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

Auxiliary power unit (APU) operators face increasingly stricter airport requirements concerning exhaust gas and noise emission levels. To simultaneously reduce exhaust gas and noise emissions and to satisfy the increasing demand of electric power on board, optimization of the current technology is necessary. Prior to any possible demonstration of optimization potential, detailed data of thermodynamic properties and emissions have to be determined. Therefore, the investigations presented in this paper were conducted at a full-scale APU of an operational aircraft. A Pratt & Whitney (East Hartford, CT) APS3200, commonly installed in the Airbus A320 aircraft family, was used for measurements of the reference data. In order to describe the APS3200, the full spectrum of feasible power load and bleed air mass flow combinations were adjusted during the study. Their effect on different thermodynamic and performance properties, such as exhaust gas temperature, pressure as well as electric and overall efficiency is described. Furthermore, the mass flows of the inlet air, exhaust gas, and fuel input were determined. Additionally, the work reports the exhaust gas emissions regarding the species CO2, CO, and NOx as a function of load point. Moreover, the acoustic noise emissions are presented and discussed. With the provided data, the paper serves as a database for validating numerical simulations and provides a baseline for current APU technology.

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Experimental Investigation of Cycle Properties, Noise and Air Pollutant Emissions of an APS3200 Auxiliary Power Unit

Volume 1: Aircraft Engine; Fans and Blowers; Marine; Honors and Awards ◽

10.1115/gt2017-63523 ◽

2017 ◽

Cited By ~ 1

Author(s):

Teresa Siebel ◽

Jan Zanger ◽

Andreas Huber ◽

Manfred Aigner ◽

Karsten Knobloch ◽

...

Keyword(s):

Power Unit ◽

Acoustic Noise ◽

Air Pollutant ◽

Pollutant Emissions ◽

Exhaust Gas ◽

Gas Temperature ◽

Noise Emission ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

Power Load

Auxiliary power unit (APU) operators face increasingly stricter airport requirements concerning exhaust gas and noise emission levels. To simultaneously reduce exhaust gas and noise emissions and to satisfy the increasing demand of electric power on board, optimization of the current technology is necessary. Prior to any possible demonstration of optimization potential, detailed data of thermodynamic properties and emissions have to be determined. Therefore, the investigations presented in this paper were conducted at a full-scale APU of an operational aircraft. A Pratt & Whitney APS3200, commonly installed in the Airbus A320 aircraft family, was used for measurements of the reference data. In order to describe the APS3200, the full spectrum of feasible power load and bleed air mass flow combinations were adjusted during the study. Their effect on different thermodynamic and performance properties, such as exhaust gas temperature, pressure as well as electric and overall efficiency is described. Furthermore, the mass flows of the inlet air, exhaust gas and fuel input were determined. Additionally, the work reports the exhaust gas emissions regarding the species CO2, CO and NOx as a function of load point. Moreover the acoustic noise emissions are presented and discussed. With the provided data the paper serves as a database for validating numerical simulations and provides a baseline for current APU technology.

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In-situ remaining useful life prediction of aircraft auxiliary power unit based on quantitative analysis of on-wing sensing data

Advances in Mechanical Engineering ◽

10.1177/1687814020911475 ◽

2020 ◽

Vol 12 (3) ◽

pp. 168781402091147

Author(s):

Liansheng Liu ◽

Qing Guo ◽

Lulu Wang ◽

Datong Liu

Keyword(s):

Power Unit ◽

Life Prediction ◽

Health Management ◽

Remaining Useful Life ◽

Maintenance And Repair ◽

Sensing Data ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

Useful Life

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

2018 Prognostics and System Health Management Conference (PHM-Chongqing) ◽

10.1109/phm-chongqing.2018.00044 ◽

2018 ◽

Cited By ~ 2

Author(s):

Liansheng Liu ◽

Lulu Wang ◽

Shaonian Wang ◽

Daotong Liu ◽

Yu Peng

Keyword(s):

Power Unit ◽

Life Prediction ◽

Remaining Useful Life ◽

Sensing Data ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

Useful Life

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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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