Title :
REMM evaluation process
Author :
Marshall, Jane ; Balderstone, Mick ; Davies, Jason ; Lumbard, Dave
Author_Institution :
Goodrich, Birmingham, UK
Abstract :
REMM (reliability enhancement methodology and modelling) is a UK DTI (Department of Trade and Industry) funded collaborative research project. The aim of the project was to develop a methodology for reliability enhancement and a model for reliability assessment and this was achieved and a second phase, REMM2, commenced in June 2002. The objective of REMM2 is to consolidate the achievements of REMM by evaluating and validating the REMM methodology and statistical model, extending and refining the methods and models and expand the scope. This paper concentrates on the evaluation and validation of the REMM process. Five industrial partners have begun implementing the REMM process and this paper provides details of the evaluation process designed by the consortium. The products discussed include electronic, electrical, electromechanical and mechanical units. They are in varying stages of development and range from a redesign of a previous product to almost entirely novel design. Three aspects of the REMM process are discussed in this paper and these are: preliminary data analysis, the reliability task list generator and the REMM statistical model. From this ongoing evaluation process, a number of lessons have been learned regarding REMM and these concentrate on refinements to: data collection; analysis of in-service data; and the process for the elicitation of engineering concerns from design team members.
Keywords :
data analysis; reliability; statistical analysis; REMM evaluation process; REMM2; UK DTI funded collaborative research project; data collection; electrical units; electromechanical units; electronic units; in-service data analysis; mechanical units; reliability assessment; reliability enhancement methodology and modelling; statistical model; Aerospace industry; Artificial intelligence; Collaboration; Data analysis; Databases; Diffusion tensor imaging; Educational institutions; Mathematical model; Predictive models; Refining;
Conference_Titel :
Reliability and Maintainability Symposium, 2003. Annual
Print_ISBN :
0-7803-7717-6
DOI :
10.1109/RAMS.2003.1182030