Analysis and Estimation of the Probability of Failures in an Electric Lighting Network Using Mamdani Inference and Scaled Conjugate Gradient
Mohammed Amine Jouahri
Zakaria Boulghasoul
Abdelouahed Tajer
Systems Engineering and Application Laboratory
Cady Ayyad University
Bd Abdelkrim Al Khattabi
Marrakech, 40000, Morocco
Abstract
This paper presents an intelligent fault detection system (FDS) for public lighting networks, designed to improve diagnostic accuracy and system reliability. The proposed system integrates the Mamdani fuzzy inference method and scaled conjugate gradient (SCG) neural networks to detect four key fault types: power, lighting, cloud cover sensor and road flow sensor. Inputs such as traffic flow, cloud cover, power supply and lighting intensity are used to ensure precise diagnostics. The Mamdani method offers strong interpretability and robustness in handling uncertainties, while the SCG algorithm enhances performance through efficient learning. Simulation results show fault detection probabilities exceeding 85%, confirming the effectiveness of the system. This paper demonstrates the potential of combining fuzzy logic and neural networks for reliable and intelligent monitoring of public lighting infrastructure.
Keywords: public lighting networks; fault detection; power faults; lighting faults; sensor faults; Mamdani inference; scaled conjugate gradient
Cite this publication as:
M. A. Jouahri, Z. Boulghasoul and A. Tajer, “Analysis and Estimation of the Probability of Failures in an Electric Lighting Network Using Mamdani Inference and Scaled Conjugate Gradient,” Complex Systems, 34(4), 2026 pp. 479–507.
https://doi.org/10.25088/ComplexSystems.34.4.479
