Fault Analysis of Shearer-Cutting Units Driven by Integrated Importance Measure
Abstract
:1. Introduction
2. Fault Analysis Method of Shearer-Cutting Unit Driven by IIM
- Structure analysis of shearer-cutting units: analyze the composition of cutting units and explore the functions of key subsystems.
- General fault identification of shearer-cutting units: determine the failure causes through the failure phenomenon and determine the location of the failure.
- Fault tree construction of shearer-cutting units: determine the top event, middle events, and bottom events; establish the logical relationships among these events; evaluate the occurrence probability of bottom events.
- The key fault causes analysis of shearer-cutting units: quantify the fault causes based on IIM and rank the fault causes to determine the key ones.
- Maintenance decision implementation based on IIM ranking: under the limited maintenance resources, the fault with higher IIM should give priority to performing maintenance.
2.1. Structure Analysis of Shearer-Cutting Units
2.2. General Fault Identification of Shearer-Cutting Units
2.3. Fault Tree Construction of Shearer-Cutting Units
2.4. Key Fault Causes Analysis of Shearer-Cutting Units
2.5. Maintenance Decision Implementation Based on IIM Ranking
3. A Case Study of Cutting Units for MG400/930-WD Shearer
3.1. Identifying General Faults of Shearer-Cutting Units
3.2. Fault Tree Construction Based on the Logical Relationship among Events
3.3. Evaluating the Occurrence Probability of Bottom Events
3.4. Analysis of Key Failure Causes Based on IIM
4. Effectiveness Discussion of IIM Ranking
4.1. Relative Value Distribution Analysis by Radial Bar Charts
4.2. Ranking Accuracy Comparison Based on Mean Average Precision
- Select n groups of key elements, assuming that the number of key elements in group i is mi.
- Determine the position Kij of the key element in a particular ranking, and the key element is the element in the group .
- Calculate the mAP of different rankings under different key element groups, as shown in Equation (5).
4.3. Ranking Results Discussion of Different Importance Measures
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Li, Y.; Pan, S.; Ning, S.; Shao, L.; Jing, Z.; Wang, Z. Coal measure metallogeny: Metallogenic system and implication for resource and environment. Sci. China Earth Sci. 2022, 65, 1211–1228. [Google Scholar] [CrossRef]
- Eshaghian, O.; Hoseinie, S.H.; Maleki, A. Multi-attribute failure analysis of coal cutting picks on longwall shearer machine. Eng. Fail. Anal. 2021, 120, 105069. [Google Scholar] [CrossRef]
- Wang, Z. Current status and prospects of reliability systems engineering in China. Front. Eng. Manag. 2021, 8, 492–502. [Google Scholar] [CrossRef]
- Bai, G.; Wang, H.; Zheng, X.; Dui, H.; Xie, M. Improved resilience measure for component recovery priority in power grids. Front. Eng. Manag. 2021, 8, 545–556. [Google Scholar] [CrossRef]
- Zuo, M. System reliability and system resilience. Front. Eng. Manag. 2021, 8, 615–619. [Google Scholar] [CrossRef]
