Selection of test nodes for analog fault diagnosis in dictionary approach

2000 ◽  
Vol 49 (6) ◽  
pp. 1289-1297 ◽  
Author(s):  
V.C. Prasad ◽  
N.S.C. Babu
Keyword(s):  
Fault Diagnosis ◽  
Analog Fault Diagnosis ◽  
Selection Of

Symbolic Techniques for the Selection of Test Frequencies in Analog Fault Diagnosis

2004 ◽  
Vol 40 (3) ◽  
pp. 205-213 ◽  
Author(s):  
F. Grasso ◽  
A. Luchetta ◽  
S. Manetti ◽  
M.C. Piccirilli
Keyword(s):  
Fault Diagnosis ◽  
Analog Fault Diagnosis ◽  
Selection Of

A Method for the Automatic Selection of Test Frequencies in Analog Fault Diagnosis

2007 ◽  
Vol 56 (6) ◽  
pp. 2322-2329 ◽  
Author(s):  
Francesco Grasso ◽  
Antonio Luchetta ◽  
Stefano Manetti ◽  
Maria Cristina Piccirilli
Keyword(s):  
Fault Diagnosis ◽  
Analog Fault Diagnosis ◽  
Automatic Selection ◽  
Selection Of

scholarly journals Vibration-Based Fault Diagnosis of Commutator Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Adam Glowacz ◽  
Witold Glowacz
Keyword(s):  
Fault Diagnosis ◽  
Extraction Methods ◽  
Total Efficiency ◽  
Practical Application ◽  
Backpropagation Neural Network ◽  
Vibration Signals ◽  
Linear Discriminant ◽  
Recognition Efficiency ◽  
Signal Processing Methods ◽  
Selection Of

This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries.

scholarly journals Fault Diagnosis of Transmission Line using Feed Forward Neural Network

2019 ◽  
Vol 8 (9S2) ◽  
pp. 263-268
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transmission Lines ◽  
Field Work ◽  
Neural Nets ◽  
Feed Forward Neural Network ◽  
Training Time ◽  
Optimum Result ◽  
Human Effort ◽  
Selection Of

The implementation of neural network for the fault diagnosis is to improve the dependability of the proposed scheme by providing a more accurate, faster diagnosis relaying scheme as compared with the conventional relaying schemes. It is important to improve the relaying schemes regarding the shortcoming of the system and increase the dependability of the system by using the proposed relaying scheme. It also provide more accurate, faster relaying scheme. It also gives selective schemes as compared to conventional system. The techniques for survey employed some methods for the collection of data which involved a literature review of journals, from review on books, newspaper, magazines as well as field work, additional data was collected from researchers who are working in this field. To achieve optimum result we have to improve following things: (i) Training time, (ii) Selection of training vector, (iii) Upgrading of trained neural nets and integration of technologies. AI with its promise of adaptive training and generalization deserves scope. As a result we obtain a system which is more reliable, more accurate, and faster, has more dependability as well as it will selective according to the proposed relaying scheme as compare to the conventional relaying scheme. This system helps us to reduce the shortcoming like major faults which we faced in the complex system of transmission lines which will helps in reducing human effort, saves cost for maintaining the transmission system.

scholarly journals Particle Swarm Optimization Based Adaptive Kalman Filters for Fault Diagnosis of Reaction Wheels

2021 ◽  
Author(s):  
Afshin Rahimi
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Unscented Kalman Filter ◽  
Particle Swarm ◽  
Filter Parameter ◽  
Filter Performance ◽  
Filter Tuning ◽  
Selection Of

There has been an increasing interest in fault diagnosis in recent years, as a result of the growing demand for higher performance, efficiency, reliability and safety in control systems. A faulty sensor or actuator may cause process performance degradation, process shut down, or a fatal accident. Quick fault detection and isolation can help avoid abnormal event progression and minimize the quality and productivity offsets. In space systems specifically, space and power are limited in the satellites, which means that hardware redundancy is not very practical. If actuator faults occur, analytical redundancy techniques should be employed to determine if, where, and how the fault(s) occurred. To do so, different approaches have been developed and studied and one of the wellknown approaches in the literature is using the Kalman Filter as an observer for the purpose of parameter estimation and fault detection. The gains for the filter should be selected and the selection of the process and measurement noise statistics, commonly referred to as “filter tuning,” is a major implementation issue for the Kalman filter. This process can have a significant impact on the filter performance. In practice, Kalman filter tuning is often an ad-hoc process involving a considerable amount of time for trial and error to obtain a filter with desirable –qualitative or quantitative- performance characteristics. This thesis focuses on presenting an algorithm for automation of the selection of the gains using an evolutionary swarm intelligence based optimization algorithm (Particle Swarm) to minimize the residuals of the estimated parameters. The methodology can be applied to any filter or controller but in this thesis, an Adaptive Unscented Kalman Filter parameter estimation applied to a reaction wheel unit is used for the purpose of performance evaluation of the proposed methodology.

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