Identification of dangerous hoisting loads based on vibration characteristics

Ma, Chenguang; Xiao, Xingming; Ma, Xiaoping

doi:10.1177/0954406216656885

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The frequency components and maximum frequency ranges of the bump and clearance faults are similar, and the detailed differences on a small time scale at defect points cannot be depicted in Figure 10b,c. Besides this, the bright areas vary with lifting speed [28], lifting load [29], and fault severity [13], which confuses the time-frequency analysis and makes it difficult to classify bump and clearance faults from time-frequency representations. Salvador et al [14] successfully identified diverse rail faults through recognizing fault characteristic frequencies in an STFT spectrogram; the key point is that the frequencies excited by different defects are known in advance and the frequency ranges are isolated, whereas the frequencies excited by bump and clearance faults in this paper under different operating conditions are unknown and may be overlapped.…”

Section: Normal Conditionmentioning

confidence: 99%

Fault Diagnosis of Mine Shaft Guide Rails Using Vibration Signal Analysis Based on Dynamic Time Warping

Jiang

2018

Symmetry

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Guide rails are amongst the most important components of mine hoist systems, and faults in them must be detected as early as possible to avoid fatal breakdowns in mine production. Presently, guide rail inspection is performed visually in most situations. In this paper, we examine a more efficient approach based on multi-time scale and dynamic time warping (DTW) to diagnose guide rail faults including embossment, bumps, and clearance. Firstly, vibration signals collected from operational conveyance under different fault categories are analyzed and the corresponding characteristic waveforms (CWs) are extracted. Embossment faults are identified with priority according to visible disparities in CW patterns on a large time scale. Then, templates for bump and clearance faults are established through processing the CWs on a small time scale. Subsequently, the distances of DTW (DDTWs) between test samples and the selected templates are calculated. Finally, the remaining fault conditions are classified according to the DDTW results since the same fault category has the smallest distance. Experiments are conducted on a guide rail fault simulator to demonstrate the reliability of the proposed method. The resultant diagnosis accuracies are 100%, 90.40%, and 84.53%, respectively, for embossment, bump, and clearance faults, which indicates that the proposed approach is feasible and effective for diagnosing guide rail faults under variable operating conditions and different fault severities.

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Section: Normal Conditionmentioning

confidence: 99%

Fault Diagnosis of Mine Shaft Guide Rails Using Vibration Signal Analysis Based on Dynamic Time Warping

Jiang

2018

Symmetry

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“…Wu et al [6] realized the fault diagnosis of guide rail faults by analysing vibration acceleration signals based on a dynamic time warping strategy. Ma et al [7] proposed a Ritz series and Dempster-Shafer evidence theory-based method to identify dangerous hoisting loads by using vibration characteristics. Xue et al [8] developed a hybrid method consisting of improved permutation entropy, ensemble empirical mode decomposition, a support vector machine, and a particle swarm optimization (PSO) algorithm to analyse mine hoist vibration signals for rope tension fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal vibration estimation of a mine hoist using a hybrid signal fusion method combining UKF, ND and improved DE

Chen,

Zhang,

Chang

et al. 2024

Meas. Sci. Technol.

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The monitoring of cage longitudinal vibration can directly indicate the operational status of mine hoists. However, it is always challenging to collect the sensor signals of moving cages with high dynamic characteristics in real time from complex working environments using traditional monitoring methods. In this study, a more practical hybrid signal fusion approach is proposed to realize estimation of cage longitudinal vibration from a low sampling rate acceleration acquisition signal and a low cost encoder signal for state estimation. A nonlinear differentiator is applied to extract encoder differential signals and expand observation variables. An unscented Kalman observer based on nonlinear mine hoist model is designed to estimate the unknown state. To overcome the influence of the uncertain parameters, an improved differential evolution (DE) algorithm combining parameter adaptive method, reverse learning competition scheme and multiple parallel populations strategy is proposed to find unknown parameters of the observation model and autotune the parameters of the algorithms by using low sampling rate acceleration. Sensor data of the simulated experiment platform were collected and processed by the xPC system to validate the effectiveness of the proposed strategy. The experimental results showed that the improved DE (IDE) algorithm had a faster mean time for parameter tuning and the smallest fitness value compared to the standard DE, the particle swarm optimization algorithm and the genetic algorithm. Moreover, the longitudinal vibration estimation system, after parameter tuning by the IDE optimization algorithm, could achieve the purpose of signal estimation, with a smaller estimation error and a better estimation effect.

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“…Ren et al [17] proposed a new type of skip load identification method based on the mapping relationship between speed and load of hoist. Ma et al [18] found that the vibration characteristics of the steel wire rope under different loads of the hoist were significantly different, and the different dynamic behaviors of the steel wire rope could be used as the basis for load monitoring. Xu et al [19] proposed to convert the wire rope tension measurement into pressure measurement.…”

Section: Introductionmentioning

confidence: 99%

Research on load monitoring technology of mine hoist based on machine vision

Tian,

Wang,

Guo

et al. 2024

Meas. Sci. Technol.

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A hoist load monitoring method based on machine vision technology is proposed in this paper to address the frequent overloading accidents of mine hoists and the low safety and reliability of existing contact load monitoring technologies. The depth image of the skip undergoes time domain and spatial bilateral filtering algorithms for noise reduction, followed by conditional filtering and downsampling algorithms to remove redundant point cloud data. Point cloud recognition, extraction, segmentation, and alignment algorithms are then applied to quickly generate a skip point cloud model. A surface reconstruction optimization process combining greedy projection triangulation algorithm and void repair algorithm is proposed to obtain a smooth and complete sealing of the skip. The closed surface model volume is calculated using VTK volume function. Based on single-rope winding hoist, a load visual monitoring system is constructed for relevant experimental research. Results show that this method can accurately measure the loaded coal volume with a relative error range of 0.05%~4.13%, meeting practical application requirements while providing an effective way for non-contact accurate measurement of hoist loads in mines.

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Identification of dangerous hoisting loads based on vibration characteristics

Cited by 3 publications

References 12 publications

Fault Diagnosis of Mine Shaft Guide Rails Using Vibration Signal Analysis Based on Dynamic Time Warping

Fault Diagnosis of Mine Shaft Guide Rails Using Vibration Signal Analysis Based on Dynamic Time Warping

Longitudinal vibration estimation of a mine hoist using a hybrid signal fusion method combining UKF, ND and improved DE

Research on load monitoring technology of mine hoist based on machine vision

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