- Si, S.; Zhao, J.; Cai, Z.; Dui, H. Recent advances in system reliability optimization driven by importance measures. Front. Eng. Manag. 2020, 7, 335–358. [Google Scholar] [CrossRef]
- Dui, H.; Zheng, X.; Wu, S. Resilience analysis of maritime transportation systems based on importance measures. Reliab. Eng. Syst. Saf. 2021, 209, 107461. [Google Scholar] [CrossRef]
- Du, Y.; Si, S.; Jin, T. Reliability importance measures for network based on failure counting process. IEEE Trans. Reliab. 2018, 68, 267–279. [Google Scholar] [CrossRef]
- Almoghathawi, Y.; Barker, K. Component importance measures for interdependent infrastructure network resilience. Comput. Ind. Eng. 2019, 133, 153–164. [Google Scholar] [CrossRef]
- Kala, Z. New importance measures based on failure probability in global sensitivity analysis of reliability. Mathematics 2021, 9, 2425. [Google Scholar] [CrossRef]
- Si, S.; Levitin, G.; Dui, H.; Sun, S. Component state-based integrated importance measure for multi-state systems. Reliab. Eng. Syst. Saf. 2013, 116, 75–83. [Google Scholar] [CrossRef]
- Dui, H.; Zhang, C.; Zheng, X. Component joint importance measures for maintenances in submarine blowout preventer system. J. Loss Prev. Process Ind. 2020, 63, 104003. [Google Scholar] [CrossRef]
- Liu, F.; Dui, H.; Li, Z. Reliability analysis for electrical power systems based on importance measures. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2022, 236, 317–328. [Google Scholar] [CrossRef]
- Chen, L.; Gao, Y.; Dui, H.; Xing, L. Importance measure-based maintenance optimization strategy for pod slewing system. Reliab. Eng. Syst. Saf. 2021, 216, 108001. [Google Scholar] [CrossRef]
- Kang, J.; Sun, L.; Soares, C.G. Fault tree analysis of floating offshore wind turbines. Renew. Energy 2019, 133, 1455–1467. [Google Scholar] [CrossRef]
- Sakurahara, T.; Reihani, S.; Kee, E.; Mohaghegh, Z. Global importance measure methodology for integrated probabilistic risk assessment. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2020, 234, 377–396. [Google Scholar] [CrossRef]
- Budiyanto, M.A.; Fernanda, H. Risk assessment of work accident in container terminals using the fault tree analysis method. J. Mar. Sci. Eng. 2020, 8, 466. [Google Scholar] [CrossRef]
- Gachlou, M.; Roozbahani, A.; Banihabib, M.E. Comprehensive risk assessment of river basins using Fault Tree Analysis. J. Hydrol. 2019, 577, 123974. [Google Scholar] [CrossRef]
- Gu, D.; Zhong, Y.; Xu, Z.; Chen, B.; Wang, Z. An importance measure of a CNC lathe considering failure correlations. Qual. Reliab. Eng. Int. 2022, 38, 1367–1379. [Google Scholar] [CrossRef]
- Ud-Din, S.; Yoon, Y. Analysis of loss of control parameters for aircraft maneuvering in general aviation. J. Adv. Transp. 2018, 2018, 1–19. [Google Scholar] [CrossRef] [Green Version]
- Shu, X.; Guo, Y.; Yang, H.; Wei, K. Reliability study of motor controller in electric vehicle by the approach of fault tree analysis. Eng. Fail. Anal. 2021, 121, 105165. [Google Scholar] [CrossRef]
