Study on vibration and sound signals for fault diagnosis of hoist disc brake system

Hao, Xia; Ding, Yaotian; Lu, Bing; Yang, Xueqiang; Gao, Chuan; Tian, Kai; Li, Yuhang; Meng, Guoying

doi:10.1080/14484846.2021.1938951

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…There are few studies on the relationship between vibration signals and noise signals of disc brake of mine hoist [7]. To sum up, it is of great signi cance to use vibration signals and noise signals to fault diagnosis and prediction of the disc brake and explore the relationship between vibration signals and noise signals of the mine hoist braking system.…”

Section: Introductionmentioning

confidence: 99%

Partial Coherence Analysis for Vibration Signal and Noise Signal of Braking System of Hoist

Hao,

Ding,

Zhang

et al. 2023

Preprint

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In present study, the correlation between vibration signal and noise signal produced by disc brake of mine hoist was investigated. Based on the theory of geometry and motion similarity, the braking system was simplified and a brake test-bed was established in the laboratory. The data of 12 sets of vibration signals and 6 sets of noise signals at three rotational speeds were collected. The 1/3-octave analysis and the partial coherence analysis was used to identify noise sources under different working conditions. The test results showed that the noise source of the faulty brake was located at the brake flange seat and the noise source of the normal brake was located at the brake caliper, which laid a theoretical foundation for the further use of vibration and noise signals for the diagnosis of faults with insufficient disc brake braking force and the health management of mine hoists.

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Section: Introductionmentioning

confidence: 99%

Partial Coherence Analysis for Vibration Signal and Noise Signal of Braking System of Hoist

Hao,

Ding,

Zhang

et al. 2023

Preprint

Self Cite

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Brake fault diagnosis using a voting ensemble of machine learning classifiers

Viswanathan,

Sridharan,

Rakkiyannan

et al. 2024

Results in Engineering

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Advancing Bearing Fault Diagnosis under Variable Working Conditions: A CEEMDAN-SBS Approach with Vibro-Electric Signal Integration

LOURARI,

SOUALHI,

BENKEDJOUH

2023

Preprint

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Bearings represent crucial components within rotating machinery, and unexpected failures can lead to significant damage and unplanned breakdowns. This paper introduces a novel approach to diagnose bearing faults under variable working conditions, leveraging the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Sequential Backward Selection (SBS). CEEMDAN automatically selects intrinsic mode functions (IMFs) from vibration and current signals to establish a comprehensive set of health indicators. Subsequently, the SBS algorithm identifies the most pertinent indicators for different bearing failure modes. The accuracy of the proposed method is evaluated on both vibration and electrical signals using data from a dedicated test bench at the Signal and Industrial Process Analysis Laboratory (LASPI). Results demonstrate the effectiveness of the proposed method in accurately identifying and classifying bearing faults across various working conditions, utilizing both types of signals. This approach holds promise for real-world industrial applications, offering a reliable method for condition monitoring and Diagnostics in bearing systems.

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Study on vibration and sound signals for fault diagnosis of hoist disc brake system

Cited by 3 publications

References 15 publications

Partial Coherence Analysis for Vibration Signal and Noise Signal of Braking System of Hoist

Partial Coherence Analysis for Vibration Signal and Noise Signal of Braking System of Hoist

Brake fault diagnosis using a voting ensemble of machine learning classifiers

Advancing Bearing Fault Diagnosis under Variable Working Conditions: A CEEMDAN-SBS Approach with Vibro-Electric Signal Integration

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