An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions
Abstract
:1. Introduction
2. Background of MP-SAX
3. The Background and Proposed Method
3.1. The Description of Background
3.2. The Proposed Method
4. The Description of Dataset and Problem in Life Prediction under Multiple Operating Conditions
4.1. The Description and Analysis of the Dataset
4.2. Evaluation Indicators of Prediction Results
4.3. The Description of Problems in Life Prediction under Multiple Operating Conditions
5. Results and Discussions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Acronyms | Meaning | Acronyms | Meaning |
---|---|---|---|
AI | Artificial intelligence | MP-SAX | Morphological pattern and symbolic aggregate approximation-based similarity measurement method |
CBM | Condition based maintenance | MP-SAX-STM | Similarity trajectory method based on morphological pattern and symbolic aggregate approximation |
CBR | Case-based reasoning | OP | Operating parameters |
CMAPSS | Commercial modular aero-propulsion system simulation | RMSE | Root mean square error |
DE | Detail component | RUL | Remaining useful life |
EMD | Empirical mode decomposition | S | Sensor |
IMF | Intrinsic mode function | SAX | Symbolic aggregate approximation |
LCS | Longest common subsequence | SC | Symbolic aggregate approximation symbol sequences |
MAE | Mean absolute error | SDE | Similarity of detail component |
MAPE | Mean absolute percentage error | STM | Similarity trajectory method |
MASE | Mean absolute scaled error | STR | Similarity of trend component |
MC | Morphological pattern symbol sequences | TR | Trend component |
MP | Morphological pattern | - | - |
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Set | Operating Condition | Failure Mode |
---|---|---|
#1 | 1 | 1 |
#2 | 6 | 1 |
#3 | 1 | 2 |
#4 | 6 | 2 |
Cycle | OP 1 | OP 2 | OP 3 | S 1 | S 2 | … | S 21 |
---|---|---|---|---|---|---|---|
1 | 42.0049 | 0.8400 | 100 | 445.00 | 549.68 | … | 6.3670 |
2 | 20.0020 | 0.7002 | 100 | 491.19 | 606.07 | … | 14.6550 |
⁝ | ⁝ | ⁝ | ⁝ | ⁝ | ⁝ | ⁝ | |
5 | 25.0063 | 0.6207 | 60 | 462.54 | 536.10 | … | 8.6754 |
6 | 34.9996 | 0.8400 | 100 | 449.44 | 554.77 | … | 8.9057 |
7 | 0.0019 | 10−4 | 100 | 518.67 | 641.83 | … | 23.4578 |
⁝ | ⁝ | ⁝ | ⁝ | ⁝ | ⁝ | ⁝ | |
17 | 9.9989 | 0.2506 | 100 | 489.05 | 603.80 | … | 17.1975 |
⁝ | ⁝ | ⁝ | ⁝ | ⁝ | ⁝ | ⁝ | |
321 | 42.0058 | 0.8400 | 100 | 445.00 | 549.71 | … | 6.4590 |
Indicators | 1/2 | 2/3 | 3/4 | 4/5 |
---|---|---|---|---|
MAE | 23.71% | 24.48% | 22.28% | 21.74% |
RMSE | 22.35% | 22.32% | 23.20% | 21.78% |
MAPE | 25.21% | 25.13% | 22.50% | 22.63% |
MASE | 23.71% | 24.48% | 22.28% | 21.74% |
Indicators | 1/2 | 2/3 | 3/4 | 4/5 |
---|---|---|---|---|
MAE | 9.30% | 13.69% | 13.51% | 16.70% |
RMSE | 8.65% | 11.38% | 11.49% | 12.36% |
MAPE | 8.67% | 13.45% | 12.82% | 13.30% |
MASE | 9.30% | 13.69% | 13.51% | 16.70% |
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Yin, J.; Li, Y.; Wang, R.; Xu, M. An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions. Appl. Sci. 2021, 11, 10968. https://doi.org/10.3390/app112210968
Yin J, Li Y, Wang R, Xu M. An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions. Applied Sciences. 2021; 11(22):10968. https://doi.org/10.3390/app112210968
Chicago/Turabian StyleYin, Jiancheng, Yuqing Li, Rixin Wang, and Minqiang Xu. 2021. "An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions" Applied Sciences 11, no. 22: 10968. https://doi.org/10.3390/app112210968
APA StyleYin, J., Li, Y., Wang, R., & Xu, M. (2021). An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions. Applied Sciences, 11(22), 10968. https://doi.org/10.3390/app112210968