- Ikwan, F.; Sanders, D.; Hassan, M. Safety evaluation of leak in a storage tank using fault tree analysis and risk matrix analysis. J. Loss Prev. Process Ind. 2021, 73, 104597. [Google Scholar] [CrossRef]
- Li, H.; Soares, C.G.; Huang, H.Z. Reliability analysis of a floating offshore wind turbine using Bayesian Networks. Ocean Eng. 2020, 217, 107827. [Google Scholar] [CrossRef]
- García Márquez, F.P.; Segovia Ramírez, I.; Mohammadi-Ivatloo, B.; Marugán, A.P. Reliability dynamic analysis by fault trees and binary decision diagrams. Information 2020, 11, 324. [Google Scholar] [CrossRef]
- Usman, K.; Peter Nicholas Burrow, M.; Singh Ghataora, G.; Sasidharan, M. Using probabilistic fault tree analysis and Monte Carlo simulation to examine the likelihood of risks associated with ballasted railway drainage failure. Transp. Res. Rec. 2021, 2675, 70–89. [Google Scholar] [CrossRef]
- Chen, J.; Li, Z.; Pan, J.; Chen, G.; Zi, Y.; Yuan, J.; Chen, B.; He, Z. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mech. Syst. Signal Process. 2016, 70, 1–35. [Google Scholar] [CrossRef]
- Li, S.; Yang, Z.; Tian, H.; Chen, C.; Zhu, Y.; Deng, F.; Lu, S. Failure analysis for hydraulic system of heavy-duty machine tool with incomplete failure data. Appl. Sci. 2021, 11, 1249. [Google Scholar] [CrossRef]
- Si, S.; Dui, H.; Zhao, X.; Zhang, S.; Sun, S. Integrated importance measure of component states based on loss of system performance. IEEE Trans. Reliab. 2012, 61, 192–202. [Google Scholar] [CrossRef]
- Gong, X.; Ma, X.; Zhang, Y.; Yang, J. Application of fuzzy neural network in fault diagnosis for scraper conveyor vibration[C]//2013 IEEE International Conference on Information and Automation (ICIA). IEEE 2013, 1135–1140. [Google Scholar] [CrossRef]
- Bołoz, Ł.; Rak, Z.; Stasica, J. Comparative analysis of the failure rates of shearer and plow systems: A case study. Energies 2022, 15, 6170. [Google Scholar] [CrossRef]
- Birnbaum, Z.W. On the Importance of Different Components in a Multi-Component System; Academic Press: New York, NY, USA, 1969; pp. 581–592. [Google Scholar]
- Lambert, H.E. Fault Trees for Decision Making in Systems Analysis. Ph.D. Thesis, University of California, Livermore, CA, USA, 1975. [Google Scholar]
- Vesely, W.E. A time-dependent methodology for fault tree evaluation. Nucl. Eng. Des. 1970, 13, 337–360. [Google Scholar] [CrossRef]
- Fussell, J.B. How to hand-calculate system reliability and safety characteristics. IEEE Trans Reliab. 1975, R-24, 169–174. [Google Scholar] [CrossRef]
- Wu, D.; Lv, S.; Jiang, M.; Song, H. Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Comput. Electron. Agric. 2020, 178, 105742. [Google Scholar] [CrossRef]
References | Importance Measures | IM Ranking Verification |
---|---|---|
[15] | Birnbaum and Fussell–Vesely IM | ✕ |
[16] | Fuzzy IM | ✕ |
[17] | Global IM | ✓ |
[18] | Birnbaum IM | ✕ |
[19] | Reliability dynamic core IM | ✕ |
[20] | Fussell–Vesely IM | ✕ |
[21] | ✕ | ✕ |
[22] | ✕ | ✕ |
[23] | FOWT IM | ✕ |
[24] | Birnbaum and Criticality IM | ✕ |
