Improved Time-Based Maintenance in Aeronautics with Regressive Support Vector Machines

Márcia Lourenço Baptista, Joao Pedro Pinheiro Malere, Cairo Lúcio Nascimento Junior, Helmut Prendinger, and Elsa Maria Pires Henriques
Submission Type: 
Full Paper
phmc_16_044.pdf2.14 MBOctober 1, 2016 - 10:57am

In modern preventive maintenance, time-based management is still the mainstream approach. This strategy continues to be the preferred choice to manage the risk of equipment failure when other alternatives, such as condition-based management, are technically or economically unfeasible. In this paper we propose a novel approach to time-based maintenance based on (linear) regressive Support Vector Machines (SVM). In the proposed modelling approach, expected lifetime is estimated based on the equipment past failure times combined with the maintenance history of similar components. Time series analysis combined with outlier detection techniques and concepts from technical analysis, such as resistance and support levels, are used to establish the SVM model prediction bounds. The proposed SVM model is compared with the traditional approach to time-based maintenance - life usage modelling - and the autoregressive moving average (ARMA) forecasting method. Results are shown on an industrial case study of data describing the maintenance life-cycle of a critical component of the aircraft bleed air system. Results suggest that the SVM model can outperform the other tested approaches in regards to the squared, percentage and absolute mean errors.

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Submission Keywords: 
Data-driven modeling
Time-based Maintenance
Maintenance Data
Regression Support Machines
Technical Analysis
Outlier detection
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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