Machine Learning-Based Operational Wear Index Estimation of LED Luminaires Using Measured Switching-Cycle Data

Reliable assessments of the operational conditions of LED lighting systems are important for maintenance planning and asset management. Conventional Wear Index (WI) approaches typically assume a fixed daily number of switching cycles, which may not accurately reflect actual operating conditions. This study proposes a machine learning-based framework for operational Wear Index estimation using measured switching-cycle data collected from an LED lighting installation. Daily energy consumption and operating time were predicted using Multiple Linear Regression (MLR), Random Forest (RF), Long Short-Term Memory (LSTM), and Backpropagation Neural Network (BPNN) models. The models were evaluated using consistent performance metrics, and the BPNN achieved the highest predictive accuracy and was subsequently applied to the Wear Index calculation. Incorporating measured daily switching activity instead of the conventional fixed assumption of nine switching cycles per day resulted in a 17.80% higher cumulative Wear Index. This result demonstrates that the representation of switching activity can materially influence operational wear assessment. The proposed framework provides a practical data-driven approach for estimating operational wear under actual operating conditions and may support maintenance planning in intelligent lighting systems. Since the results are based on a single monitored installation, they should be interpreted as a case study.