[25] | Relative IM | ✕ |
Subsystem | Fault Phenomenon (Middle Events) | Fault Causes (Bottom Events) | Symbol |
---|---|---|---|
Rocker arm | Rocker arm shell fault | Fracture of ear or shell | C1 |
Unable to lift rocker arm | Abnormal output voltage | C2 | |
Overflow solenoid valve failure | C3 | ||
Lifting solenoid valve failure | C4 | ||
Oil clogging | C5 | ||
Cutting motor | Oil-gathering in motor cavity | Oil seal leakage | C6 |
Oil spill port blockage | C7 | ||
Motor overheating, leakage, short circuit | Short circuit between coils, water in connection cavity, dampened circuit | C8 | |
Leakage or open circuit of motor cable | C9 | ||
Cutting drum | Pick fault | Tool bit fragmentation, shedding, wear, or loss | C10 |
Tooth seat cracking | C11 | ||
Drum shell failure | Bolt fracture | C12 | |
Welds-opened roller | C13 | ||
Transmission | Gear tooth surface wear | Too high temperature of tooth surface contact point; oil film thermal bonding; gearbox lubrication deterioration | C14 |
Planetary gear fracture | Fatigue damage, gearbox dry friction or particle contamination | C15 | |
Gearbox planet carrier sealing failure | Floating seal damage or poor seal installation status | C16 | |
Reducer overheating | Nonconformity of oil varieties or improper oil quantity | C17 | |
Cooling spray function failure | C18 | ||
Bearing fault | Bearing wear | C19 | |
Bearing cage failure | C20 |
Subsystem | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | Average Failure Probability |
---|---|---|---|---|---|---|
Rocker arm | 2 | 2 | 6 | 3 | 8 | 0.029804 |
Cutting motor | 20 | 20 | 50 | 25 | 36 | 0.218441 |
Cutting drum | 14 | 19 | 4 | 2 | 18 | 0.112070 |
Transmission | 43 | 45 | 220 | 129 | 71 | 0.639686 |
Subsystem | Bottom Events | Failure Rate | Failure Times | Conditional Failure Probability | Subsystem Failure Probability | Failure Probability |
---|---|---|---|---|---|---|
Rocker arm | C1 | 1.10 × 10−6 | 39 | 0.475610 | 0.029804 | 0.014175 |
C2 | 3.70 × 10−7 | 5 | 0.060976 | 0.029804 | 0.001817 | |
C3 | 6.00 × 10−7 | 3 | 0.036585 | 0.029804 | 0.001090 | |
C4 | 6.00 × 10−7 | 2 | 0.024390 | 0.029804 | 0.000727 | |
C5 | 8.00 × 10−7 | 33 | 0.402439 | 0.029804 | 0.011994 | |
Cutting motor | C6 | 1.20 × 10−6 | 14 | 0.170732 | 0.218441 | 0.037295 |
C7 | 1.20 × 10−6 | 7 | 0.085366 | 0.218441 | 0.018647 | |
C8 | 5.80 × 10−7 | 50 | 0.609756 | 0.218441 | 0.133196 | |
C9 | 3.00 × 10−7 | 11 | 0.134146 | 0.218441 | 0.029303 | |
Cutting drum | C10 | 7.00 × 10−7 | 78 | 0.523490 | 0.112070 | 0.058667 |
C11 | 1.75 × 10−7 | 1 | 0.00671 | 0.112070 | 0.000752 | |
C12 | 2.20 × 10−6 | 69 | 0.463087 | 0.112070 | 0.051898 | |
C13 | 1.50 × 10−8 | 1 | 0.006711 | 0.112070 | 0.000752 | |
Transmission | C14 | 1.20 × 10−7 | 21 | 0.115385 | 0.639686 | 0.073810 |
C15 | 1.20 × 10−7 | 15 | 0.082418 | 0.639686 | 0.052721 | |
C16 | 8.00 × 10−8 | 14 | 0.076923 | 0.639686 | 0.049207 | |
C17 | 3.50 × 10−6 | 21 | 0.115385 | 0.639686 | 0.073810 | |
C18 | 2.70 × 10−6 | 40 | 0.219780 | 0.639686 | 0.140590 | |
