Street lighting maintenance management system based on fuzzy logic

Abstract

Purpose

This research was carried out in response to the need to find new alternative ways of maintaining public lighting with high pressure sodium vapor lamps. The objective was to develop a public lighting maintenance management system based on fuzzy logic that guarantees maximum energy efficiency and is economically feasible.

Design/methodology/approach

A preliminary study was carried out on the complaints due to failures of public lighting for three years in the municipality of Camagüey, Cuba, determining the failure rate of each control and the time between failures, a statistical evaluation of the time between failures was carried out identifying that this variable responds to a Weibull distribution, the membership functions of the proposed four linguistic variables and the rule base for their fuzzy sets were created, obtaining as output linguistic variable the mass replacement and cleaning time.

Findings

The fuzzy logic maintenance model developed is effective in making better use of the useful life of high-pressure sodium vapor lamps, increasing the time between maintenance operations of mass lamp replacement and mass cleaning of luminaires up to 8 and 9 years, without compromising the required lighting levels and energy efficiency.

Originality/value

The literature contains very few references to the use of condition-based maintenance in this type of system, so a novel approach by a robust heuristic model of street lighting condition-based maintenance management driven by data is proposed, the model integrates through fuzzy logic all the factors that influence the progressive deterioration of these installations and maintenance actions that guarantee compliance with the established service quality standards, with the maximum energy efficiency that is economically justified.

Citation

Barrios Martínez, F.A., Sierra Gil, E., Borges Vasconcellos, D.E. and Gondres Torné, I. (2025), "Street lighting maintenance management system based on fuzzy logic", Journal of Quality in Maintenance Engineering, Vol. 31 No. 1, pp. 105-128. https://doi.org/10.1108/JQME-05-2024-0051