Low-Speed Bearing Fault Diagnosis Based on ArSSAE Model Using Acoustic Emission and Vibration Signals

Saufi, Syahril Ramadhan; Ahmad, Zair Asrar; Leong, M. Salman; Lim, Meng Hee

doi:10.1109/access.2019.2909756

Cited by 41 publications

(16 citation statements)

References 45 publications

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“…e larger value of the total energy E i calculated by equation (1) will cause difficulty in the subsequent feature selection [28]. erefore, the feature vector matrix 2) , and the feature vector matrix…”

Section: Time-frequency Domain Feature Extractionmentioning

confidence: 99%

“…e rolling bearings in hydraulic pumps operate in harsh environments with high pressure and high temperature, leading to operation degradation or even complete shutdown of the entire mechanical system [1,2]. However, the fault representative features are nonlinear and nonstationary and seriously modulated by noise, and the traditional time domain and frequency domain fault diagnosis methods are not efficient to predict bearing faults, especially under changing and complex operating conditions [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Diagnosis in the Mixed Domain Based on Crossover‐Mutation Chaotic Particle Swarm

Tongle

Kong

et al. 2021

Complexity

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The classification frameworks for fault diagnosis of rolling element bearings in rotating machinery are mostly based on analysis in a single time-frequency domain, where sensitive features are not completely extracted. To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency domain. Secondly, the weighted maximum relevance minimum redundancy (WMRMR) algorithm is adopted to reduce the dimension of the feature set and to establish the representative feature set. Thirdly, a new crossover-mutation strategy is suggested to reduce the local minima in optimization, and an optimization disturbance is added. Finally, the support vector machine is optimized using the improved chaotic particle swarm to improve fault classification diagnosis. The effectiveness of the proposed new bearing fault diagnostic technique is verified by experimental tests under different bearing conditions. Test results showed that the bearing fault classification accuracy of CMCPSO-SVM in the mixed domain was much higher than those in a single feature domain.

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Section: Time-frequency Domain Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis in the Mixed Domain Based on Crossover‐Mutation Chaotic Particle Swarm

Tongle

Kong

et al. 2021

Complexity

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show abstract

“…Input characteristics for DL models are essential for accurate diagnosis. Saufi et al conducted a study in which each type of input data produced a different diagnosis performance [71]. The authors used a stacked sparse autoencoder (SSAE) model to analyse multiple types of input, proving the versatility of a DL model over a SML model.…”

Section: ) Characteristics Of Input Datamentioning

confidence: 99%

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

et al. 2019

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In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed. INDEX TERMSDeep learning, fault detection and diagnosis, current challenges, future developments. I. INTRODUCTION Safety and reliability are key factors in industrial operations. Rotating machinery is a vital component in many industries, and it is prone to failure due to harsh working conditions and long operational times [1], [2]. Examples of rotating machinery components including gears [3], pumps [4], bearings [5], shafts [6], blades [7], motors [8] and engines [9]. Failures in rotating machinery should be detected as early as possible to prevent critical damage [10] and sudden halt of machine operation. Failures may cause delays in operations and, consequently, tremendous economic loss [11]. For example, petrochemical industries lose around 20 billion dollars per year due to faults in their machine components [12]. According to a report by Duan et al. maintenance accounts for more than 60% of the total cost of aircraft engine components [13]. In the worst case, a machinery component failure may lead to loss of human life. Elasha et al. discussed a caseThe associate editor coordinating the review of this article and approving it for publication was Kezhi Li.

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“…In recent years, scholars have carried out a large number of systematic studies on the quantitative diagnosis mechanism and method of the defect size of rolling bearing and have obtained some remarkable achievements that enhanced the scholarship. If a rolling element bearing has a local fault, a series of impulses with certain laws will be generated in its time-domain waveform [2], [3], so vibration signals are used widely for the fault diagnosis of bearing [4]- [9]. However, bearing fault signatures are usually contaminated or even overwhelmed by interfering noise [10].…”

Section: Introductionmentioning

confidence: 99%

Mechanism and Method for Outer Raceway Defect Localization of Ball Bearings

et al. 2020

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The localization of outer raceway defect plays a significant role in malfunction elimination, failure cause analysis as well as the residual life prediction of ball bearings. Based on the nonlinear dynamic model for a ball bearing and the outer raceway defect model considering the ball finite size, this article employs the detailed mathematical derivation and theoretical analysis of the load distribution for the bearing system with an outer raceway defect located at the different angular positions. Therefore, the essential mechanism of the approximate linear relationship between the proposed indices, namely horizontal-vertical synchronized Peak (HVSPeak) and horizontal-vertical synchronized RMS (HVSRMS), and the defect angular position is explained. More importantly, it is theoretically demonstrated that HVSRMS is approximately a cotangent function with the defect angular position as the only variable, which indicates that the index has excellent anti-interference and practicability. In addition, the superiority and necessity of the HVSRMS index can be seen when compared to HVSPeak, RMS, SampEn and Lemple-Ziv. It is validated through simulation and experiment results that HVSRMS index can efficiently diagnose the angular position of outer raceway defect. Finally, the signal denoising methods for HVSRMS are compared and studied, which indicates the direction for subsequent research.

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Low-Speed Bearing Fault Diagnosis Based on ArSSAE Model Using Acoustic Emission and Vibration Signals

Cited by 41 publications

References 45 publications

Bearing Fault Diagnosis in the Mixed Domain Based on Crossover‐Mutation Chaotic Particle Swarm

Bearing Fault Diagnosis in the Mixed Domain Based on Crossover‐Mutation Chaotic Particle Swarm

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

Mechanism and Method for Outer Raceway Defect Localization of Ball Bearings

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