C19 | 3.53 × 10−6 | 68 | 0.373626 | 0.639686 | 0.239004 | |
C20 | 5.00 × 10−7 | 3 | 0.016484 | 0.639686 | 0.010544 |
Bottom Events | IIM | Bottom Events | IIM |
---|---|---|---|
C1 | 3.99911 × 10−7 | C11 | 6.36222 × 10−8 |
C2 | 1.34516 × 10−7 | C12 | 7.99822 × 10−7 |
C3 | 2.18133 × 10−7 | C13 | 5.45333 × 10−9 |
C4 | 2.18133 × 10−7 | C14 | 4.36267 × 10−8 |
C5 | 2.90844 × 10−7 | C15 | 4.36266 × 10−8 |
C6 | 7.83712 × 10−9 | C16 | 2.90845 × 10−8 |
C7 | 1.59783 × 10−8 | C17 | 1.27244 × 10−6 |
C8 | 2.10862 × 10−7 | C18 | 9.81600 × 10−7 |
C9 | 1.09067 × 10−7 | C19 | 1.28335 × 10−6 |
C10 | 2.54489 × 10−7 | C20 | 1.81778 × 10−7 |
Bottom Event | IIM | BIM | CIM | FVIM |
---|---|---|---|---|
C1 | 3.99911 × 10−7 | 0.368783 | 0.008214 | 0.022272 |
C2 | 1.34516 × 10−7 | 0.364217 | 0.001040 | 0.002855 |
C3 | 2.18133 × 10−7 | 0.363952 | 0.000623 | 0.001713 |
C4 | 2.18133 × 10−7 | 0.36382 | 0.000416 | 0.001142 |
C5 | 2.90844 × 10−7 | 0.367969 | 0.006934 | 0.018845 |
C6 | 7.83712 × 10−9 | 0.006784 | 0.000398 | 0.001093 |
C7 | 1.59783 × 10−8 | 0.013568 | 0.000398 | 0.001093 |
C8 | 2.10862 × 10−7 | 0.419421 | 0.087777 | 0.209282 |
C9 | 1.09067 × 10−7 | 0.374530 | 0.017244 | 0.046042 |
C10 | 2.54489 × 10−7 | 0.386214 | 0.035601 | 0.092179 |
C11 | 6.36222 × 10−8 | 0.363829 | 0.000430 | 0.001182 |
C12 | 7.99822 × 10−7 | 0.383456 | 0.031268 | 0.081544 |
C13 | 5.45333 × 10−9 | 0.363829 | 0.000430 | 0.001182 |
C14 | 4.36267 × 10−8 | 0.392528 | 0.045522 | 0.115973 |
C15 | 4.36266 × 10−8 | 0.383789 | 0.031792 | 0.822837 |
C16 | 2.90845 × 10−8 | 0.382371 | 0.029563 | 0.077316 |
C17 | 1.27244 × 10−6 | 0.392528 | 0.045522 | 0.115973 |
C18 | 9.81600 × 10−7 | 0.423029 | 0.093447 | 0.2208997 |
C19 | 1.28335 × 10−6 | 0.477737 | 0.179404 | 0.375530 |
C20 | 1.81778 × 10−7 | 0.367430 | 0.006087 | 0.016567 |
Importance Measures | No. 1 | No. 2 | No. 3 | mAP |
---|---|---|---|---|
IIM | 0.94285714 | 0.89285714 | 1 | 94.52% |
BIM | 0.80833333 | 0.85416667 | 0.91666667 | 85.97% |
CIM | 0.80833333 | 0.85416667 | 0.91666667 | 85.97% |
FVIM | 0.59261905 | 0.58452381 | 0.58888889 | 58.87% |
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Zhao, J.-b.; Liang, M.-t.; Zhang, Z.-y.; Cui, J.; Cao, X.-g. Fault Analysis of Shearer-Cutting Units Driven by Integrated Importance Measure. Appl. Sci. 2023, 13, 2711. https://doi.org/10.3390/app13042711
Zhao J-b, Liang M-t, Zhang Z-y, Cui J, Cao X-g. Fault Analysis of Shearer-Cutting Units Driven by Integrated Importance Measure. Applied Sciences. 2023; 13(4):2711. https://doi.org/10.3390/app13042711
Chicago/Turabian StyleZhao, Jiang-bin, Meng-tao Liang, Zao-yan Zhang, Jian Cui, and Xian-gang Cao. 2023. "Fault Analysis of Shearer-Cutting Units Driven by Integrated Importance Measure" Applied Sciences 13, no. 4: 2711. https://doi.org/10.3390/app13042711
APA StyleZhao, J.-b., Liang, M.-t., Zhang, Z.-y., Cui, J., & Cao, X.-g. (2023). Fault Analysis of Shearer-Cutting Units Driven by Integrated Importance Measure. Applied Sciences, 13(4), 2711. https://doi.org/10.3390/app13042711