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 Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

2020 ◽  
Vol 10 (20) ◽  
pp. 7068
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Extraction Methods ◽  
High Accuracy ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

2021 ◽  
pp. 147592172110360
Author(s):  
Dongming Hou ◽  
Hongyuan Qi ◽  
Honglin Luo ◽  
Cuiping Wang ◽  
Jiangtian Yang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Signal Attenuation ◽  
Transmission Path ◽  
Bearing Fault Diagnosis ◽  
Operation Conditions ◽  
Fault Features ◽  
High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

Three-Point Bending Test Evaluation of Concrete Specimens by Non-Traditional Analysis of Acoustic Emission Method

2016 ◽  
Vol 837 ◽  
pp. 198-202
Author(s):  
Luboš Pazdera ◽  
Libor Topolář ◽  
Tomáš Vymazal ◽  
Petr Daněk ◽  
Jaroslav Smutny
Keyword(s):  
Acoustic Emission ◽  
Signal Analysis ◽  
Measurement Data ◽  
Bending Test ◽  
Test Evaluation ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Three Point Bending ◽  
Time Frequency ◽  
Emission Signal

The aim of the paper is focused on the analysis of the mechanical properties of the concrete specimens with plasticizer at three point bending test by the signal analysis of the acoustic emission signal. The evaluations were compared the measurement and the results obtained with theoretical presumptions. The Joint Time Frequency Analysis applied on measurement data and its evaluation is described. It is well known that the Acoustic Emission Method is a very sensitive method to determine active cracks into structure. However, evaluation of acoustic emission signals is very difficult. A non-traditional method was used to signal analysis of burst acoustic emission signals recorded during three point bending test.

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.

scholarly journals Acoustic Emission Wavelet Analysis of Filament Wound Composite Tube Fatigue Damage Process

2015 ◽  
Vol 9 (1) ◽  
pp. 214-219 ◽  
Author(s):  
Su Hua ◽  
Chang Cheng
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Fatigue Damage ◽  
Frequency Band ◽  
Low Frequency ◽  
High Signal ◽  
Wave Form ◽  
Radial Compression ◽  
Time Frequency ◽  
Emission Signal

This paper performed a radial compression fatigue test on glass fiber winding composite tubes, collected acoustic emission signals at different fatigue damages stages, used time frequency analysis techniques for modern wavelet transform, and analyzed the wave form and frequency characteristics of fatigue damaged acoustic emission signals. Three main frequency bands of acoustic emission signal had been identified: 80-160 kHz (low frequency band), 160-300 kHz (middle frequency band), and over 300kHz (high frequency band), corresponding to the three basic damage modes: the fragmentation of matrix resin, the layered damage of fiber and matrix, and the fracture of cellosilk respectively. The usage of wavelet transform enabled the separation of fatigue damaged acoustic emission signals from interference wave, and the access to characteristics of high signal-noise-ratio fatigue damage.

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.

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