A novel approach to fault detection and isolation based on wavelet analysis and neural network

2003 ◽  
Author(s):  
Zhihan Xu ◽  
Qing Zhao
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Wavelet Analysis ◽  
Fault Detection And Isolation ◽  
Novel Approach

LRE Fault Detection and Isolation Based on Fuzzy Direction Neural Network

2015 ◽  
Vol 727-728 ◽  
pp. 880-883
Author(s):  
Min Chao Huang ◽  
Bao Yu Xing
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Rocket Engine ◽  
Fault Detection And Isolation ◽  
Liquid Rocket Engine ◽  
Direction Vector ◽  
Liquid Rocket ◽  
Turbo Pump

A fuzzy directions neural network used for fault detection and isolation (FDI) of a liquid rocket engine (LRE) is presented in this paper. Neural network utilizes fuzzy sets as engine fault classes. Each fuzzy set is an aggregate of fuzzy direction bodies. A fuzzy direction body is described by a direction vector, an included angle and two radii. FDI simulation of the turbo-pump fed liquid rocket engine demonstrates the strong qualities of the fuzzy direction neural network.

Fault Detection of Induction Motors Using Fourier and Wavelet Analysis

2004 ◽  
Vol 8 (4) ◽  
pp. 431-436 ◽  
Author(s):  
Hyeon Bae ◽  
◽  
Youn-Tae Kim ◽  
Sungshin Kim ◽  
Sang-Hyuk Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Wavelet Analysis ◽  
Time Domain ◽  
Induction Motors ◽  
Fuzzy Inference ◽  
Classification Model ◽  
Diagnosis And Prognosis ◽  
Signal Features ◽  
The Time Domain

The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier Transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detecting signal features.

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