected, a trained Softmax regression model can diagnose a possible fault mode. The hydraulic pump fault diagnosis method has the advantages that the health state of the hydraulic pump can be effectively evaluated, and the fault diagnosis can be carried out.

Description

technical field [0001] The invention relates to a hydraulic pump fault diagnosis method based on local mean transformation and Softmax (Softmax Regression), belonging to the technical field of fault diagnosis. Background technique [0002] The hydraulic pump is one of the important components of the hydraulic system. Its performance has an important impact on the reliability of the entire hydraulic system operation. The health monitoring and fault diagnosis methods of the primary hydraulic pump are of great significance in industrial applications. Once the hydraulic pump fails, the vibration and noise will increase, which will reduce the work efficiency; if it is serious, the hydraulic pump will not be able to work, and even cause serious accidents. In the fault diagnosis of hydraulic pumps, the selection and extraction of feature information is very critical. Most of the fault diagnosis signals of hydraulic pumps are non-stationary signals, so a feature extraction method s...

Claims

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Application Information

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IPC IPC(8): F04B51/00
Inventor 吕琛丁宇马剑田野王洋王亚杰
Owner 北京恒兴易康科技有限公司
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