Vibration monitoring and analysis of ball bearing using GSD platform

Nithyavathy, N.; Kumar, Satish; Sheriff, K. A. Ibrahim; Hariram, A.; Prasaad, P. Hare

doi:10.1016/j.matpr.2020.12.1088

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The commonly used CM techniques are the following (UESystems 2019): ultrasound (Bakar et al 2013), vibration analysis (Wen and Gao 2018;Nithyavathy et al 2021), wear particle testing (Yadav et al 2021;Baptista et al 2021), thermography, motor signal current analysis (Dos Santos et al 2017) and nondestructive testing (Narushin et al 2021), but additional techniques exist such as torque, voltage and envelopes (Rajan Babu et al 2021), acoustic emission (Jones et al 2022), pressure (Zhao et al 2017) and temperature monitoring (Bakar et al 2013;Zhao et al 2017).…”

Section: Predictive Maintenance Backgroundmentioning

confidence: 99%

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects

Serradilla

Zugasti

Rodriguez³

et al. 2022

Appl Intell

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Given the growing amount of industrial data in the 4th industrial revolution, deep learning solutions have become popular for predictive maintenance (PdM) tasks, which involve monitoring assets to anticipate their requirements and optimise maintenance tasks. However, given the large variety of such tasks in the literature, choosing the most suitable architecture for each use case is difficult. This work aims to facilitate this task

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Section: Predictive Maintenance Backgroundmentioning

confidence: 99%

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects

Serradilla

Zugasti

Rodriguez³

et al. 2022

Appl Intell

View full text Add to dashboard Cite

show abstract

“…As the main supporting component of rotating machinery, rolling bearing is widely used in mechanical industries such as aerospace [1], shipbuilding [2], and the automobile industry [3]. The high-speed operation of bearing is the premise of the high-speed operation of the whole equipment [4].…”

Section: Introductionmentioning

confidence: 99%

Study on speed transfer characteristics of double-deck ball bearing based on generalized four-terminal parameter method

Zhang

2022

Nonlinear Dyn

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The speed transfer ratio is one of the most important parameters of the double-deck ball bearing. For the calculation of speed ratio, most of the theoretical estimation formulas related to the structure are used, but under some measured working conditions, the speed ratio is not only related to structural parameters. To explore the speed transmission mechanism of double-deck ball bearing, a new method for calculating the speed transmission characteristics of double-deck ball bearing is proposed in this paper. The generalized four-terminal parameter method is applied to the speed analysis of double-deck ball bearing for the first time. The speed transmission analysis model of double-deck ball bearing is established by combining the three theories of quasi statics theory, elastohydrodynamic lubrication theory, and generalized four-terminal parameter theory. A variety of factors are taken into account to solve the research status of rotational speed transfer ratio only considering a single factor. The speed transmission characteristics of the double-deck ball bearing after the system are accelerated to the steady-state are studied, and the correctness of the results is verified by experiments, which provides a new idea for the analysis of the speed transmission characteristics of the double-deck ball bearing, and provides a theoretical basis for the exploration and design of the potential application value of the double-deck ball bearing.

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Efficient vibration analysis system using empirical mode decomposition residual signal and multi-axis data

Ninawe,

Deshmukh

2024

Journal of Vibration and Control

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The empirical mode decomposition (EMD) method is a technique that recursively decomposes an input signal into intrinsic mode functions (IMFs) by residual signals, primarily for identifying desirable features. The suggested algorithm observes the residual signal instead of the IMF, which lowers the computing load. The study introduces a new method for detecting bearing faults by enhancing signal extraction from sensor data using EMD and multi-axis feature extraction. This method streamlines the process by filtering out high-frequency noise and correlating residual signal information with analysis. The approach also enhances the signal-to-noise ratio (SNR) and feature signature identification using digital signal processing (DSP) techniques. The algorithm for vibration data analysis is tested for bearing failures, identifying shaft frequency and inner race bearing faults, which can be implemented in parallel. For the inner race fault bearing analysis, two-level EMD with a residual signal generates output similar to five-iteration EMD, saving 60% of computations. The use of spectral multiplication to multi-axis data processing produced a rise in the SNR of 18.32 dB to 20.92 dB for Y-axis and X-axis input, respectively. When compared to the single-axis IMF data computation, 20% fewer iterations are needed overall. A single-level EMD is adequate for calculating the rotational frequency of a healthy bearing. For the Y- and X-axis input, multi-axis analysis increases SNR by 10.68 dB and 13.14 dB, accordingly. This comprehensive strategy reduces computational complexity, improves fault detection accuracy, and minimizes noise impact, making it a promising solution for bearing fault detection.

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Vibration monitoring and analysis of ball bearing using GSD platform

Cited by 4 publications

References 8 publications

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects

Study on speed transfer characteristics of double-deck ball bearing based on generalized four-terminal parameter method

Efficient vibration analysis system using empirical mode decomposition residual signal and multi-axis data

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