TY  - JOUR
AB  - Purpose– To help the maintenance managers/decision makers to select a suitable maintenance strategy for the components/parts associated with the system.Design/methodology/approach– An approach based on fuzzy linguistic modeling is used to select the most effective and efficient maintenance strategy. Three input parameters, i.e. historical data (I1), present data (I2) and competence of data (I3) related to failures of a component (gears), were taken to judge the effectiveness of the nature of maintenance strategies. These parameters are represented as members of a fuzzy set, combined by matching them against (if‐then) rules in rule base, evaluated in fuzzy inference system (Mamdani, min‐max type) and then defuzzified to assess the capability or effectiveness of maintenance strategy.Findings– The results show how the fuzzy logic approach translates vague, ambiguous, qualitative and imprecise information into numerical/quantitative terms, which helps to identify the most informative and efficient maintenance strategy. From the computed performance index values for each maintenance strategy it is observed that proactive (CBM) and aggressive maintenance strategy (TPM) are far better compared with traditional, reactive (BDM) maintenance strategy.Originality/value– The paper integrates fuzzy logic modeling – a knowledge‐based approach with database obtained through maintenance logs, historical records, equipment manuals and expert judgement, which might prove beneficial for maintenance managers/engineers/practitioners to select a suitable maintenance strategy for each piece of equipment associated with the systems.
VL  - 11
IS  - 4
SN  - 1355-2511
DO  - 10.1108/13552510510626981
UR  - https://doi.org/10.1108/13552510510626981
AU  - Sharma Rajiv Kumar
AU  - Kumar Dinesh
AU  - Kumar Pradeep
PY  - 2005
Y1  - 2005/01/01
TI  - FLM to select suitable maintenance strategy in process industries using MISO model
T2  - Journal of Quality in Maintenance Engineering
PB  - Emerald Group Publishing Limited
SP  - 359
EP  - 374
Y2  - 2024/04/20
ER  -