A fuzzy expectation maximization based method for estimating the parameters of a multi-state degradation model from imprecise maintenance outcomes

Abstract

Multi-State (MS) reliability models are used in practice to describe the evolution of degradation in industrial components and systems. To estimate the MS model parameters, we propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements. A procedure for estimating the Remaining Useful Life (RUL) based on the MS model and conditional on such imprecise evidence is, then, developed. The proposed method is applied to a case study concerning the degradation of pipe welds in the coolant system of a Nuclear Power Plant (NPP). The obtained results show that the combination of field data with expert knowledge can allow reducing the uncertainty in degradation estimation and RUL prediction.

Increasing the Reliability of an Electrical Power System in a Big European Hospital through the Petri Nets and Fuzzy Inference System Mamdani Modelling

“…Also, several previous researchers put forward their ideas in articles they wrote as follows. Cannarile et al (2017) "propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements" [40].…”

An evidential similarity-based regression method for the prediction of equipment remaining useful life in presence of incomplete degradation trajectories

“…The predicted degradation state is, then, compared with a failure criterion (failure threshold) representative of the degradation state beyond the which the equipment fails performing its required functions. Examples of modeling techniques used in degradation-based approaches are Wiener Processes (WP) [4], Gamma Processes (GP) [5], Inverse Gaussian Processes (IGP) [6], Semi-Markov Models (SMM) [7], Hidden Semi-Markov Models (HSMM) [8], General Path Models (GPM) [9] and fuzzy transition models [10] based on Mamdani models [11].…”

A heterogeneous ensemble approach for the prediction of the remaining useful life of packaging industry machinery

“…The predicted degradation state is, then, compared with a failure criterion, such as the value of deg-radation beyond which the equipment fails performing its function (failure threshold). Examples of modeling techniques used in degradationbased approaches are Auto-Regressive models (Gorjian et al, 2009), Relevance Vector Machines (Di Maio et al, 2012) and Semi-Markov Models (Cannarile et al, 2017a) (Cannarile et al, 2018). Direct RUL predictions approaches, instead, typically resort to machine learning techniques that directly map the relation between the observable parameters and the equipment RUL, without the need of predicting the equipment degradation state evolution towards a failure threshold (Schwabacher et al, 2007).…”