Motor Fault On-Line Diagnosis Based on Innovation Energy Detection

Jian Xiong Long

doi:10.4028/www.scientific.net/AMM.66-68.577

Paper Titles

Study on the Electromagnetic Environment of the Inner and Outer Fuselage and the Hazard of the Environment to Electro-Explosive Systems
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Modeling Traffic Volume Based on Highway Toll Database Using GM (1,1)
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A Simulation Study on the End Milling Operation with Multiple Process Steps of Aeronautical Frame Monolithic Components
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Test Research and Parameter Analysis of Grain Drying Model
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Motor Fault On-Line Diagnosis Based on Innovation Energy Detection
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Research of MH-Ni Battery State of Charge Estimation Based on Grey System Neural Network Model
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Optimum Layout of Partially Covered Sandwich Beam with Constrained Layer Damping
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Probabilistic Study on Corrosion Damage Distribution and Fatigue Lives of LY12CZ Aluminum Alloy Structure
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The Comparison of Oil Quality from Different Processes
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 66-68Motor Fault On-Line Diagnosis Based on Innovation...

Motor Fault On-Line Diagnosis Based on Innovation Energy Detection

Abstract:

For the motor current noise signal, vibration signal, the phase current signal, a motor dynamic system model is established. A motor energy detection equation is introduced and constituted by an innovation series of these signal tested in the dynamic system, then the main factor analysis of the motor abnormal elements is presented by orthogonal decomposition, and the main factor distribution chart is described by decomposing the main failure elements to tow-dimensional observation vector. The motor fault can be detected on-line by using the main factor analysis, and then the failure factors can be identified with fault Mode.

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Periodical:

Applied Mechanics and Materials (Volumes 66-68)

Pages:

577-582

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.66-68.577

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Online since:

July 2011

Authors:

Jian Xiong Long

Keywords:

Dynamic System Model, Fault Factor Identification, Innovation Series, Motor Fault Detection

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References

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