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Turntable Fault Diagnosis System

Author: LiChao
Tutor: ZengQingShuang
School: Harbin Institute of Technology
Course: Control Theory and Control Engineering
Keywords: Turntable Wavelet Transform T-S fuzzy neural network Subtractive clustering Anti-noise training
CLC: TH165.3
Type: Master's thesis
Year: 2009
Downloads: 117
Quote: 1
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Abstract


Turntable is a complex precision electromechanical test equipment , is to protect the inertial navigation system ground testing and simulation of key equipment. In order to ensure long-term stable and reliable turntable can run , its fault diagnosis technology is particularly important. In this paper, wavelet theory in signal - frequency domain analysis of the advantages and TS fuzzy neural network universal approximation capability for turntable fault and fault characteristic signals the end of the event the complex nonlinear relationship between the proposed based on subtractive clustering and TS fuzzy neural network fault diagnosis system turntable . Through experimental verification , and achieved good results. First, for a rotary transformer and inductosyn as angular components Turntable common fault , this chapter will be broken down into software failure, malfunction angle measuring system , communication system failures , the control circuit faults and turntable total of five sets of component failure , etc. faults were established each subsystem fault tree analysis on the basis of a failure the end event and the eventual establishment of a turntable system failure fault characteristic signal corresponding table . Secondly, the use of wavelet analysis theory , completed the singular values ​​of the signal and eliminate high frequency noise tests conducted simulation experiments ; and for power frequency signal interference , the use of Chebyshev best approximation of the theoretical design of the FIR notch filter. Finally, the system introduces the TS fuzzy neural network and its learning algorithm, and apply it to the turntable fault diagnosis . Expressed in numerical form a clear diagnosis expert experience less clustering algorithm then introduced the concept of the weight factor to obtain simple rules table ; followed by the use of anti-noise training method to train the network , so that within a certain amplitude to overcome noise ; Finally, the noisy test data and test data , respectively . Test results showed that: this method can effectively reduce the number of diagnostic rules to accurately achieve fault identification, fault-tolerant capability for noise , there is a strong engineering practicality.

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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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