scholarly journals An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors

2019 ◽  
Vol 9 (18) ◽  
pp. 3902 ◽  
Author(s):  
Dong Zhen ◽  
Zuolu Wang ◽  
Haiyang Li ◽  
Hao Zhang ◽  
Jie Yang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Carrier Frequency ◽  
Induction Motors ◽  
Window Size ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Frequency Resolution ◽  
Spectral Correlation ◽  
Broken Rotor Bar ◽  
Cyclic Frequency

Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.

High Resonance Component of Resonance-Based Sparse Decomposition Application in Extraction of Rolling Bearing Fault Information

2013 ◽  
Vol 753-755 ◽  
pp. 2290-2296 ◽  
Author(s):  
Wen Tao Huang ◽  
Yin Feng Liu ◽  
Pei Lu Niu ◽  
Wei Jie Wang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Frequency Resolution ◽  
Sparse Decomposition ◽  
High Resonance ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Information Frequency ◽  
Fault Information

In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise, resulting in the difficulties in analysis of early weak fault signal. This article introduces resonance-based signal sparse decomposition (RSSD) into rolling bearing fault diagnosis, and studies the fault information contained in high resonance component and low resonance component. This article compares the effect of the two resonance components to extract rolling bearing fault information in four aspects: the amount of fault information, frequency resolution of subbands, sensitivity to noise and immunity to autocorrelation processing. We find that the high resonance component has greater advantage in extraction of rolling bearing fault information, and it is able to indicate rolling bearing failure accurately.

scholarly journals Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network

2021 ◽  
Vol 3 (2) ◽  
pp. 99-113
Author(s):  
Karuppusamy P.
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Rotating Machinery ◽  
Research Report ◽  
Sufficient Information ◽  
Financial Loss ◽  
Discussion Section ◽  
Early Failures

In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.

scholarly journals Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

2017 ◽  
Vol 62 (4) ◽  
pp. 2413-2419 ◽  
Author(s):  
A. Glowacz ◽  
W. Glowacz ◽  
Z. Glowacz ◽  
J. Kozik ◽  
M. Gutten ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Induction Motors ◽  
Vibration Signal ◽  
Degradation Process ◽  
Conveyor Belt ◽  
Metallurgical Equipment ◽  
Financial Loss ◽  
Linear Discriminant ◽  
Three Phase

AbstractA degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies – Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

A velocity synchrosqueezing transform for fault diagnosis of planetary gearboxes under nonstationary conditions

2016 ◽  
Vol 231 (15) ◽  
pp. 2868-2884 ◽  
Author(s):  
Yunpeng Guan ◽  
Ming Liang ◽  
Dan-Sorin Necsulescu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Machinery Condition Monitoring ◽  
Frequency Representation ◽  
Nonstationary Vibration ◽  
Synchrosqueezing Transform ◽  
Fine Resolution ◽  
Nonstationary Conditions

Time–frequency analysis is widely used in the field of machinery condition monitoring and fault diagnosis under nonstationary conditions. Among the time–frequency methods synchrosqueezing transform outperforms others in providing fine-resolution time–frequency representation. However, it suffers from time–frequency smear when analysing nonstationary signals. To address this issue, this paper proposes a new synchrosqueezing-transform-based method which works by (1) mapping the raw nonstationary vibration signal into a corresponding stationary angle domain signal to meet the stationarity requirement of the synchrosqueezing transform, (2) performing the synchrosqueezing transform of the corresponding signal and (3) restoring the time–frequency representation of the raw signal from the synchrosqueezing transform result of the corresponding signal. As the synchrosqueezing transform is applied to the stationary corresponding signal, the time–frequency smear is eliminated in the synchrosqueezing transform result of the corresponding signal and the final signal time–frequency representation. As such the proposed method can generate a smear-free time–frequency representation with fine time–frequency resolution and thus provide more reliable diagnosis decisions. A fast implementation algorithm is also developed to simplify the implementation of the proposed method. The effectiveness of the proposed method is validated using both simulated and experimental vibration signals of planetary gearboxes.

The Application of Improved Cyclical Spectrum Density Method in Fault Diagnosis of Rolling Bearing

2013 ◽  
Vol 572 ◽  
pp. 401-404
Author(s):  
Min Li ◽  
Xiao Jing Wang ◽  
Jian Hong Yang
Keyword(s):  
Blast Furnace ◽  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Carrier Frequency ◽  
Rolling Bearing ◽  
Weight Coefficient ◽  
Density Method ◽  
Fault Type ◽  
Cyclic Frequency ◽  
Spectrum Density

The characteristic of cyclical impact is reflected on the signal of rolling bearing in fault condition. The carrier frequency is modulated by times of the failure frequencies. When the traditional cyclical spectrum density (CSD) method is used to analyze the signal, all the modulation frequencies will be demodulated in the cyclic frequency spectrum. In this case, it is difficult to recognize the fault type of the bearing. Therefore, a new cyclical spectrum density method based on the kurtosis energy (CSDK) is proposed. The kurtosis of every cyclic frequency’s slice is used as the weight coefficient of the cyclic frequency’s energy accumulation to extract fault feature effectively. The proposed method has greatly reduced times of the harmonic frequencies’ effect in traditional CSD method. The analysis of the signal gathered from the outer rolling bearing of blast furnace belt cylinder shows that the fault feature extracted by the new method is more clear and accurate than CSD method.

scholarly journals Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment

2019 ◽  
Vol 6 (2) ◽  
pp. d1-d8 ◽  
Author(s):  
S. Altaf ◽  
M. S. Mehmood ◽  
M. W. Soomro
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Industrial Applications ◽  
Manufacturing Companies ◽  
Signal Processing Technique ◽  
Broken Rotor Bar ◽  
Machine Fault Diagnosis ◽  
Machine Fault

Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.

scholarly journals Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network

2021 ◽  
Vol 3 (2) ◽  
pp. 99-113
Author(s):  
Karuppusamy P.
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Rotating Machinery ◽  
Research Report ◽  
Sufficient Information ◽  
Financial Loss ◽  
Discussion Section ◽  
Early Failures

In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.

The Application of Extension Algorithms in Induction Motor Mechanical Fault Diagnosis

2010 ◽  
Vol 426-427 ◽  
pp. 593-598
Author(s):  
Kuei Hsiang Chao
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Vibration Signal ◽  
Element Model ◽  
Measured Data ◽  
Extension Method ◽  
Fault Type ◽  
Correlation Degree ◽  
Diagnosis Method ◽  
Extension Algorithms

The main purpose of this paper is to apply the extension method based on the spectrum of the vibration signal for motor mechanical fault diagnosis. The measured data of mechanical fault in induction motors are adopted to carry out the spectrum analysis and then establish the extension element model. According to the distance and the correlation function value concept of extension theory, one can calculate the correlation degree of various fault type and then determine the most possible category of mechanical fault in induction motors. Finally, some simulation and experimental results are made to verify the effectiveness of the proposed motor mechanical fault diagnosis method.

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