Bearing fault diagnosis based on a new acoustic emission sensor technique

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.

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Bearing fault diagnosis in rotating machinery based on cepstrum pre-whitening of vibration and acoustic emission

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-019-04171-6 ◽

2019 ◽

Vol 104 (9-12) ◽

pp. 4155-4168 ◽

Cited By ~ 3

Author(s):

David Ibarra-Zarate ◽

Oscar Tamayo-Pazos ◽

Antonio Vallejo-Guevara

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Distance and Density Similarity Based Enhancedk-NN Classifier for Improving Fault Diagnosis Performance of Bearings

Shock and Vibration ◽

10.1155/2016/3843192 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Sharif Uddin ◽

Md. Rashedul Islam ◽

Sheraz Ali Khan ◽

Jaeyoung Kim ◽

Jong-Myon Kim ◽

...

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Similarity Measure ◽

Classification Accuracy ◽

Diagnostic Performance ◽

Nearest Neighbor ◽

Neighborhood Size ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Ae Signals

An enhancedk-nearest neighbor (k-NN) classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditionalk-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size,k. This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposedk-NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE) signals. Experimental results demonstrate that the proposed scheme, which uses the enhancedk-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size,k.

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Low speed bearing fault diagnosis using acoustic emission sensors

Applied Acoustics ◽

10.1016/j.apacoust.2015.10.028 ◽

2016 ◽

Vol 105 ◽

pp. 35-44 ◽

Cited By ~ 43

Author(s):

Brandon Van Hecke ◽

Jae Yoon ◽

David He

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Low Speed ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Acoustic Emission Sensors

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Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

Structural Health Monitoring ◽

10.1177/1475921716687167 ◽

2017 ◽

Vol 17 (2) ◽

pp. 156-168 ◽

Cited By ~ 14

Author(s):

Cheng-Wei Fei ◽

Yat-Sze Choy ◽

Guang-Chen Bai ◽

Wen-Zhong Tang

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Frequency Domain ◽

Rolling Bearing ◽

Distance Method ◽

Bearing Faults ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Rolling Element ◽

Entropy Distance

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

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On the Use of Spectral Averaging of Acoustic Emission Signals for Bearing Fault Diagnostics

Journal of Vibration and Acoustics ◽

10.1115/1.4028322 ◽

2014 ◽

Vol 136 (6) ◽

Cited By ~ 16

Author(s):

Brandon Van Hecke ◽

David He ◽

Yongzhi Qu

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Reduction Technique ◽

Signal Frequency ◽

Fault Diagnostics ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Area Of Interest ◽

Ae Signals ◽

Spectral Averaging

For years, vibration analysis has been the industry standard for bearing fault diagnosis. However, due to the various advantages over vibration based techniques, the quantification of acoustic emission (AE) for bearing health diagnosis has been an area of interest for recent years. Additionally, most AE based methodologies to date utilize data mining technologies. Presented in this paper is a new approach, combining a heterodyne based frequency reduction technique, time synchronous resampling, and spectral averaging to process AE signals and compute condition indicators (CIs) for bearing fault diagnostics. First, the heterodyne based frequency reduction technique allows the AE signal frequency to be down shifted from several MHz to less than 50 kHz, which approaches that of vibration based methodologies. Next, the sampled AE signals are band pass filtered to retain the useful information related to the bearing defects. Last, a trigger signal is utilized to time synchronously resample the AE signals to allow the calculation of a spectral average and the extraction and evaluation of CIs for bearing fault diagnosis. The technique presented in this paper is validated using the AE signals of seeded fault steel bearings on a bearing test rig. Presented is an effective AE based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage. Moreover, the effectiveness of the presented approach is established through the comparison of both AE and vibration data.

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Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals

Applied Sciences ◽

10.3390/app10062050 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2050 ◽

Cited By ~ 5

Author(s):

JaeYoung Kim ◽

Jong-Myon Kim

Keyword(s):

Neural Network ◽

Acoustic Emission ◽

Fault Diagnosis ◽

Bearing Failure ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Effective Form ◽

Fault Features ◽

Rolling Elements ◽

Bearing Characteristic

Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure.

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