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Research on Fault Diagnosis of Gear System Based on Blind Signal Separation

Author: WuYongJun
Tutor: ChenEnLi；ShenYongJun
School: Shijiazhuang Railway Institute
Course: Mechanical Design and Theory
Keywords: Gear system Fault Diagnosis Singular Value Decomposition Blind Signal Separation
CLC: TH165.3
Type: Master's thesis
Year: 2008
Downloads: 103
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Abstract


Gear system as essential in the modern industrial system mechanical transmission, the transmission of power, to pass motion accurate, smooth transmission, and many other advantages. With modern machinery and equipment toward the large-scale, high-efficiency, high-strength, automation and high performance direction, plays an increasingly important role as the gear to transmit motion and power. However, due to its own complex structure, poor working conditions and other reasons, gears and gear box are vulnerable to damage and malfunction. Therefore, the use of advanced technology for condition monitoring and fault diagnosis of gear and gear box can be achieved by corrective maintenance, periodic maintenance to a fundamental change in condition maintenance, reduce unnecessary losses, so as to create greater economic and social benefits , is of great significance. In this paper, the common faults of the gear box, the analysis of the typical failure mechanism and vibration characteristics, focusing on two fault feature extraction techniques, including improvement based on the reconstruction of the attractor trajectory matrix singular value decomposition technique based on improved singular value decomposition and Blind Signal separation combining new technology of fault diagnosis, and these two methods for the analysis of the actual fault signal, the results provide a new idea for the gear system fault diagnosis. The main contents and conclusions are as follows: (1) Improving the the existing singular value decomposition technique. The second chapter in the detailed study based on the reconstruction of the attractor trajectory matrix singular value decomposition on the basis of the basic principles of the technology, the introduction of the autocorrelation analysis to improve the original algorithm to make it more scientific and reasonable. Numerical simulation and experimental data analysis showed: improved singular value decomposition technique to extract the strong noise modulation fault information, the gear system fault diagnosis is of great significance. (2) study the basic principles and application of blind signal separation. Chapter III detailed derivation the JADE Act batch method, adaptive Infomax algorithm and FastICA fixed-point algorithms, and analyze their respective characteristics. The numerical simulation results show that: JADE method and FastICA law of for blind signal separation technology in multi-channel mixed-signal source separation is very effective, and this provides a new idea for the gear system fault diagnosis. (3) gearbox fault diagnosis experiment. Chapter IV under the existing experimental conditions, the design of the experimental program, the original gearbox fault bench improvements, set up a typical type of fault the fault diagnosis experiment. (4) based on singular value decomposition to improve the technology and blind signal separation combined fault diagnosis method. Chapter first use of the singular value decomposition technique on the measured signal noise reduction processing, recycling blind signal separation technology blind source separation of noise signal, a combination of singular value decomposition technology and blind signal separation technology, succeeded in isolating the measured the typical fault signal in the signal, the fault characteristics and experimental set the fault exactly. At the same time, the separation of the experimental data results show that: JADE Act FastICA algorithm can obtain good separation. Finally, Chapter VI of the research achievements were summarized and pointed out the direction worthy of further study.

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CLC: > Industrial Technology > Machinery and Instrument Industry > Machinery Manufacturing Technology > Flexible manufacturing systems and flexible manufacturing cell > Fault diagnosis and maintenance
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