scholarly journals Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Paulo Antonio Delgado-Arredondo ◽  
Arturo Garcia-Perez ◽  
Daniel Morinigo-Sotelo ◽  
Roque Alfredo Osornio-Rios ◽  
Juan Gabriel Avina-Cervantes ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Comparative Study ◽  
Vibration Analysis ◽  
Induction Motors ◽  
Transient Vibration ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Bearing Defects ◽  
Critical Components

Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

Identification of Fault Signals in Rotating Machinery Using Higher Order Time Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Fault Diagnosis ◽  
Wigner Distribution ◽  
Higher Order ◽  
Rotating Machinery ◽  
Data Sets ◽  
Higher Order Statistics ◽  
Vibration Signals ◽  
Time Frequency ◽  
Increasing Trend ◽  
Monitoring Application

Abstract Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has superior detection performance to second order Wigner distribution for typical impulsive signals found in a condition monitoring application. These methods are also applied to data sets measured within a car engine and industrial gearbox.

scholarly journals Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3398 ◽  
Author(s):  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bano ◽  
Jordi Burriel-Valencia ◽  
Martin Riera-Guasp
Keyword(s):  
Fault Diagnosis ◽  
Single Point ◽  
General Scheme ◽  
Induction Machine ◽  
Induction Machines ◽  
Transient Conditions ◽  
Time Frequency ◽  
Bearing Defects ◽  
Motor Current Signature Analysis ◽  
The Hilbert Transform

Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms—as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform—which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.

Time-Frequency Analysis of Transient Vibrations to Improve Turbo-Compressor Start-Up

2008 ◽  
Author(s):  
Juan C. Jauregui ◽  
Oscar Gonzalez ◽  
Eduardo Rubio
Keyword(s):  
Frequency Analysis ◽  
Fourier Transforms ◽  
Transient Vibration ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Start Up ◽  
Rotational Systems ◽  
Turbo Compressor ◽  
Different Sources

Diagnosis of turbo-compressors during start-up is a particularly challenging task. One of the reason is the reduced set of instruments that monitor this procedure. It is cumbersome to adjust lubrication and steam valves while controlling the speed and dynamic stability. In order to get the turbo-compressor out of a high vibration zone, it is important to be able to predict instabilities associated to the start-up process. Thus, it is necessary to have a measurement system with the ability of fault detection, especially at early stages of fault appearance. In this way, the start-up time can be significantly reduced. Although recent developed diagnosis methods use information from different sources and measurements, data structures are not designed to carry predictive information related to the turbo-compressor health. Therefore, it is important to extract early warning signals related to instability conditions. Vibration signals during machine start-up are non-stationary in nature, and conventional techniques, such as Fourier transforms and time series analysis, have difficulties to extract the full features of the vibrations signature. In this paper, the features of start-up vibrations in rotational systems like those found in turbo compressors are investigated by time-frequency analysis, and appropriate analysis of the transient vibration during compressor start-up is presented.

Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ravikumar KN ◽  
Hemantha Kumar ◽  
Kumar GN ◽  
Gangadharan KV
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Internal Combustion ◽  
Frequency Characteristics ◽  
Ic Engine ◽  
Vibration Signals ◽  
Content Type ◽  
Time Frequency ◽  
Gear Fault

PurposeThe purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.Design/methodology/approachVibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.FindingsThe obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.Practical implicationsThe proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.Originality/valueIn this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

A NewFault Detection and Diagnosis Method Based on Wigner-Ville Spectrum Entropy for the Rolling Bearing

2012 ◽  
Vol 197 ◽  
pp. 346-350 ◽  
Author(s):  
Ping Xie ◽  
Yu Xin Yang ◽  
Guo Qian Jiang ◽  
Yi Hao Du ◽  
Xiao Li Li
Keyword(s):  
Fault Detection ◽  
Rolling Bearing ◽  
Work Conditions ◽  
Fault Detection And Diagnosis ◽  
Vibration Signals ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
Detection And Diagnosis ◽  
Critical Components

The rolling bearings are one of the most critical components in rotary machinery. To prevent unexpected bearing failure, it is crucial to develop the effective fault detection and diagnosis techniques to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new fault detection and diagnosis method based on Wigner-Ville spectrum entropy (WVSE) is proposed. First, the local mean decomposition (LMD) and the Wigner-Ville distribution (WVD) are combined to develop a new feature extraction approach to extract the fault features in time-frequency domain of the bearing vibration signals. Second, the concept of the Shannon entropy is integrated into the WVD to define the Wigner-Ville spectrum entropy to quantify the energy variation in time-frequency distribution under different work conditions. The research results from the bearing vibration signals demonstrate that the proposed method based on WVSE can identify different fault patterns more accurately and effectively comparing with other methods based on singular spectrum entropy (SSE) or power spectrum entropy (PSE).

scholarly journals Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Shuang Pan ◽  
Tian Han ◽  
Andy C. C. Tan ◽  
Tian Ran Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Vibration Signals ◽  
Vibration Data ◽  
Motor Vibration

An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.

Improved Blade Fault Diagnosis Using Discrete Blade Passing Energy Packet and Rotor Dynamics Wavelet Analysis

2010 ◽  
Author(s):  
Meng Hee Lim ◽  
M. Salman Leong
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Vibration Analysis ◽  
Rotor System ◽  
Field Conditions ◽  
Spectra Analysis ◽  
Vibration Spectra ◽  
Time Frequency ◽  
Blade Tip ◽  
Rotor Dynamic

Blade fault represents one of the most frequent causes of gas turbine failures. Although various measurement methods (i.e. pressure, strain gauges, and blade tip measurements) have been found to be effective in diagnosing blade faults, it is often difficult to deploy these methods under field conditions due to the requirement of mounting sensors in the interior of a running gas turbine. Vibration spectra analysis is inevitably still represents the most widely used method for blade fault diagnosis under field conditions. However, this method is known to be only effective in detecting severe blade fault conditions (i.e. terminal rubbing); whilst, minor and transient blade faults (i.e. geometry alterations, reduction in blade tip clearance, and Foreign Object Damage (FOD) event) are often left undetected. This makes vibration spectra analysis an unreliable tool for total blade fault diagnosis in the field. This study was thus conducted to investigate methods that can improve the sensitivity and reliability of vibration analysis for blade faults diagnosis. Two novel vibration analysis methods were formulated, namely the Rotor Dynamic Wavelet Map (RDWM) and Blade Passing Energy Packet (BPEP). Experimental results showed that the time-frequency display of RDWM could provide a clearer picture of the rotor dynamic characteristics of a rotor system compared to vibration spectra. RDWM also provides a better visualization of the blade condition in the rotor and enables discrimination of various blade fault conditions (i.e. creep rub and eccentricity rub). Meanwhile, the BPEP method which breaks the overall Blade Passing Frequency (BPF) component into instantaneous and discrete energy packets of running blades in the rotor system, enables a more sensitive detection of rotor eccentricity conditions and provides early warning for impending blade rubbing which is often undetectable in the vibration spectra.

Sign in / Sign up

Export Citation Format

Share Document