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.

A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements

2021 ◽  
Author(s):  
Yongzhi Qu ◽  
Gregory W. Vogl ◽  
Zechao Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Time Domain ◽  
Frequency Response Function ◽  
Frequency Component ◽  
Operating Conditions ◽  
Input Output ◽  
The Fourier Transform ◽  
The Time Domain

Abstract The frequency response function (FRF), defined as the ratio between the Fourier transform of the time-domain output and the Fourier transform of the time-domain input, is a common tool to analyze the relationships between inputs and outputs of a mechanical system. Learning the FRF for mechanical systems can facilitate system identification, condition-based health monitoring, and improve performance metrics, by providing an input-output model that describes the system dynamics. Existing FRF identification assumes there is a one-to-one mapping between each input frequency component and output frequency component. However, during dynamic operations, the FRF can present complex dependencies with frequency cross-correlations due to modulation effects, nonlinearities, and mechanical noise. Furthermore, existing FRFs assume linearity between input-output spectrums with varying mechanical loads, while in practice FRFs can depend on the operating conditions and show high nonlinearities. Outputs of existing neural networks are typically low-dimensional labels rather than real-time high-dimensional measurements. This paper proposes a vector regression method based on deep neural networks for the learning of runtime FRFs from measurement data under different operating conditions. More specifically, a neural network based on an encoder-decoder with a symmetric compression structure is proposed. The deep encoder-decoder network features simultaneous learning of the regression relationship between input and output embeddings, as well as a discriminative model for output spectrum classification under different operating conditions. The learning model is validated using experimental data from a high-pressure hydraulic test rig. The results show that the proposed model can learn the FRF between sensor measurements under different operating conditions with high accuracy and denoising capability. The learned FRF model provides an estimation for sensor measurements when a physical sensor is not feasible and can be used for operating condition recognition.

Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

2012 ◽  
Vol 3 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Manjeevan Seera ◽  
Chee Peng Lim ◽  
Dahaman Ishak
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Induction Motors ◽  
Fault Detection And Diagnosis ◽  
Broken Rotor Bars ◽  
Power Spectral ◽  
Motor Current Signature Analysis ◽  
Detection And Diagnosis ◽  
Current Signatures

In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

Review of Optimal Solutions to the Robustness Problem in Observer-Based Fault Detection

1993 ◽  
Vol 207 (2) ◽  
pp. 105-112 ◽  
Author(s):  
P M Frank ◽  
B Koppen
Keyword(s):  
Fault Detection ◽  
General Solution ◽  
Frequency Domain ◽  
Time Domain ◽  
Fault Detection And Isolation ◽  
Unknown Input ◽  
Optimal Solutions ◽  
Unified Approach ◽  
Ultimate Objective ◽  
The Time Domain

The paper presents a unified approach and general solution of the robustness problem in fault detection and isolation concepts. The ultimate objective is the design of an unknown input fault-detection observer providing a perfect decoupling between unknown inputs and faults. If this is not possible because certain prerequisites are not fulfilled, two optimal compromises in the time domain and in the frequency domain are described. The basic definitions concerning robustness and unknown input fault detectability are given, and the design techniques for the proposed approaches are outlined. The cross-connections to other methods are discussed and a practical example is given.

Financial Index Prediction Based on Wavelet Analysis and SVM

2013 ◽  
Vol 303-306 ◽  
pp. 1114-1118
Author(s):  
Xian Tan
Keyword(s):  
Wavelet Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Financial Time Series ◽  
Support Vector ◽  
Frequency Domain Method ◽  
Signal Characteristics ◽  
Vector Machines ◽  
Financial Index ◽  
The Time Domain

The analysis of the time sequence can be two ways in the time domain and frequency domain. But many financial time series exhibit strong non-stationary and long memory, which makes many traditional individually focused on the research and analysis of the time domain or frequency domain method is no longer applicable. In this paper, wavelet analysis and support vector machines for use in the time domain and frequency domain have the ability to characterize the local signal characteristics, location and mutation of the singular points and irregular mutation analysis, these mutations detected the degree of significance.

scholarly journals Fault Detection and Isolation in Electrical Machines using Deep Neural Networks

2019 ◽  
Vol 69 (3) ◽  
pp. 249-253
Author(s):  
M. Sai ◽  
Parth Upadhyay ◽  
Babji Srinivasan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Health Monitoring ◽  
Time Domain ◽  
Deep Neural Networks ◽  
Experimental Studies ◽  
Electrical Machines ◽  
Fault Detection And Isolation ◽  
Challenging Problem ◽  
Original Time

Condition and health monitoring of electrical machines during dynamic loading is a common, yet challenging problem in main battle tanks. Existing methods address this issue by extracting various features which are subsequently used in a classifier to isolate faults. However, this approach relies on the feature set being extracted and therefore most of the time does not provide expected accuracy in identification of faults. In this work, we have used convolution neural network that utilises the original time domain measurements for fault detection and isolation (FDI). Results from experimental studies indicate that the proposed approach can perform FDI with more than 95\% accuracy using commonly available current measurements.

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