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.

scholarly journals A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization

2020 ◽  
Vol 10 (24) ◽  
pp. 8800
Author(s):  
Bach Phi Duong ◽  
Jae Young Kim ◽  
Inkyu Jeong ◽  
Kichang Im ◽  
Cheol Hong Kim ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Experimental Tests ◽  
Time Frequency ◽  
Emission Signal ◽  
Bearing Fault Diagnosis ◽  
Bearing Defects ◽  
Novel Strategy ◽  
Pattern Visualization ◽  
Wavelet Image

A new method is established to construct the 2-D fault diagnosis representation of multiple bearing defects from 1-D acoustic emission signals. This technique starts by applying envelope analysis to extract the envelope signal. A novel strategy is propounded for the deployment of the continuous wavelet transform with damage frequency band information to generate the defect signature wavelet image (DSWI), which describes the acoustic emission signal in time-frequency-domain, reduces the nonstationary effect in the signal, shows discriminate pattern visualization for different types of faults, and associates with the defect signature of bearing faults. Using the resultant DSWI, the deep convolution neural network (DCNN) architecture is designed to identify the fault in the bearing. To evaluate the proposed algorithm, the performance of this technique is scrutinized by a series of experimental tests acquired from a self-designed testbed and corresponding to different bearing conditions. The performance from the experimental dataset demonstrates that the suggested methodology outperforms conventional approaches in terms of classification accuracy. The result of combining the DCNN with DSWI input yields an accuracy of 98.79% for classifying multiple bearing defects.

scholarly journals Fault Diagnosis of Induction Machines in Transient Regime Using Current Sensors with an Optimized Slepian Window

2017 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Baño ◽  
Manuel Pineda-Sanchez
Keyword(s):  
Fault Diagnosis ◽  
Induction Machines ◽  
Transient Regime ◽  
Sequence Length ◽  
Time Frequency ◽  
Stator Current ◽  
Start Up ◽  
Analysis Window ◽  
Computational Resources ◽  
Short Time

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

scholarly journals Fault Diagnosis of Induction Machines in Transient Regime Using Current Sensors with an Optimized Slepian Window

2017 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bañó ◽  
Manuel Pineda-Sanchez
Keyword(s):  
Fault Diagnosis ◽  
Induction Machines ◽  
Transient Regime ◽  
Sequence Length ◽  
Time Frequency ◽  
Stator Current ◽  
Start Up ◽  
Analysis Window ◽  
Computational Resources ◽  
Short Time

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

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.

Fault diagnosis of flexible production line machining center based on PCA and ABC-LVQ

2020 ◽  
pp. 095440542097051
Author(s):  
Yuanmeng Xia ◽  
Wei Wang ◽  
Zhiyong Song ◽  
Zhaoling Xie ◽  
Xuezhen Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Production Line ◽  
General Scheme ◽  
Principal Component ◽  
Flexible Production ◽  
Time Frequency ◽  
Bee Colony ◽  
Machining Center

A fault diagnosis method of flexible production line machining center based on principal component analysis (PCA) and artificial bee colony based learning vector quantization (ABC-LVQ) neural network is proposed to target the issue of key vulnerable parts of the flexible production line machining center. Along with that, a general scheme of fault diagnosis is designed according to the characteristics of flexible production line which includes multiple machining centers. Firstly, the acceleration sensor is used to collect the vibration signals of the key and critical components of the flexible production line machining center, to analyze and pick up the multi-information characteristics, the time domain, the frequency domain and the time-frequency domain of the signals. Then, in order to reduce the dimension of the eigenvector, PCA is adopted to realize such reduction. Finally, aiming at the problem that LVQ neural network is sensitive to the initial value weights, an ABC algorithm is proposed to quickly obtain excellent initial weights vector of LVQ neural network, which can accurately classify all kinds of faults. The experimental results show that the scheme can diagnose and monitor several key components of horizontal machining center of flexible production line in a quick and accurate manner.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

scholarly journals Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4514
Author(s):  
Vincent Becker ◽  
Thilo Schwamm ◽  
Sven Urschel ◽  
Jose Alfonso Antonino-Daviu
Keyword(s):  
Condition Monitoring ◽  
False Alarms ◽  
Signature Analysis ◽  
Variable Speed ◽  
Monitoring Methods ◽  
Time Frequency ◽  
Motor Current ◽  
Load Point ◽  
Bearing Defects ◽  
Current Signature

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.

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