Fault diagnosis of head sheaves based on vibration measurement and data mining method
Head sheaves are critical components in a mine hoisting system. It is inconvenient for workers to climb up to the high platform for overhaul and maintenance, and there is an urgent need for condition monitoring and fault diagnosis of head sheaves. In this article, Fault Tree Analysis is employed to...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2020-07-01
|
Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814020941331 |
id |
doaj-dd49782783bb416e8dba46bc5d311ac8 |
---|---|
record_format |
Article |
spelling |
doaj-dd49782783bb416e8dba46bc5d311ac82020-11-25T03:20:16ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402020-07-011210.1177/1687814020941331Fault diagnosis of head sheaves based on vibration measurement and data mining methodChi Ma0Jiannan Yao1Xinming Xiao2Xiaohan Zhang3Yuqiang Jiang4China University of Mining and Technology, Xuzhou, ChinaSchool of Mechanical Engineering, Nantong University, Nantong, ChinaChina University of Mining and Technology, Xuzhou, ChinaChina University of Mining and Technology, Xuzhou, ChinaChina University of Mining and Technology, Xuzhou, ChinaHead sheaves are critical components in a mine hoisting system. It is inconvenient for workers to climb up to the high platform for overhaul and maintenance, and there is an urgent need for condition monitoring and fault diagnosis of head sheaves. In this article, Fault Tree Analysis is employed to investigate the faults of head sheaves, and headframe inclination, bearing faults, and head sheave swing are the three focal faults discussed. A test rig is built to simulate these three faults and collect vibration signals at bearing blocks. Based on vibration signals, some characteristic parameters are calculated, and together with the fault labels, a sample set is established. Before the selection of an excellent data mining method, these features are screened according to their significance, and then, gain–percentile chart, response–percentile chart, and prediction accuracy are used as the criteria to make a comparison between data mining algorithms. The result shows the boosted tree algorithm outperforms others and presents excellent performance on the evaluation of head sheave faults. Finally, this method is verified on a data set of 20 samples, and each case is identified correctly, which illustrates its high applicability.https://doi.org/10.1177/1687814020941331 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chi Ma Jiannan Yao Xinming Xiao Xiaohan Zhang Yuqiang Jiang |
spellingShingle |
Chi Ma Jiannan Yao Xinming Xiao Xiaohan Zhang Yuqiang Jiang Fault diagnosis of head sheaves based on vibration measurement and data mining method Advances in Mechanical Engineering |
author_facet |
Chi Ma Jiannan Yao Xinming Xiao Xiaohan Zhang Yuqiang Jiang |
author_sort |
Chi Ma |
title |
Fault diagnosis of head sheaves based on vibration measurement and data mining method |
title_short |
Fault diagnosis of head sheaves based on vibration measurement and data mining method |
title_full |
Fault diagnosis of head sheaves based on vibration measurement and data mining method |
title_fullStr |
Fault diagnosis of head sheaves based on vibration measurement and data mining method |
title_full_unstemmed |
Fault diagnosis of head sheaves based on vibration measurement and data mining method |
title_sort |
fault diagnosis of head sheaves based on vibration measurement and data mining method |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2020-07-01 |
description |
Head sheaves are critical components in a mine hoisting system. It is inconvenient for workers to climb up to the high platform for overhaul and maintenance, and there is an urgent need for condition monitoring and fault diagnosis of head sheaves. In this article, Fault Tree Analysis is employed to investigate the faults of head sheaves, and headframe inclination, bearing faults, and head sheave swing are the three focal faults discussed. A test rig is built to simulate these three faults and collect vibration signals at bearing blocks. Based on vibration signals, some characteristic parameters are calculated, and together with the fault labels, a sample set is established. Before the selection of an excellent data mining method, these features are screened according to their significance, and then, gain–percentile chart, response–percentile chart, and prediction accuracy are used as the criteria to make a comparison between data mining algorithms. The result shows the boosted tree algorithm outperforms others and presents excellent performance on the evaluation of head sheave faults. Finally, this method is verified on a data set of 20 samples, and each case is identified correctly, which illustrates its high applicability. |
url |
https://doi.org/10.1177/1687814020941331 |
work_keys_str_mv |
AT chima faultdiagnosisofheadsheavesbasedonvibrationmeasurementanddataminingmethod AT jiannanyao faultdiagnosisofheadsheavesbasedonvibrationmeasurementanddataminingmethod AT xinmingxiao faultdiagnosisofheadsheavesbasedonvibrationmeasurementanddataminingmethod AT xiaohanzhang faultdiagnosisofheadsheavesbasedonvibrationmeasurementanddataminingmethod AT yuqiangjiang faultdiagnosisofheadsheavesbasedonvibrationmeasurementanddataminingmethod |
_version_ |
1724618509072203776 |