Jet engine life prediction systems integrated with prognostics health management

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

10.26434/chemrxiv.8378342 ◽

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.

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

Advances in Mechanical Engineering ◽

10.1177/1687814018784201 ◽

2018 ◽

Vol 10 (6) ◽

pp. 168781401878420 ◽

Cited By ~ 5

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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Residual Life Prediction of Jet Engine Rotor Disks

Design of Fatigue and Fracture Resistant Structures ◽

10.1520/stp28870s ◽

2009 ◽

pp. 408-408-16 ◽

Cited By ~ 1

Author(s):

M Zako ◽

T Kawashima ◽

H Aono ◽

K Jimboh ◽

H Ohnabe ◽

...

Keyword(s):

Life Prediction ◽

Residual Life ◽

Jet Engine ◽

Residual Life Prediction ◽

Engine Rotor

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A Framework of Prognosis and Health Management: A Multidisciplinary Approach

Volume 1: Turbo Expo 2007 ◽

10.1115/gt2007-27953 ◽

2007 ◽

Cited By ~ 2

Author(s):

Jeff Bird ◽

Xijia Wu ◽

Prakash Patnaik ◽

Azzedine Dadouche ◽

Sylvain Le´tourneau ◽

...

Keyword(s):

Information Management ◽

Life Prediction ◽

Information Technologies ◽

Multidisciplinary Approach ◽

Health Management ◽

Legacy Systems ◽

Prognostics And Health Management ◽

Functional Blocks ◽

Multi Disciplinary Team ◽

Future Plans

A multi-disciplinary team covering mechanical, materials and information technologies are investigating and developing appropriate technologies for aircraft prognostics and health management (PHM) applications. To integrate the efforts, a framework for PHM has been developed with three major functional blocks: Life Prediction, State Awareness, and Information Management. This framework provides a hierarchy for development and implementation. Achievements and future plans within this framework are presented with specific applications to military legacy systems and future acquisitions. Opportunities are described within some new initiatives for development and implementation.

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Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine

Data ◽

10.3390/data3040049 ◽

2018 ◽

Vol 3 (4) ◽

pp. 49 ◽

Cited By ~ 3

Author(s):

Faisal Khan ◽

Omer Eker ◽

Atif Khan ◽

Wasim Orfali

Keyword(s):

Neural Network ◽

Particle Filter ◽

Health Management ◽

Cost Effective ◽

Remaining Useful Life ◽

Operating Conditions ◽

Data Driven ◽

Prediction Errors ◽

Degradation Model ◽

Jet Engine

In the aerospace industry, every minute of downtime because of equipment failure impacts operations significantly. Therefore, efficient maintenance, repair and overhaul processes to aid maximum equipment availability are essential. However, scheduled maintenance is costly and does not track the degradation of the equipment which could result in unexpected failure of the equipment. Prognostic Health Management (PHM) provides techniques to monitor the precise degradation of the equipment along with cost-effective reliability. This article presents an adaptive data-driven prognostics reasoning approach. An engineering case study of Turbofan Jet Engine has been used to demonstrate the prognostic reasoning approach. The emphasis of this article is on an adaptive data-driven degradation model and how to improve the remaining useful life (RUL) prediction performance in condition monitoring of a Turbofan Jet Engine. The RUL prediction results show low prediction errors regardless of operating conditions, which contrasts with a conventional data-driven model (a non-parameterised Neural Network model) where prediction errors increase as operating conditions deviate from the nominal condition. In this article, the Neural Network has been used to build the Nominal model and Particle Filter has been used to track the present degradation along with degradation parameter.

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Analysis of Aircraft Composite Structural Health Monitoring and Life Prediction Technology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.88-89.515 ◽

2011 ◽

Vol 88-89 ◽

pp. 515-523

Author(s):

Xiao Chuang Tao ◽

Chen Lu

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Life Prediction ◽

Health Management ◽

Structure Design ◽

Prognostics And Health Management ◽

Aircraft Structure ◽

Reliability Design ◽

Structural Health ◽

Critical Issues

Along with the constantly updated aircraft structure design, higher performance and reliability design indexes as well as usage of a large portion of new materials especially lightweight composite materials put forward higher requirements for aircraft structure safety. The damage detection, diagnosis, forecast and management become an important part of aircraft Prognostics and Health Management(PHM).In order to better build the Structural Prognostics and Health Management system of a new generation aircraft for the improvement of security, task reliability and economy, this paper introduced the development situation of aircraft composite structural health monitoring and life prediction technologies, classified the existing technologies, and then discussed the principle, quality point, applicability and application situation, finally, pointed out several critical issues which still need further study.

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Model Based Control Concepts for Jet Engines

Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award ◽

10.1115/2001-gt-0016 ◽

2001 ◽

Cited By ~ 12

Author(s):

Klaus Lietzau ◽

Andreas Kreiner

Keyword(s):

Turbine Blades ◽

Engine Performance ◽

Current Control ◽

Engine Control ◽

Jet Engines ◽

Jet Engine ◽

Engine Model ◽

Model Based ◽

Safety Margins ◽

Engine Life

Many jet engine variables cannot be measured in-flight or can only be measured with a complex, and hence unreliable, instrumentation system. This includes variables that are of imminent importance for the safe operation of the engine or for engine life, such as the temperature of the high pressure turbine blades or the surge margins of the turbo compressors, for instance. Current control systems therefore transform limits on these variables into limits on other variables measured by the engine’s sensors. This leads to increased safety margins and thus to non-optimal engine performance. An onboard engine model incorporated into the engine control system could provide information about all engine variables. This could enable further control and monitoring system optimisations leading to improved engine performance, reduced fuel consumption, increased safety and engine life. This paper explains the principle of model based engine control and gives an overview about possible applications for conventional and also thrust vectored jet engines. Modeling methods for real-time simulation as well as methods for online model adaptation are presented. The potential of model based jet engine control is analyzed and fortified by some prototype realizations.

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Research of Engine Performance Control Based on EGTM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.148-149.431 ◽

2011 ◽

Vol 148-149 ◽

pp. 431-436

Author(s):

Hong Bo Peng ◽

Min Dan

Keyword(s):

Condition Monitoring ◽

Life Prediction ◽

Great Influence ◽

Engine Performance ◽

Operating Costs ◽

Estimation Methods ◽

Performance Control ◽

Definition Of ◽

Engine Condition ◽

Engine Life

Life prediction is one important of Engine research. Take-off EGTM is an important parameter to monitor Engine performance. Take-off EGTM have great influence on Engine life, Reducing EGT will help to extend Engine life on wing (LOW), thereby reducing operating costs. Aiming at Engine condition monitoring, the definition of take-off EGT Margin is given, estimation methods and their application on Engine life prediction are discussed.

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