Prognosis and Health Management in system design

Massé, Jean-Rémi; Lamoureux, Benjamin; Boulet, Xavier

doi:10.1109/icphm.2011.6024346

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…The PHM system design is intimately related to the one of the turbofan, as it monitors the engine components' health and the engine includes elements serving the PHM purpose such as sensors or the computing unit (Massé, Lamoureux, & Boulet, 2011). It is therefore essential not to uncouple the design of both systems.…”

Section: Relationship With the Turbofan Developmentmentioning

confidence: 99%

An Approach to Designing PHM Systems with Systems Engineering

2016

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This work shows that given the numerous specific constraints that PHM systems for turbofan engines have to deal with and despite the low level of criticality of such systems, the use of Systems Engineering (or even System of Systems Engineering) methods and tools is essential to ensure project success. Various aspects of the methodology which were applied in our projects are presented here: on one hand general system design and project management considerations, on the other hand transversal methodological items such as model-based systems engineering and methods to manage requirements, hypotheses, interfaces, configuration, change, compatibility, validation, verification and integration. Associated pitfalls and lessons learned from applying these elements are highlighted, especially the importance of defining from the beginning of the design all the project management plans, with a constant focus on customer needs satisfaction, interfaces and design documentation while allowing iterations.

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Section: Relationship With the Turbofan Developmentmentioning

confidence: 99%

An Approach to Designing PHM Systems with Systems Engineering

2016

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“…This is illustrated by an abnormality score based on an estimation of the daily lubricant consumption (Figure 1). The concern of alert threshold set for continuously adjustable abnormality score distributions has been addressed by the authors in several papers (Masse, Hmad, Boulet, 2012. In these contributions, the observed CDF of the abnormality score with no degradation may be fit with a Parzen non parametric continuous CDF.…”

Section: First Situation: Continuously Adjustable Abnormality Score D...mentioning

confidence: 99%

Placement of alert thresholds on abnormality scores

et al. 2014

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The “s or more threshold trespassings out of N consecutive watch periods” detection verification strategy is known to offer advantages in terms of threshold value not too extreme under the constraint of low false alert rate, PFA. Typically PFA < 5%. The definition of PFA here considered is P(No degradation|Alert). It means the probability that there is no degradation given that degradation has been detected. The alert threshold placement has previously been addressed in the case where the abnormality score with no degradation has a stationary distribution and may be approached with a continuous non parametric Parzen distribution. This is illustrated on an abnormality score of the daily lubricant consumption estimation of an aircraft engine. The watch period is a day. The N consecutive watch periods are seven consecutive service days. The s or more trespassings are six or more trespassings out of seven consecutive days. In such configuration, the threshold is 0.21 l/h, which is inside the observed distribution. With an abnormality alert strategy with no verification, i.e. s = N = 1, the threshold is a more extreme value of 0.31 l/h which is outside the observed distribution. Two steps were considered. Step 1: Learning of the abnormality score distribution with no degradation by a non parametric Parzen fit. Step 2: Threshold set by quintile interpolation on the adjustment. This is extended to the case where the abnormality score with no degradation has a discrete distribution close to a Dirac distribution. This is typically the case for abnormality scores based on “out of range” counts for measurement chains along M clock increments of a watch period, corresponding to a flight cycle. With no degradation, most of the counts during a flight, but not all, are zero. Another example is an abnormality score based on a rough quantification of the time, “t SAV open”, between the open command and the start of movement of a starter air valve, during a watch period corresponding to a start sequence. With no degradation, most of the t SAV open of a start sequence are reported “zero”. Only a few start sequences trespass the few first quantification times. In these discrete cases close to Dirac the Parzen adjustment is no longer acceptable. A discrete degradation detection threshold, l, is set as a “l events or more count out of M” clock increments of a watch period, at each watch period for an “s out of N watch periods” confirmation strategy under the same constraint of P(No degradation| Alert) < PFA. This is done according to a binomial as well as a Poisson distribution on the number of events. Like in the continuous case two steps are considered. Step 1: Estimation of the ratio of discrete events with α confidence level based on the number, r, of events during a learning phase of I time increments over watch periods with no degradation. Step 2: Alert threshold set as the limit, l, on a watch period of size M for a “s out of N limit trespassings” detection strategy.

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“…2, is called V2-model. The main features of this model are the presence of a virtual implementation, a two-steps Verification and Validation (V&V) and a maturation phase [5]. In this paper, the focus is on the virtual implementation and the monitoring process validation as the rest of the method will be treated separately in other works.…”

Section: A Integrated Prognostics and Health Managementmentioning

confidence: 99%