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.

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

2020 ◽  
Vol 20 (14) ◽  
pp. 7848-7858 ◽  
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

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

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.

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

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.

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

Aircraft Fleet Maintenance Planning Using Combined Cost Benefit Model and Branch and Bound

2013 ◽  
Author(s):  
Behnam Razavi ◽  
Farrokh Sassani
Keyword(s):  
Branch And Bound ◽  
Health Management ◽  
Cost Benefit ◽  
Remaining Useful Life ◽  
Maintenance Planning ◽  
Flight Delays ◽  
Engine Operation ◽  
Maintenance And Repair ◽  
Flight Schedules ◽  
Useful Life

The tasks of maintenance and repair without optimal planning can be costly and result in prolonged maintenance times, reduced availability and possible flight delays. Aircraft manufacturers and maintainers see significant benefits in constantly improving Health Management and Maintenance (HMM) practices by deploying the most effective maintenance planning strategies. The planning of the maintenance and repair is a complex task due to chain dependency of engines to aircraft, and aircraft to the flight schedules. This paper presents a scheduling method for determining the time of maintenance based on the historical engine operation data in order to maximize the use of estimated remaining useful life of the engines as well as lowering the cost and duration of the downtime. The Time-on-Wing (TOW) data is used in conjunction with probability density functions to determine the shape of the respective distribution of the time of maintenance to minimize the loss of expected remaining useful life. Data from each engine with most chance of failure is then selected and fed into an extended Branch and Bound (B&B) routine to determine the best optimum sequence for entering the facility in order to minimize the waiting time.

scholarly journals Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction

2019 ◽  
Author(s):  
Sunny Singh ◽  
Praneet Shiv ◽  
Atif Ahmed
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
System Failure ◽  
Mechanical Signal ◽  
Long Time ◽  
Data Points ◽  
Useful Life ◽  
Bearing System

In this paper, we introduce the Prognostics and Health Management of gear bearing system using autoencoder neural networks. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. This study presents an outlier modeling method for forecasting the gear bearing system failure using the health indicators constructed from mechanical signal processing and modeling. Outlier modeling aims to find patterns in data that are significantly different from what is defined as normal. In the unsupervised outlier modeling setting, prior labels about the anomalousness of data points are not available. In such cases, the most common techniques for scoring data points for outlyingness include distance-based methods density-based methods, and linear methods. The conventional outlier modeling methods have been used for a long time to detect anomalous observations in data. However, this paper shows that autoencoders are a very competitive technique compared to other existing methods. The developed method is demonstrated using the IMS bearing data from NASA Acoustics and Vibration Database.

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.

scholarly journals Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878420 ◽  
Author(s):  
Shu-Fa Yan ◽  
Biao Ma ◽  
Chang-Song Zheng
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Cumulative Distribution ◽  
Kernel Principal Component Analysis ◽  
Degradation Index ◽  
Life Estimation ◽  
Power Shift ◽  
Useful Life

Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process–based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.

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