Bearing Fault Detection Using Acoustic Emission Signals Analyzed by Empirical Mode Decomposition

Hiremath, Niranjan

doi:10.15623/ijret.2014.0315084

Cited by 5 publications

(1 citation statement)

References 17 publications

(5 reference statements)

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“…It was found that the clutch with a logarithmic profile had a better working performance in comparison with the two other profiles. Niranjan Hiremath and D.Malikarjuna Reddy 22,23 through comparative analysis, reveal that acoustic technology can detect bearing failure more effectively than vibration technology and further investigated the effect for system friction and vibration of TiN and AlCrN based PVD coatings.…”

Section: Introductionmentioning

confidence: 99%

Model and Characteristic Simulation Analysis of a Two-Speed Transmission System

Chen¹,

Xie²,

Liu³

et al. 2021

The International Journal of Acoustics and Vibration

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One of the main tasks in the research of a helicopter two-speed transmission system was to improve its dynamic characteristics. For the low gear mode of the system, a dynamic model was established by using the lumped parameter method, the method of Runge-Kutta was used to solve the nonlinear dynamic system equations. The effect of the gear module on the dynamic transmission error, dynamic load of the gear pair and the dynamic windup angle of a one-way clutch were studied. And the effect of the one-way clutch torsional stiffness on the dynamic transmission error and dynamic load of the gear pair was also studied. The results show that: 1)~the dynamic transmission error of the gear pair decreases and the dynamic load of the gear pair increases with the increase of the gear module at the lower range of excitation frequencies; 2)~the dynamic windup angle of the one-way clutch increases with an increase of the gear module. 3)~the dynamic transmission error of the gear pair and the maximum dynamic load increases with an increase of the one-way clutch torsional stiffness at the lower and medium range of excitation frequencies. The above results can provide reference for the subsequent upgrade and improvement of the two-speed transmission system.

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Section: Introductionmentioning

confidence: 99%

Model and Characteristic Simulation Analysis of a Two-Speed Transmission System

Chen¹,

Xie²,

Liu³

et al. 2021

The International Journal of Acoustics and Vibration

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Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis

Faysal

Ngui

Lim

2021

J. Vib. Eng. Technol.

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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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Bearing Fault Detection Using Acoustic Emission Signals Analyzed by Empirical Mode Decomposition

Abstract: Abstract

Cited by 5 publications

References 17 publications

Model and Characteristic Simulation Analysis of a Two-Speed Transmission System

Model and Characteristic Simulation Analysis of a Two-Speed Transmission System

Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis

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

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