Design of a Robust Classification Fusion Platform for Structural Health Diagnostics

Click on the DOI link to access the article (may not be free).Efficient health diagnostics provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of engineered systems. This paper presents a multi-attribute classification fusion approach which leverages the strengths provided by multiple membership classifiers to form a robust classification model for structural health diagnostics. Health diagnosis using the developed approach consists of three primary steps: (i) fusion formulation using a k-fold cross validation model; (ii) diagnostics with multiple multi-attribute classifiers as member algorithms; and (iii) classification fusion through a weighted majority voting with dominance system. State-of-the-art classification techniques from three broad categories (i.e., supervised learning, unsupervised learning, and statistical inference) were employed as the member algorithms. The proposed classification fusion approach is demonstrated with a bearing health diagnostics problem. Case study results indicated that the proposed approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness

Concurrent Design of Functional Reliability and Failure Prognosis for Engineered Resilience

Click on the DOI link to access the article (may not be free).This paper presents a new system design platform and approaches leading to the development of resilient engineered systems through integrating design of system functions and prognosis of function failures in a unified design framework. Failure prognosis plays an increasingly important role in complex engineered systems since it detects, diagnoses, and predicts the system-wide effects of adverse events, therefore enables a proactive approach to deal with system failures at the life cycle use phase. However, prognosis of system functional failures has been largely neglected in the past at early system design stage, mainly because quantitative analysis of failure prognosis in the early system design stage is far more challenging than these activities themselves that have been mainly carried out at the use phase of a system life cycle. In this paper, a generic mathematical formula of resilience and predictive resilience analysis will be introduced, which offers a unique way to consider lifecycle use phase failure prognosis in the early system design stage and to systematically analyze their costs and benefits, so that it can be integrated with system function designs concurrently to generate better overall system designs. Engineering design case studies will be used to demonstrate the proposed design for resilience methodology

Resiliencedriven system design of complex engineered systems

Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. Currently, most engineered systems are designed with system redundancies to ensure required system reliability under adverse events. However, a high level of system redundancy increases a system's life-cycle cost (LCC). Recently, proactive maintenance decisions have been enabled through the development of prognostics and health management (PHM) methods that detect, diagnose, and predict the effects of adverse events. Capitalizing on PHM technology at an early design stage can transform passively reliable (or vulnerable) systems into adaptively reliable (or resilient) systems while considerably reducing their LCC. In this paper, we propose a resilience-driven system design (RDSD) framework with the goal of designing complex engineered systems with resilience characteristics. This design framework is composed of three hierarchical tasks: (i) the resilience allocation problem (RAP) as a top-level design problem to define a resilience measure as a function of reliability and PHM efficiency in an engineering context, (ii) the system reliability-based design optimization (RBDO) as the first bottom-level design problem for the detailed design of components, and (iii) the system PHM design as the second bottom-level design problem for the detailed design of PHM units. The proposed RDSD framework is demonstrated using a simplified aircraft control actuator design problem resulting in a highly resilient actuator with optimized reliability, PHM efficiency and redundancy for the given parameter settings

A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

Click on the DOI link to access the article (may not be free).This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology

Disruption of PTPS Gene Causing Pale Body Color and Lethal Phenotype in the Silkworm, Bombyx mori

Phenylketonuria (PKU) is an inborn error of metabolism caused by mutations in the phenylalanine hydroxylase (PAH) gene or by defects in the tetrahydrobiopterin (BH4) synthesis pathway. Here, by positional cloning, we report that the 6-pyruvoyl-tetrahydropterin synthase (PTPS) gene, encoding a key enzyme of BH4 biosynthesis, is responsible for the alc (albino C) mutation that displays pale body color, head shaking, and eventually lethality after the first molting in silkworm. Compared to wild type, the alc mutant produced more substrates (phenylalanine (Phe) and tyrosine (Tyr)) and generated less DOPA and dopamine. Application of 2,4-diamino-6-hydroxypyrimidine (DAHP) to block BH4 synthesis in the wild type effectively produced the alc-like phenotype, while BH4 supplementation rescued the defective body color and lethal phenotype in both alc and DAHP-treated individuals. The detection of gene expressions and metabolic substances after drugs treatments in alc and normal individuals imply that silkworms and humans have a high similarity in the drugs metabolic features and the gene pathway related to BH4 and the dopamine biosynthesis. We propose that the alc mutant could be used as an animal model for drug evaluation for BH4-deficient PKU