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Machine learning-based methods for TTF estimation with application to APU prognostics

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Abstract

Machine learning-based predictive modeling is to develop machine learning-based or data-driven models to predict failures before they occur and estimate the remaining useful life or time to failure (TTF) accurately. Accurate TTF estimation plays a vital role in predictive maintenance or PHM (Prognostic and Health Management). Despite the availability of large amounts of data and a variety of powerful data analysis methods, predictive models developed for PHM still fail to provide accurate and precise TTF estimations. This paper addresses this problem by integrating machine learning algorithms such as classification, regression and clustering. A classification system is used to determine the likelihood of component failures such that rough indications of TTF are provided. Clustering and SVM-based local regression are then introduced to refine the time to failure estimations provided by the classification system. The paper illustrates the applicability of the proposed approach through a real world aerospace application with details on data pre-processing requirements. The results demonstrate that the proposed method can reduce uncertainty in estimating time to failure, which in turn helps augment the usefulness of predictive maintenance.

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Acknowledgments

Many people at the National Research Council of Canada have contributed to this work. We would also like to thank Air Canada for providing the data used in this research. This work is supported by the Natural Science Foundation (Grant No.61463031).

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Authors and Affiliations

  1. National Research Council Canada, Ottawa, Ontario, K1A 0R6, Canada

    Chunsheng Yang & Sylvain Letourneau

  2. Carleton University, Ottawa, Canada

    Jie Liu

  3. Nanchang University, Nanchang, China

    Qiangqiang Cheng

  4. The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yubin Yang

Authors
  1. Chunsheng Yang
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  2. Sylvain Letourneau
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  3. Jie Liu
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  4. Qiangqiang Cheng
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  5. Yubin Yang
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Corresponding author

Correspondence to Chunsheng Yang.

Appendices

Acronym

ACARS:

Aircraft Communications Addressing & Reporting System

APU:

Auxiliary Power Unit

EM:

Expectation Maximization Algorithm for clustering

J48:

Decision Tree Classification Algorithm

MSE:

Mean Squared Error

NN:

Neural Network

PHM:

Prognostic and Health Management

SMO:

Sequential Minimal Optimization

SVM:

Support Vector Machine

TTF:

Time to Failure

WEKA:

Waikato Environment for Knowledge Analysis

Notation

p :

number of positive predictions

N :

total number of positives made by classifiers in training dataset

score i :

score from the reward function for the i th instance classified as positive

NbrDetected :

number of detected failures

NbrofCase :

total number of failures

M :

number of observations kept before each failure

N :

number of observations kept after each failure

Sign :

sign of \({\sum }_{i=1}^{p} {score_{i} } \)

TTF C :

TTF estimate from the classifier

TTF R :

TTF estimate from the regression model

RemainingOPH :

remaining operational life of a component (in hours)

X i j :

The system state observation: the j th instance in i th time-series.

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Yang, C., Letourneau, S., Liu, J. et al. Machine learning-based methods for TTF estimation with application to APU prognostics. Appl Intell 46, 227–239 (2017). https://doi.org/10.1007/s10489-016-0829-4

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  • DOI: https://doi.org/10.1007/s10489-016-0829-4

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