scholarly journals Improving EGT sensing data anomaly detection of aircraft auxiliary power unit

2020 ◽  
Vol 33 (2) ◽  
pp. 448-455 ◽  
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

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

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.

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

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3935 ◽  
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.

Approximation of Payback Period of Fuel Cell Auxiliary Power Unit for Large Trucks

2009 ◽  
Vol 129 (2) ◽  
pp. 228-229
Author(s):  
Noboru Katayama ◽  
Hideyuki Kamiyama ◽  
Yusuke Kudo ◽  
Sumio Kogoshi ◽  
Takafumi Fukada
Keyword(s):  
Fuel Cell ◽  
Power Unit ◽  
Payback Period ◽  
Auxiliary Power Unit ◽  
Auxiliary Power

Electric auxiliary power unit for Shuttle evolution

1989 ◽  
Author(s):  
DOUG MEYER ◽  
KENT WEBER ◽  
WALTER SCOTT
Keyword(s):  
Power Unit ◽  
Auxiliary Power Unit ◽  
Auxiliary Power

Low-Emissions Technology Development for Auxiliary Power Unit Combustion Systems

2021 ◽  
Author(s):  
Thomas Bronson ◽  
Rudy Dudebout ◽  
Nagaraja Rudrapatna
Keyword(s):  
Gas Turbine ◽  
Power Unit ◽  
Fuel Injection ◽  
Oxides Of Nitrogen ◽  
Combustion System ◽  
Auxiliary Power Unit ◽  
Criteria Pollutants ◽  
Auxiliary Power ◽  
Combustion Systems ◽  
Aircraft Application

Abstract The aircraft Auxiliary Power Unit (APU) is required to provide power to start the main engines, conditioned air and power when there are no facilities available and, most importantly, emergency power during flight operation. Given the primary purpose of providing backup power, APUs have historically been designed to be extremely reliable while minimizing weight and fabrication cost. Since APUs are operated at airports especially during taxi operations, the emissions from the APUs contribute to local air quality. There is clearly significant regulatory and public interest in reducing emissions from all sources at airports, including from APUs. As such, there is a need to develop technologies that reduce criteria pollutants, namely oxides of nitrogen (NOx), unburned hydrocarbons (UHC), carbon monoxide (CO) and smoke (SN) from aircraft APUs. Honeywell has developed a Low-Emissions (Low-E) combustion system technology for the 131-9 and HGT750 family of APUs to provide significant reduction in pollutants for narrow-body aircraft application. This article focuses on the combustor technology and processes that have been successfully utilized in this endeavor, with an emphasis on abating NOx. This paper describes the 131-9/HGT750 APU, the requirements and challenges for small gas turbine engines, and the selected strategy of Rich-Quench-Lean (RQL) combustion. Analytical and experimental results are presented for the current generation of APU combustion systems as well as the Low-E system. The implementation of RQL aerodynamics is well understood within the aero-gas turbine engine industry, but the application of RQL technology in a configuration with tangential liquid fuel injection which is also required to meet altitude ignition at 41,000 ft is the novelty of this development. The Low-E combustion system has demonstrated more than 25% reduction in NOx (dependent on the cycle of operation) vs. the conventional 131-9 combustion system while meeting significant margins in other criteria pollutants. In addition, the Low-E combustion system achieved these successes as a “drop-in” configuration within the existing envelope, and without significantly impacting combustor/turbine durability, combustor pressure drop, or lean stability.

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