Remaining Useful Life Prediction of Aircraft Auxiliary Power Unit with On-wing Sensing Data

The in-situ prognostics and health management of aircraft auxiliary power unit faces difficulty using the sparse on-wing sensing data. As the key technology of prognostics and health management, remaining useful life prediction of in-situ aircraft auxiliary power unit is hard to achieve accurate results. To solve this problem, we propose one kind of quantitative analysis of its on-wing sensing data to implement remaining useful life prediction of auxiliary power unit. Except the most important performance parameter exhaust gas temperature, the other potential parameters are utilized based on mutual information, which can be used as the quantitative metric. In this way, the quantitative threshold of mutual information for enhancing remaining useful life prediction result can be determined. The implemented cross-validation experiments verify the effectiveness of the proposed method. The real on-wing sensing data of auxiliary power unit for experiment are from China Southern Airlines Company Limited Shenyang Maintenance Base, which spends over $6.5 million on auxiliary power unit maintenance and repair each year for the fleet of over 500 aircrafts. Although the relative improvement is not too large, it is helpful to reduce the maintenance and repair cost.

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A Hybrid Method of Remaining Useful Life Prediction for Aircraft Auxiliary Power Unit

IEEE Sensors Journal ◽

10.1109/jsen.2020.2979797 ◽

2020 ◽

Vol 20 (14) ◽

pp. 7848-7858 ◽

Cited By ~ 2

Author(s):

Xiaolei Liu ◽

Liansheng Liu ◽

Datong Liu ◽

Lulu Wang ◽

Qing Guo ◽

...

Keyword(s):

Power Unit ◽

Hybrid Method ◽

Life Prediction ◽

Remaining Useful Life ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

Useful Life

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Remaining useful life prediction for auxiliary power unit based on particle filter

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410020940882 ◽

2020 ◽

Vol 234 (15) ◽

pp. 2211-2217

Author(s):

Jiachen Guo ◽

Jing Cai ◽

Heng Jiang ◽

Xin Li

Keyword(s):

Particle Filter ◽

Power Unit ◽

Engineering Application ◽

Remaining Useful Life ◽

Civil Aviation ◽

Recursive Feature Elimination ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

High Prediction ◽

Useful Life

Auxiliary power unit is one of the indispensable systems for civil aviation aircraft but the traditional planned maintenance cannot meet the actual needs of airlines. In this work, the key performance parameters of the auxiliary power unit are selected by using recursive feature elimination method. With the selected parameters, the remaining useful flight cycle of the auxiliary power unit is predicted by applying particle filter techniques. Some improved algorithms such as Gaussian particle filter and auxiliary particle filter are also compared. The experimental results demonstrate that the particle filter-based method has high prediction accuracy and engineering application value.

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Aircraft auxiliary power unit performance assessment and remaining useful life evaluation for predictive maintenance

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650919883718 ◽

2019 ◽

Vol 234 (6) ◽

pp. 804-816

Author(s):

Fangyuan Wang ◽

Jianzhong Sun ◽

Xinchao Liu ◽

Cui Liu

Keyword(s):

Power Unit ◽

Performance Degradation ◽

Remaining Useful Life ◽

Commercial Aircraft ◽

Baseline Model ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

Auxiliary Power Units ◽

Useful Life ◽

Condition Monitoring System

Modern commercial aircraft are usually configured with aircraft condition monitoring system to collect the operating data of subsystems and components, which can be used for airborne system health monitoring and predictive maintenance. This paper presents a baseline model based aircraft auxiliary power unit performance assessment and remaining useful life prediction method using aircraft condition monitoring system reports data, which can facilitate a cost-effective management of auxiliary power units of aircraft fleet. Firstly, the performance baseline model for auxiliary power unit is established using random forest method. Then a health index characterizing the performance degradation of in-service auxiliary power units is obtained based on the performance baseline model. Finally, the performance degradation trend is predicted using Bayesian dynamic linear model. To improve the prediction accuracy, four performance baseline models are established from the data of auxiliary power units under different operating conditions, among which an optimal model is determined. This data-driven baseline model can be used to quantify the performance degradation of auxiliary power units in service, and can be further used to evaluate the remaining useful life of auxiliary power unit using a Bayesian dynamic model. The developed approach is applied on a real data set from 22 auxiliary power units of a commercial aircraft fleet. The results show that the computed health index can effectively characterize the auxiliary power units performance degradation and the remaining useful life relative prediction errors are less than 4% when auxiliary power unit enters the rapid degradation stage. This would allow operators to accurately assess the performance degradation for the auxiliary power units and further proactively plan future maintenance events based on remaining useful life prediction.

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Improving EGT sensing data anomaly detection of aircraft auxiliary power unit

Chinese Journal of Aeronautics ◽

10.1016/j.cja.2019.10.001 ◽

2020 ◽

Vol 33 (2) ◽

pp. 448-455 ◽

Cited By ~ 6

Author(s):

Liansheng LIU ◽

Yu PENG ◽

Lulu WANG ◽

Yu DONG ◽

Datong LIU ◽

...

Keyword(s):

Anomaly Detection ◽

Power Unit ◽

Sensing Data ◽

Auxiliary Power Unit ◽

Auxiliary Power

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Exhaust Gas Temperature Sensing Data Anomaly Detection for Aircraft Auxiliary Power Unit Condition Monitoring

2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC) ◽

10.1109/sdpc.2018.8664831 ◽

2018 ◽

Cited By ~ 2

Author(s):

Lulu Wang ◽

Ming Li ◽

Liansheng Liu ◽

Datong Liu

Keyword(s):

Anomaly Detection ◽

Power Unit ◽

Condition Monitoring ◽

Temperature Sensing ◽

Exhaust Gas ◽

Gas Temperature ◽

Sensing Data ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

Exhaust Gas Temperature

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Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine

Sensors ◽

10.3390/s19183935 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3935 ◽

Cited By ~ 3

Author(s):

Xiaolei Liu ◽

Liansheng Liu ◽

Lulu Wang ◽

Qing Guo ◽

Xiyuan Peng

Keyword(s):

Neural Network ◽

Extreme Learning Machine ◽

Power Unit ◽

Back Propagation ◽

Performance Parameter ◽

Sensing Data ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

Learning Machine ◽

Parameter Prediction

The aircraft auxiliary power unit (APU) is responsible for environmental control in the cabin and the main engines starting the aircraft. The prediction of its performance sensing data is significant for condition-based maintenance. As a complex system, its performance sensing data have a typically nonlinear feature. In order to monitor this process, a model with strong nonlinear fitting ability needs to be formulated. A neural network has advantages of solving a nonlinear problem. Compared with the traditional back propagation neural network algorithm, an extreme learning machine (ELM) has features of a faster learning speed and better generalization performance. To enhance the training of the neural network with a back propagation algorithm, an ELM is employed to predict the performance sensing data of the APU in this study. However, the randomly generated weights and thresholds of the ELM often may result in unstable prediction results. To address this problem, a restricted Boltzmann machine (RBM) is utilized to optimize the ELM. In this way, a stable performance parameter prediction model of the APU can be obtained and better performance parameter prediction results can be achieved. The proposed method is evaluated by the real APU sensing data of China Southern Airlines Company Limited Shenyang Maintenance Base. Experimental results show that the optimized ELM with an RBM is more stable and can obtain more accurate prediction results.

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