Fault Diagnosis of Uninterruptible Power System Based on Gaussian Mixed Model and XGBoost

Chen, Hongyu; Peng, Yanqing; Yang, Qiuyu; Yan, Lei

doi:10.1109/iccse49874.2020.9201873

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…In this study, we employed the XGBoost (eXtreme Gradient Boost) algorithm for fault classification modeling [21,22]. The XGBoost algorithm is based on the gradient-boosted decision tree (GBDT) and encompasses the advantages of both bagging and boosting, representing an enhanced algorithm.…”

Section: Basics Of Phm Methodologymentioning

confidence: 99%

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The Optimization of a Model for Predicting the Remaining Useful Life and Fault Diagnosis of Landing Gear

Chang,

Hsu,

Hsu

et al. 2023

Aerospace

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With the development of next-generation airplanes, the complexity of equipment has increased rapidly, and traditional maintenance solutions have become cost-intensive and time-consuming. Therefore, the main objective of this study is to adopt predictive maintenance techniques in daily maintenance in order to reduce manpower, time, and the cost of maintenance, as well as increase aircraft availability. The landing gear system is an important component of an aircraft. Wear and tear on the parts of the landing gear may result in oscillations during take-off and landing rolling and even affect the safety of the fuselage in severe cases. This study acquires vibration signals from the flight data recorder and uses prognostic and health management technology to evaluate the health indicators (HI) of the landing gear. The HI is used to monitor the health status and predict the remaining useful life (RUL). The RUL prediction model is optimized through hyperparameter optimization and using the random search algorithm. Using the RUL prediction model, the health status of the landing gear can be monitored, and adaptive maintenance can be carried out. After the optimization of the RUL prediction model, the root-mean-square errors of the three RUL prediction models, that is, the autoregressive model, Gaussian process regression, and the autoregressive integrated moving average, decreased by 45.69%, 55.18%, and 1.34%, respectively. In addition, the XGBoost algorithm is applied to simultaneously output multiple fault types. This model provides a more realistic representation of the actual conditions under which an aircraft might exhibit multiple faults. With an optimal fault diagnosis model, when an anomaly is detected in the landing gear, the faulty part can be quickly diagnosed, thus enabling faster and more adaptive maintenance. The optimized multi-fault diagnosis model proposed in this study achieves average accuracy, a precision rate, a recall rate, and an F1 score of more than 96.8% for twenty types of faults.

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Section: Basics Of Phm Methodologymentioning

confidence: 99%

“…However, defining a formula to discover the optimal hyperparameters is challenging. Some optimization strategies, such as manual search, grid search, random search, and Bayesian optimization, have been proposed [18][19][20][21][22][23]. In the current study, random search was used for RUL model optimization.…”

Section: Optimization Of the Rul Modelmentioning

confidence: 99%