Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer
Abstract
:1. Introduction
2. Problem Statements and Fault Diagnosis Objectives
3. Mathematical Modeling of REBs
4. Proposed Method
4.1. ARX-Laguerre Proportional-Integral Observer (APIO)
4.2. Proposed Higher-Order Super-Twisting Sliding Mode Observer (HOSTSMO)
5. Datasets, Results, and Analysis
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Parameters | Value |
---|---|
Number of balls | 9 |
Stiffness of ball | 5.96 × 107 () |
Mass of outer (Kg) | 2.7 (Kg) |
Stiffness of outer | 1.31 × 105 () |
Mass of shaft (Kg) | 1.36 (Kg) |
Stiffness of Shaft | 23.3 × 106 () |
Damping | 654 () |
Ball diameter | |
Pitch diameter | |
Defect size | |
Defect depth |
Dataset | Fault Types | Load (hp) | Fault Crack Sizes (in) |
---|---|---|---|
Dataset 1 | Normal state | 0 | 0.007, 0.014, and 0.021 |
IR fault states | 0 | ||
OR fault states | 0 | ||
Ball fault states | 0 | ||
Dataset 2 | Normal state | 1 | 0.007, 0.014, and 0.021 |
IR fault states | 1 | ||
OR fault states | 1 | ||
Ball fault states | 1 | ||
Dataset 3 | Normal state | 2 | 0.007, 0.014, and 0.021 |
IR fault states | 2 | ||
OR fault states | 2 | ||
Ball fault states | 2 | ||
Dataset 4 | Normal state | 3 | 0.007, 0.014, and 0.021 |
IR fault states | 3 | ||
OR fault states | 3 | ||
Ball fault states | 3 |
Algorithms | Proposed Method | ALPIO | ||||
---|---|---|---|---|---|---|
Crack Diameters (in) | 0.007 | 0.014 | 0.021 | 0.007 | 0.014 | 0.021 |
Normal State | 100% | 100% | 100% | 89% | 89% | 89% |
IR Faults | 96% | 93% | 96% | 66% | 70% | 70% |
OR Fault | 100% | 100% | 100% | 75% | 80% | 78% |
Ball Fault | 100% | 100% | 100% | 81% | 81% | 84% |
Average | 99% | 98.3% | 99% | 78% | 80% | 80.3% |
Algorithms | Proposed Method | ALPIO | ||||
---|---|---|---|---|---|---|
Crack Diameters (in) | 0.007 | 0.014 | 0.021 | 0.007 | 0.014 | 0.021 |
Normal State | 100% | 100% | 100% | 89% | 89% | 89% |
IR Faults | 100% | 100% | 100% | 66% | 70% | 70% |
OR Fault | 95% | 93% | 95% | 75% | 80% | 78% |
Ball Fault | 98% | 90% | 98% | 81% | 81% | 84% |
Average | 98.3% | 95.7% | 98.3% | 78% | 80% | 80.3% |
Algorithms | Proposed Method | ALPIO | ||||
---|---|---|---|---|---|---|
Crack Diameters (in) | 0.007 | 0.014 | 0.021 | 0.007 | 0.014 | 0.021 |
Normal State | 100% | 100% | 100% | 85% | 85% | 85% |
IR Faults | 100% | 100% | 100% | 73% | 70% | 75% |
OR Fault | 92% | 85% | 95% | 75% | 75% | 75% |
Ball Fault | 93% | 90% | 90% | 78% | 81% | 81% |
Average | 96.3% | 93.8% | 96.3% | 77.8% | 77.8% | 79% |
Algorithms | Proposed Method | ALPIO | ||||
---|---|---|---|---|---|---|
Crack Diameters (in) | 0.007 | 0.014 | 0.021 | 0.007 | 0.014 | 0.021 |
Normal State | 100% | 100% | 100% | 90% | 90% | 90% |
IR Faults | 94% | 100% | 100% | 75% | 75% | 75% |
OR Fault | 90% | 90% | 90% | 75% | 75% | 78% |
Ball Fault | 92% | 86% | 90% | 78% | 75% | 81% |
Average | 94% | 94% | 95% | 79.5% | 78.75% | 81% |
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Piltan, F.; Kim, J.-M. Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer. Sensors 2018, 18, 1128. https://doi.org/10.3390/s18041128
Piltan F, Kim J-M. Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer. Sensors. 2018; 18(4):1128. https://doi.org/10.3390/s18041128
Chicago/Turabian StylePiltan, Farzin, and Jong-Myon Kim. 2018. "Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer" Sensors 18, no. 4: 1128. https://doi.org/10.3390/s18041128
APA StylePiltan, F., & Kim, J.-M. (2018). Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer. Sensors, 18(4), 1128. https://doi.org/10.3390/s18041128