Fault diagnosis of head sheaves based on vibration measurement and data mining method

Ma, Chenguang; Yao, Jiannan; Xiao, Xinming; Zhang, Xiaohan; Jiang, Yuqiang

doi:10.1177/1687814020941331

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…e diagnostic method based on the neural network is divided into two categories. One is the neural network as the output estimator and replaces the traditional observer; the other is the neural network for classification and pattern recognition [5].…”

Section: Introductionmentioning

confidence: 99%

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

Guo¹,

Meng²,

Yu³

et al. 2022

International Transactions on Electrical Energy Systems

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In recent years, with the continuous improvement of system networking, the use of robot electric power equipment is becoming more and more extensive, so it is very necessary to diagnose and monitor its status. Therefore, a diagnosis and monitoring system based on the data mining algorithm is constructed. This study mainly uses the gray prediction algorithm and discrete prediction algorithm; the results showed that after the combination of gray prediction has a certain degree of increase, through the analysis, we can draw the reason for this is that gray prediction algorithm to check the failure data is no longer as input data in the detection of outliers, thereby reducing the noise of the data set, so that the perfomance of oulier detection algorithm can ahieve a great progress. In terms of the running time of outlier detection, the running time of outlier detection using K-Mean algorithm and DBSCAN algorithm increases to a certain extent because the algorithm combines gray prediction and increases the algorithm process.

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Section: Introductionmentioning

confidence: 99%

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

Guo¹,

Meng²,

Yu³

et al. 2022

International Transactions on Electrical Energy Systems

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“…The large headframe is the core structure of a mine hoisting system, and also a highrise and hollow steel-framed building [1][2][3]. With the development of mine hoisting system towards large-scale, higher requirements are put on the structural design of the large headframe [4,5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Characteristic Analysis and Structural Optimization Design of the Large Mining Headframe

Liu

Huang

et al. 2022

Machines

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A large headframe is the core structure of a mine hoisting system. In the traditional design, only the static analysis under load is considered, resulting in the resonance phenomenon of the large headframe in later applications. In order to restrain the resonance phenomenon, a novel method for dynamic characteristic analysis and structural optimization design of a large headframe is proposed. First, the eigenfrequencies and vibration modes of the large headframe were obtained through modal analysis. The results showed that the numerical values of the multi-order eigenfrequencies of the system are relatively close. When subjected to alternating loads of similar frequencies, a large headframe is prone to the resonance phenomenon. Second, the steady-state vibration response of the large headframe was obtained through harmonic response analysis. The results showed that when the frequency of the alternating load is close to the first-order eigenfrequency, the vibration amplitude increases. Meanwhile, the fourth-order and the fifth-order eigenfrequencies are very close. When subjected to alternating loads of similar frequencies, the fourth-order and the fifth-order vibration modes of the headframe will be excited simultaneously. At this time, the headframe will have a strong resonance, which may cause structural damage and other problems. Finally, based on the above analysis, nine different structural optimization schemes are proposed in this paper. Through modal analysis and harmonic response analysis, the nine schemes were compared and analyzed, and the optimal scheme was eventually determined as scheme 9. The method proposed in this paper provides a new concept for the structural optimization design of a large mining headframe, and it has great significance for restraining the resonance phenomenon and ensuring the safety of mining operations.

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“…Once a bearing fault occurs, it is effortless to cause serious safety accidents. erefore, the study of bearing automatic fault diagnosis is very important for a safe operation of the hoisting system [1,2].…”

Section: Introductionmentioning

confidence: 99%

Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Automatic Fault Diagnosis of Hoist Bearing

Kou

et al. 2021

Shock and Vibration

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Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a powerful method that can extract the periodic characteristics of signal effectively, but this method needs to evaluate the fault cycle a priori, and moreover, the results obtained in a complex environment are easily affected by noise. These drawbacks reduce the application of MOMEDA in engineering practice greatly. In order to avoid such problems, in this paper, we propose an adaptive fault diagnosis method composed of two parts: fault information integration and extracted feature evaluation. In the first part, a Teager energy spectrum amplitude factor (T-SAF) is proposed to select the intrinsic mode function (IMF) components decomposed by ensemble empirical mode decomposition (EEMD), and a combined mode function (CMF) is proposed to further reduce the mode mixing. In the second part, the particle swarm optimization (PSO) taking fractal dimension as the objective function is employed to choose the filter length of MOMEDA, and then the feature frequency is extracted by MOMEDA from the reconstructed signal. A cyclic recognition method is proposed to appraise the extracted feature frequency, and the evaluation system based on threshold and weight coefficient removes the wrong feature frequency. Finally, the feasibility of the method is verified by simulation data, experimental signals, and on-site signals. The results show that the proposed method can effectively identify the bearing state.

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Fault diagnosis of head sheaves based on vibration measurement and data mining method

Cited by 5 publications

References 18 publications

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

Dynamic Characteristic Analysis and Structural Optimization Design of the Large Mining Headframe

Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Automatic Fault Diagnosis of Hoist Bearing

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