Fatigue life estimation of fixed-wing unmanned aerial vehicle engine by grey forecasting
To avoid infrared or thermal signatures of the fixed-wing unmanned aerial vehicle, the engine is encapsulated in a special cowling that limits the ventilation and causes thermal stress. The stressed condition heats up the engine and accelerates the degradation process compromising life and causing e...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-05-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/0020294020915215 |
| _version_ | 1819144590940176384 |
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| author | Noor Muhammad Zhigeng Fang Yingsai Cao |
| author_facet | Noor Muhammad Zhigeng Fang Yingsai Cao |
| author_sort | Noor Muhammad |
| collection | DOAJ |
| description | To avoid infrared or thermal signatures of the fixed-wing unmanned aerial vehicle, the engine is encapsulated in a special cowling that limits the ventilation and causes thermal stress. The stressed condition heats up the engine and accelerates the degradation process compromising life and causing early failure. Fatigue life estimation can help to predict and prevent sudden failure and improve safety and reliability. The study presents a grey forecasting methodology for estimating the fatigue life of fixed-wing unmanned aerial vehicle engines operating under a stressed environment. Grey forecasting models are used for fatigue life estimation of the unmanned aerial vehicle engine using degradation data of output power for reliable flight hours (50 h). The result of grey forecasting models reveals that under normal operation, engine power drops to a threshold value of 9.4 kW (below this engine does not remain flight worthy) after 100 h. The forecasted life is in close agreement with the specification of the engine under normal operating conditions. This validates the accuracy of forecasting models. Furthermore, the forecast models are applied to estimate the fatigue life using degradation data in a stressed environment, which comes out to be 70 h. The study proposes application of grey forecasting to predict mechanical degradation and early failures by considering single or multiple parameters undergoing degradation and having limited data samples. Forecasting results are compared with other prediction tools like autoregressive–moving-average and found more accurate which shows the significance of grey forecasting models in a limited data sample environment. The results are also compared with exponential regression and found in close agreement but more robust. |
| first_indexed | 2024-12-22T12:44:34Z |
| format | Article |
| id | doaj.art-18595891161b48df878adcb66999869f |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-12-22T12:44:34Z |
| publishDate | 2021-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-18595891161b48df878adcb66999869f2022-12-21T18:25:20ZengSAGE PublishingMeasurement + Control0020-29402021-05-015410.1177/0020294020915215Fatigue life estimation of fixed-wing unmanned aerial vehicle engine by grey forecastingNoor MuhammadZhigeng FangYingsai CaoTo avoid infrared or thermal signatures of the fixed-wing unmanned aerial vehicle, the engine is encapsulated in a special cowling that limits the ventilation and causes thermal stress. The stressed condition heats up the engine and accelerates the degradation process compromising life and causing early failure. Fatigue life estimation can help to predict and prevent sudden failure and improve safety and reliability. The study presents a grey forecasting methodology for estimating the fatigue life of fixed-wing unmanned aerial vehicle engines operating under a stressed environment. Grey forecasting models are used for fatigue life estimation of the unmanned aerial vehicle engine using degradation data of output power for reliable flight hours (50 h). The result of grey forecasting models reveals that under normal operation, engine power drops to a threshold value of 9.4 kW (below this engine does not remain flight worthy) after 100 h. The forecasted life is in close agreement with the specification of the engine under normal operating conditions. This validates the accuracy of forecasting models. Furthermore, the forecast models are applied to estimate the fatigue life using degradation data in a stressed environment, which comes out to be 70 h. The study proposes application of grey forecasting to predict mechanical degradation and early failures by considering single or multiple parameters undergoing degradation and having limited data samples. Forecasting results are compared with other prediction tools like autoregressive–moving-average and found more accurate which shows the significance of grey forecasting models in a limited data sample environment. The results are also compared with exponential regression and found in close agreement but more robust.https://doi.org/10.1177/0020294020915215 |
| spellingShingle | Noor Muhammad Zhigeng Fang Yingsai Cao Fatigue life estimation of fixed-wing unmanned aerial vehicle engine by grey forecasting Measurement + Control |
| title | Fatigue life estimation of fixed-wing unmanned aerial vehicle engine by grey forecasting |
| title_full | Fatigue life estimation of fixed-wing unmanned aerial vehicle engine by grey forecasting |
| title_fullStr | Fatigue life estimation of fixed-wing unmanned aerial vehicle engine by grey forecasting |
| title_full_unstemmed | Fatigue life estimation of fixed-wing unmanned aerial vehicle engine by grey forecasting |
| title_short | Fatigue life estimation of fixed-wing unmanned aerial vehicle engine by grey forecasting |
| title_sort | fatigue life estimation of fixed wing unmanned aerial vehicle engine by grey forecasting |
| url | https://doi.org/10.1177/0020294020915215 |
| work_keys_str_mv | AT noormuhammad fatiguelifeestimationoffixedwingunmannedaerialvehicleenginebygreyforecasting AT zhigengfang fatiguelifeestimationoffixedwingunmannedaerialvehicleenginebygreyforecasting AT yingsaicao fatiguelifeestimationoffixedwingunmannedaerialvehicleenginebygreyforecasting |