An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current

The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time–frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator current signal and vibration signal are used for the detection of short circuit faults. Two different experimental data from a three-phase PMSM were processed and analyzed by this time–frequency method in LabVIEW. The feasibility of this approach is shown by the experimental test.

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Deep Learning Approach for Vibration Signals Applications

Sensors ◽

10.3390/s21113929 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3929

Author(s):

Han-Yun Chen ◽

Ching-Hung Lee

Keyword(s):

Surface Roughness ◽

Tool Wear ◽

Parameters Optimization ◽

Frequency Spectra ◽

Vibration Signals ◽

Bearing Faults ◽

Time Frequency ◽

And Performance ◽

The One ◽

Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

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Fuzzified time-frequency method for identification and localization of power system faults

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189769 ◽

2021 ◽

pp. 1-13

Author(s):

Pullabhatla Srikanth ◽

Chiranjib Koley

Keyword(s):

Power System ◽

High Accuracy ◽

Fuzzy Decision ◽

Frequency Method ◽

Time Frequency ◽

Novel Approach ◽

S Transform ◽

Different Types ◽

Power System Faults ◽

Frequency Magnitude

In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM.

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Generalized Roughness Bearing Faults Diagnosis Based on Induction Motor Stator Current

Radioengineering ◽

10.13164/re.2018.1166 ◽

2018 ◽

Vol 27 (4) ◽

pp. 1166-1173 ◽

Cited By ~ 2

Author(s):

I. Andrijauskas ◽

M. Vaitkunas ◽

R. Adaskevicius

Keyword(s):

Induction Motor ◽

Bearing Faults ◽

Stator Current ◽

Faults Diagnosis

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Design of Vibration Frequency Method with Fine-Tuned Factor for Fault Detection of Three Phase Induction Motor

Journal of Innovative Image Processing - December 2019 ◽

10.36548/jiip.2021.1.005 ◽

2021 ◽

Vol 3 (1) ◽

pp. 52-65

Author(s):

Thomas Amanuel ◽

Amanuel Ghirmay ◽

Huruy Ghebremeskel ◽

Robel Ghebrehiwet ◽

Weldekidan Bahlibi

Keyword(s):

Induction Motor ◽

Vibration Frequency ◽

Vibration Monitoring ◽

Slight Variation ◽

Frequency Method ◽

Monitoring Method ◽

Stator Current ◽

Research Article ◽

Proposed Model ◽

Fault Parameters

This research article focuses on industrial applications to demonstrate the characterization of current and vibration analysis to diagnose the induction motor drive problems. Generally, the induction motor faults are detected by monitoring the current and proposed fine-tuned vibration frequency method. The stator short circuit fault, broken rotor bar fault, air gap eccentricity, and bearing fault are the common faults that occur in an induction motor. The detection process of the proposed method is based on sidebands around the supply frequency in the stator current signal and vibration. Moreover, it is very challenging to diagnose the problem that occur due to the complex electromagnetic and mechanical characteristics of an induction motor with vibration measures. The design of an accurate model to measure vibration and stator current is analyzed in this research article. The proposed method is showing how efficiently the root cause of the problem can be diagnosed by using the combination of current and vibration monitoring method. The proposed model is developed for induction motor and its circuit environment in MATLAB is verified to perform an accurate detection and diagnosis of motor fault parameters. All stator faults are turned to turn fault; further, the rotor-broken bar and eccentricity are structured in each test. The output response (torque and stator current) is simulated by using a modified winding procedure (MWP) approach by tuning the winding geometrical parameter. The proposed model in MATLAB Simulink environment is highly symmetrical, which can easily detect the signal component in fault frequencies that occur due to a slight variation and improper motor installation. Finally, this research article compares the other existing methods with proposed method.

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IDENTIFICATION OF IMPACT FORCE ON THE GAS PIPE BASED ON ANALYSIS OF THE ACOUSTIC WAVE

International Journal of Modern Physics B ◽

10.1142/s0217979208046281 ◽

2008 ◽

Vol 22 (09n11) ◽

pp. 1039-1044 ◽

Cited By ~ 2

Author(s):

MIN-SOO KIM ◽

SANG-KWON LEE ◽

SUNG-JONG KIM

Keyword(s):

Acoustic Wave ◽

Cavity Mode ◽

Impact Force ◽

Correlation Technique ◽

Frequency Method ◽

Time Frequency ◽

Long Time ◽

Mode Characteristics ◽

The Impact ◽

The Waves

An acoustic wave signal measured on the gas pipe due to impact force is transfer to the far distance through the medium inside of duct. This signal is very complex since it includes the acoustic wave and solid wave. Acoustic wave is affected by the cavity mode inside of duct. The analysis of this acoustic wave gives information about the impact force. For the analysis of this signal, the correlation technique has been used for a long time. This method has a limitation for the application since the waves have dispersive and cavity mode characteristics for the flexible wave. In this paper, we present the time-frequency method for the identification of impact force and the location of impact on the gas pipe. The results give the useful information for the impact force and are applied to the analysis of leakage location of the gas pipe.

