Prognostics and Health Management of Engineering Systems

Abstract

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Automotive IVHM: Towards Intelligent Personalised Systems Healthcare

“…It focusses on assessing the impact of failure to implement optimal maintenance actions to minimise impact of loss onto the system and the user. Figure 2 illustrates the diagnostics and prognostics driven health management (Kim et al, 2017).…”

“…While the IVHM concept has been introduced for a relatively long time (see for example Benedettini et al, 2009), there are very few mature, comprehensive implementations of IVHM systems. The challenges with prognostics and health management systems have been discussed and summarised by many authors (see for example Lee et al, 2014;Kim et al, 2017;Elattar et al, 2016, Schleigh et al, 2017. Some of these challenges, will be discussed herein from an automotive systems perspective, emphasizing the differences between different industry sectors.…”

Measures of Entropy to Characterize Fatigue Damage in Metallic Materials

“…The traditional damage measurements in fatigue, for example, crack growth and load-carrying capacity reduction, are detectable only in the later stages of life and are ineffective in characterizing damage during the earlier periods of life [1]. In contrast, PHM-based life estimation and prognosis incorporates related monitored damage variables into deterministic physics of failure (PoF) models [2][3][4][5][6]. In data-driven prognostics in PHM, observed damage precursors, such as initiation of very small cracks, are collected during system operation and are used to estimate the so-called remaining useful life (RUL) [5,7].…”

Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

“…As wind power is a leading renewable energy source, availability, reliability, and lifetimes are taken incrementally into account by investors. In this work, we focus on slow developing faults, which, when not addressed, can cause unwanted or unnecessary costly downtime [1][2][3][4][5][6][7][8]. SPecifically, we will focus on (i) investigate main bearing monitoring, (ii) give a full account of the underlying neural network (NN) approach presented by Herp et al [9] on different timescales, (iii) present how this ties into O&M efforts, and (iv) compare expected main bearing RUL with model predictions.…”