2019
DOI: 10.1155/2019/6986240
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Fault Detection for the Scraper Chain Based on Vibration Analysis Using the Adaptive Optimal Kernel Time‐Frequency Representation

Abstract: A scraper conveyor is a key component of large-scale mechanized coal mining equipment, and its failure patterns are mainly caused by chain jam and chain fracture. Due to the difficulties with direct measurement for multiple performance parameters of the scraper chain, this paper deals with a novel strategy for fault detection of the scraper chain based on vibration analysis of the chute. First, a chute vibration model (CVM) is applied for modal analysis, and the hammer impact test (HIT) is conducted to validat… Show more

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Cited by 10 publications
(6 citation statements)
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“…Some experts and scholars have done a lot of work on flexible body dynamics, bending, finite element simulation and so on, which provide a theoretical basis for this study, such as references [11][12][13][14][15][16].…”
Section: Introductionmentioning
confidence: 99%
“…Some experts and scholars have done a lot of work on flexible body dynamics, bending, finite element simulation and so on, which provide a theoretical basis for this study, such as references [11][12][13][14][15][16].…”
Section: Introductionmentioning
confidence: 99%
“…The crack initiation properties of the chain ring under the action of time-varying load were studied, and a prediction model of the crack initiation life was established using the multiaxial fatigue strength theory. Zhang et al [21] proposed a novel strategy for the fault detection of scraper chains based on a vibration analysis of the chute. Zhao et al [22] proposed a local gear fault diagnosis method based on a bispectral analysis of signal envelopes obtained from stator current processing under time-varying load.…”
Section: Introductionmentioning
confidence: 99%
“…For signals in the time or frequency domain, several signal processing techniques have been proposed for the extraction of features from the acceleration responses. These include short-term Fourier transform [ 42 ], wavelet transform [ 11 ], singular value decomposition [ 43 ], kernel time-frequency representation [ 44 ] and more. Each one has its own merits and demerits.…”
Section: Introductionmentioning
confidence: 99%