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EEG Signal Analysis using Empirical Mode Decomposition and Time-Frequency Method

10.1007/978-981-16-2709-5_13 ◽

2021 ◽

pp. 155-170

Author(s):

Subha D. Puthankattil ◽

Arun Rayakwar

Keyword(s):

Empirical Mode Decomposition ◽

Signal Analysis ◽

Eeg Signal ◽

Frequency Method ◽

Time Frequency ◽

Mode Decomposition ◽

Eeg Signal Analysis

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The application of semi-nonnegative matrix factorization for detection of incipient faults in bearings

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406219827332 ◽

2019 ◽

Vol 233 (13) ◽

pp. 4543-4555 ◽

Cited By ~ 1

Author(s):

Akhand Rai ◽

Sanjay H Upadhyay

Keyword(s):

Frequency Domain ◽

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Nonnegative Matrix ◽

Health Indicators ◽

Data Matrix ◽

Bearing Faults ◽

Time Frequency ◽

Factorization Algorithm ◽

Incipient Faults

Bearing faults are a major reason for the catastrophic breakdown of rotating machinery. Therefore, the early detection of bearing faults becomes a necessity to attain an uninterrupted and safe operation. This paper proposes a novel approach based on semi-nonnegative matrix factorization for detection of incipient faults in bearings. The semi-nonnegative matrix factorization algorithm creates a sparse, localized, part-based representation of the original data and assists to capture the fault information in bearing signals more effectively. Through semi-nonnegative matrix factorization, two bearing health indicators are derived to fulfill the desired purpose. In doing so, the paper tries to address two critical issues: (i) how to reduce the dimensionality of feature space (ii) how to obtain a definite range of the indicator between 0 and 1. Firstly, a set of time domain, frequency domain, and time–frequency domain features are extracted from the bearing vibration signals. Secondly, the feature dataset is utilized to train the semi-nonnegative matrix factorization algorithm which decomposes the training data matrix into two new matrices of lower ranks. Thirdly, the test feature vectors are projected onto these lower dimensional matrices to obtain two statistics called as square prediction error and Q2. Finally, the Bayesian inference approach is exploited to convert the two statistics into health indicators that have a fixed range between [0–1]. The application of the advocated technique on experimental bearing signals demonstrates that it can effectively predict the weak defects in bearings as well as performs better than the earlier methods like principal component analysis and locality preserving projections.

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TQWT-assisted statistical process control method for condition monitoring and fault diagnosis of bearings in high-speed rail

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x20958321 ◽

2020 ◽

pp. 1748006X2095832 ◽

Cited By ~ 1

Author(s):

Wei Fan ◽

Hongtao Xue ◽

Cai Yi ◽

Zhenying Xu

Keyword(s):

Fault Diagnosis ◽

Condition Monitoring ◽

High Speed ◽

Control Method ◽

Wavelet Coefficients ◽

Frequency Method ◽

Statistical Process ◽

High Speed Rail ◽

Distribution Method ◽

Time Frequency

Condition monitoring and fault diagnosis of bearings in high-speed rail have attracted considerable attention in recent years, however, it’s still a hard work due to harsh environments with high speeds and high loads. A statistical condition monitoring and fault diagnosis method based on tunable Q-factor wavelet transform (TQWT) is developed in this study. The core idea of this method is that the TQWT can extract oscillatory behaviors of bearing faults. The vibration data under the normal condition are first decomposed by the TQWT into different wavelet coefficients. Two health indicators are then formulated by the dominant wavelet coefficients and the remaining coefficients for condition monitoring. The upper control limits are established using the one-sided confidence limit of the indicators by using the non-parametric bootstrap scheme. The Shewhart control charts on multiscale wavelet coefficients are constructed for fault diagnosis. We demonstrate the effectiveness of the proposed method by monitoring and diagnosing single and multiple railway axle bearing defects. Furthermore, the comparison studies show that the proposed method outperforms a traditional time-frequency method, the Wigner-Ville distribution method.

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