Self-tuning fault diagnosis of MEMS

Mechatronics ◽  
2013 ◽  
Vol 23 (8) ◽  
pp. 1094-1099 ◽  
Author(s):  
Afshin Izadian
Keyword(s):  
Fault Diagnosis ◽  
Self Tuning

A Method of Fault Signature Extraction for Improved Diagnosis

1991 ◽  
Vol 113 (4) ◽  
pp. 634-638 ◽  
Author(s):  
Hsinyung Chin ◽  
Kourosh Danai
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Pattern Classification ◽  
False Alarms ◽  
Binary Vectors ◽  
Signature Extraction ◽  
Sensory Data ◽  
Tuning Method ◽  
Self Tuning ◽  
Efficient Extraction

Efficient extraction of fault signatures from sensory data is a major concern in fault diagnosis. This paper introduces a self-tuning method of fault signature extraction that enhances fault detection, minimizes false alarms, improves diagnosability, and reduces fault signature variability. The proposed method uses a Flagging Unit to convert the processed measurements to binary vectors, and utilizes nonparametric pattern classification techniques to estimate the fault signatures. The performance of the Flagging Unit, which relies on its adaptation algorithms to optimize its performance based upon a sample batch of measurement-fault vectors, is demonstrated in simulation.

Hybrid state-space self-tuning control using dual-rate sampling

1991 ◽  
Vol 138 (1) ◽  
pp. 50 ◽  
Author(s):  
Leang S. Shieh ◽  
Xiao M. Zhao ◽  
John W. Sunkel
Keyword(s):  
State Space ◽  
Hybrid State ◽  
Self Tuning

Self-tuning control for systems employing feedforward

1981 ◽  
Vol 128 (6) ◽  
pp. 283 ◽  
Author(s):  
A.Y. Allidina ◽  
F.M. Hughes ◽  
F.M. Tye
Keyword(s):  
Self Tuning

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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