System dynamic reliability assessment and failure prognostics

Abstract

Traditionally, equipment reliability assessment is based on failure data from a population of similar equipment, somewhat giving an average description of the reliability performance of an equipment, not capturing the specificity of the individual equipment. Monitored degradation data of the equipment can be used to specify its behavior, rendering dynamic the reliability assessment and the failure prognostics of the equipment, as shown in some recent literature. In this paper, dynamic reliability assessment and failure prognostics with noisy monitored data are developed for a system composed of dependent components. Parallel Monte Carlo simulation and recursive Bayesian method are integrated in the proposed modelling framework to assess the reliability and to predict the Remaining Useful Life (RUL) of the system. The main contribution of the paper is to propose a framework to estimate the degradation state of a system composed of dependent degradation components whose conditions are monitored (even without knowing the initial system degradation state) and to dynamically assess the system risk and RUL. As case study, a subsystem of the residual heat removal system of a nuclear power plant is considered. The results shows the significance of the proposed method for tailored reliability assessment and failure prognostics.

Prognostics of a multistack PEMFC system with multiagent modeling

“… Resampling : Repeating the previous steps for a number of iterations may skew the distribution of particles by observing that only one particle has non‐negligible weight . In this work, a systematic resampling algorithm is implemented for its low computational burden .…”

“…Denoting that x k = k , the state equation can be represented as By Equation (5-7), the observation equation can be expressed as Note that the difference from the previous work using PF is that the uncertain measurements of a PEMFC considered in this work include the voltage and current, as shown in Equations (11) and (12). The state equation and observation equation define a Bayesian tracking system 29 given the initial state distribution p x 0 |V m 0 ,I m 0 = p x 0 and the independent monitored current and voltage values until time k 0 . Because of the noise distribution and nonlinear relation between x k and V m k , I m k , the optimal solution can not always be found analytically.…”

A Review of Methodologies for Safety and Hazard Management in Chemical Industries

“…The condition‐monitoring data approach has been used to conduct DRA based on different approaches, and incorporating different techniques: A self‐organizing map‐based approach has been used to deal with non‐linear and non‐Gaussian features Kalman filtering has been applied for the estimation of the true degradation states, based on loss function associated with them . Principal component analysis (PCA), and particle filtering have also been shown to be useful , . The RUL (remaining useful life) of components has been predicted by applying Kalman filtering and then a fault tree model was employed for DRA . Multiple condition‐monitoring variables have also been dealt with using the remaining time idea . A Bayesian reliability updating method, based on the dependencies between two components has also been shown . The Dynamic Bayesian Network (DBN) concept has been used where the parameters are updated by condition‐monitoring data from a process monitoring system . Condition‐based fault tree analysis, where the condition‐monitoring data helps in updating the failure rates of basic events, has also been developed . …”

“…A Bayesian reliability updating method, based on the dependencies between two components has also been shown .…”

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

“…Critical product failures can bring heavy loss to economic value and society benefit, which means the analysis of product failure is essential for reliability optimization design [19][20][21]. Quantitative indicators of product failure are traditionally the failure frequency and failure severity, and some scholars dealt with the product reliability optimal design by the introduction of the qualitative analysis and quantitative calculation of failure mode effects analysis (FMEA) [22].…”