Machine Learning Approaches Towards Streetlight Fault Prediction for Smart Infrastructure

Abstract

Urban street lighting systems are the linchpins of contemporary urban environments, playing an indispensable role in safeguarding public spaces, bolstering security, sustaining nighttime economic activities and ensuring the safety and efficiency of vehicular and pedestrian traffic flow. Nevertheless, these critical infrastructures are vulnerable to a wide array of accountabilities, spanning from routine issues like lamp burnout and connection failures to more intricate problems such as electrical malfunctions, voltage fluctuations and adverse environmental impacts. The occurrence of these faults can precipitate hazardous conditions, including compromised visibility and increased accident risks and can also lead to substantial economic losses due to maintenance costs and disruptions to commerce. Traditional approaches to streetlight maintenance, which primarily involve reactive responses to reported failures, are often characterized by inefficiency, significant resource consumption and extended periods of service disruption. This paper introduces a proactive, data-driven solution, the application of machine learning (ML) methodologies to accurately predict incipient streetlight faults before they manifest, thereby transforming maintenance practices, minimizing downtime and optimizing resource allocation. Our research methodology leverages the predictive power of a diverse ensemble of machine learning algorithms, encompassing Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, XGBoost, Naive Bayes and AdaBoost. These models meticulously analyze electrical current signatures derived from streetlight operations, with a keen focus on identifying subtle yet critical patterns within fluctuations and voltage variations that serve as early indicators of impending failures. The results of our evaluation are compelling, with the Random Forest and XGBoost models demonstrating exceptional predictive accuracy, achieving up to 84% in fault prediction. These findings represent a substantial advancement over traditional reactive maintenance paradigms. While we readily acknowledge the presence of ongoing challenges, such as the imperative for high-quality, representative datasets, the complexities associated with real-time data acquisition and processing and the intricacies of seamless integration with legacy infrastructure, this research provides a robust and scalable framework for the development of intelligent, sustainable and resilient smart city street lighting systems.