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Feature Engineering and Health Indicator Construction for Fault Detection and Diagnostic

In: Control Charts and Machine Learning for Anomaly Detection in Manufacturing

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  • Khanh T. P. Nguyen

    (INP Toulouse, The National Engineering School of Tarbes)

Abstract

Nowadays, the rapid growth of modern technologies in Internet of Things (IoT) and sensing platforms is enabling the development of autonomous health management systems. This can be done, in the first step, by using intelligent sensors, which provide reliable solutions for systems monitoring in real-time. Then, the monitoring data will be treated and analyzed in the second step to extract health indicators (HIs) for maintenance and operation decisions. This procedure called feature engineering (FE) and HI construction is the key step that decides the performance of condition monitoring systems. Hence, in this chapter we present a comprehensive review and new advances of FE techniques and HI construction methods for fault detection and diagnostic (FDD) of engineering systems. This chapter would also serve as an instructive guideline for industrial practitioners and researchers with different levels of experience to broaden their skills about system condition monitoring procedure.

Suggested Citation

  • Khanh T. P. Nguyen, 2022. "Feature Engineering and Health Indicator Construction for Fault Detection and Diagnostic," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 243-269, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-030-83819-5_10
    DOI: 10.1007/978-3-030-83819-5_10
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