David H. Collins

System Health Assessment

ABSTRACT Complex systems are increasingly confronted by two conflicting sets of requirements: on the one hand, demands for continuous operational readiness with high reliability and availability; on the other, the need to minimize life cycle cost, implying reduced inspections, maintenance, and logistics support. An emerging paradigm to address this challenge is prognostics and health management (PHM), where measures of system health are used to determine needs for preventive and corrective maintenance, to optimize maintenance scheduling and parts stocking, and to forecast when a system will reach the end of its useful life. Two key components of PHM are a definition of system health and a strategy for how it is to be measured as part of system health assessment (SHA). In this article we discuss system health as a general concept, illustrate its application with examples, and describe how the use of system health metrics as part of an SHA program can facilitate PHM.

Bayesian nonparametric models for combining heterogeneous reliability data

Modern complex engineering systems often present the analyst with a mix of data types that can be used for reliability prediction: system test results, lifetime data from unit tests of components, and subsystem data, all of which may have predictive value for the system lifetime. We present a hierarchical nonparametric framework, using Dirichlet processes, in which time-to-event distributions may be estimated from sample data or derived based on physical failure mechanisms. By applying a Bayesian methodology, the framework can incorporate prior information, including expert opinion.

Accelerated Test Methods for Reliability Prediction

Perusal of quality- and reliability-engineering literature indicates some confusion over the meaning of accelerated life testing (ALT), highly accelerated life testing (HALT), highly accelerated stress screening (HASS), and highly accelerated stress auditing (HASA). In addition, there is a significant conflict between testing as part of an iterative process of finding and removing defects and testing as a means of estimating or predicting product reliability. We review the basics of these testing methods and describe how they relate to statistical methods for estimation and prediction of reliability and reliability growth. We also outline potential synergies to help reconcile statistical and engineering approaches to accelerated testing, resulting in better product quality at lower cost.

A comprehensive method for solving finite-state semi-Markov processes

Semi–Markov processes (SMPs) provide a rich framework for many real–world problems. However, owing to difficulty in implementing practical solutions they are rarely used with their full capability. The theory of SMPs is quite mature but was mainly developed at a time when computational resources were not widely available. With the exception of some of the simplest cases, solutions to SMPs are inherently numerical, and SMPs have been underutilised by practitioners because of difficulty in implementing the theory in applications. This paper demonstrates the theory and computational methods needed to implement SMP models in practical settings. Methods are illustrated with an application modelling the movement of coronary patients in a hospital. Our aim is to allow practitioners to use richer SMP models without being burdened with the rigorous mathematical theory.

Panning for gold: Enhancing the precision of sensitivity test data

Sensitivity tests apply a range of stimulus values to experimental subjects and record binary responses in order to estimate the distribution of threshold values in the subject population, where thresholds delineate responses from nonresponses. In many applications, such as explosives engineering, individual tests are expensive and are conducted in small runs. Scarcity of data results in nonexistence of estimates, or estimates with low precision. We discuss various methods, such as combining test runs, covariate analysis, and penalized maximum likelihood, for enhancing precision and “mining more gold” from expensive test results.

An introduction to statistical flowgraph models for engineering systems

Statistical flowgraph models have proven useful for analysis and modeling of complex systems viewed as multistate processes that lead to outcomes such as degraded operation or failure. This article provides an engineering-oriented introduction to statistical flowgraph models: system representation, setting up a flowgraph model, parameter estimation, solution of the model (using either a frequentist or Bayesian approach), and interpretation of model outputs. The method is illustrated with a model for piping reliability in a nuclear power plant, and compared with alternative solution methods.