Reliability prediction and failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic

Abstract

Purpose

The paper presents an efficient methodology that was developed for the reliability prediction and the failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic.

Design/methodology/approach

The reliability prediction is based on the general features and characteristics of the MIL‐HDBK‐217FN2 technical document and a derating plan for the system design is developed in order to maintain low components’ failure rates. These failure rates are used in the FMECA, which uses fuzzy sets to represent the respective parameters. A fuzzy failure mode risk index is introduced that gives priority to the criticality of the components for the system operation, while a knowledge base is developed to identify the rules governing the fuzzy inputs and output. The fuzzy inference module is Mamdani type and uses the min‐max implication‐aggregation.

Findings

A typical power electronic device such as a switched mode power supply was analyzed and the appropriate reliability indices were estimated using the stress factors of the derating plan. The fuzzy failure mode risk indices were calculated and compared with the respective indices calculated by the conventional FMECA.

Research limitations/implications

Further research efforts are needed for the application of fuzzy modeling techniques in the area of reliability assessment of electronic devices. These research efforts can be concentrated in certain applications that have practical value.

Practical implications

Practical applications can use a fuzzy FMECA modeling instead of the classical FMECA one, in order to obtain a more accurate analysis.

Originality/value

Fuzzy modeling of FMECA is described which can calculate fuzzy failure mode risk indices.

Citation

Zafiropoulos, E.P. and Dialynas, E.N. (2005), "Reliability prediction and failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic", International Journal of Quality & Reliability Management, Vol. 22 No. 2, pp. 183-200. https://doi.org/10.1108/02656710510577233