Defining PHM, A Lexical Evolution of Maintenance and Logistics

Kalgren, P.W.; Byington, Carl S.; Roemer, Michael; Watson, Matthew J.

doi:10.1109/autest.2006.283685

Cited by 112 publications

(71 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…PHM is said to exert an essential influence on the spare supply process and the maintenance activities [17,18]. Nonetheless, a reliable prognostic mechanism and RUL estimation approach is supposed to be the prerequisite of it.…”

Section: Introductionmentioning

confidence: 99%

Discrete Event Logistics Systems (DELS) simulation modeling incorporating two-step Remaining Useful Life (RUL) estimation

Cui

Shi

Wang

2015

Computers in Industry

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Discrete Event Logistics Systems (DELS) simulation modeling incorporating two-step Remaining Useful Life (RUL) estimation

Cui

Shi

Wang

2015

Computers in Industry

View full text Add to dashboard Cite

“…While different forms of the PHM process can be found, the most commonly used, at least in the industry, is the openstructure architecture for conditioned based maintenance (OSA-CBM) scheme [4]. Although PHM is a quite recent discipline, it has reached a certain maturity with the development of its own standards, as shown in [5,6]. It has also been frequently applied and has demonstrated good results, first in its original field of application, structural health monitoring (SHM) [7], and later, in other fields, such as bearing monitoring [8] and battery life prediction [9].…”

Section: Introductionmentioning

confidence: 99%

A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators

Lamoureux

Mechbal

Massé

2014

Reliability Engineering & System Safety

View full text Add to dashboard Cite

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. b s t r a c tTo increase the dependability of complex systems, one solution is to assess their state of health continuously through the monitoring of variables sensitive to potential degradation modes. When computed in an operating environment, these variables, known as health indicators, are subject to many uncertainties. Hence, the stochastic nature of health assessment combined with the lack of data in design stages makes it difficult to evaluate the efficiency of a health indicator before the system enters into service. This paper introduces a method for early validation of health indicators during the design stages of a system development process. This method uses physics-based modeling and uncertainties propagation to create simulated stochastic data. However, because of the large number of parameters defining the model and its computation duration, the necessary runtime for uncertainties propagation is prohibitive. Thus, kriging is used to obtain low computation time estimations of the model outputs. Moreover, sensitivity analysis techniques are performed upstream to determine the hierarchization of the model parameters and to reduce the dimension of the input space. The validation is based on three types of numerical key performance indicators corresponding to the detection, identification and prognostic processes. After having introduced and formalized the framework of uncertain systems modeling and the different performance metrics, the issues of sensitivity analysis and surrogate modeling are addressed. The method is subsequently applied to the validation of a set of health indicators for the monitoring of an aircraft engine's pumping unit.

show abstract

“…PHM has been defined as "a maintenance and asset management approach utilizing signals, measurements, models, and algorithms to detect, assess, and track degraded health, and to predict failure progression [1]." As defined, PHM encompasses much more than is currently addressed by SCC20; however, the AI-ESTATE standard has been found to address many PHM issues related to fault/failure diagnosis.…”

Section: Introductionmentioning

confidence: 99%