Data acquisition systems for alternating current switch machine prediction and health management

Alternating current (AC) switch health predictive maintenance is crucial for reducing downtime and improving efficiency. The system analyses operational data like pressure, temperature, vibration, and voltage to predict potential failures using various learning techniques. However, it faces challenges such as slow convergence, suboptimal accuracy, and high computational costs. These issues are addressed by the optimised neural model (ONM), which employs a sequence-to-sequence neural model and grasshopper optimisation. Data is processed through windowing and lag feature procedures, followed by feature engineering to extract domain-specific statistics. The optimised algorithm fine-tunes parameters and captures temporal dependencies, achieving 98.56% accuracy and a loss function of 0.012. This enhances prediction robustness and reliability, ultimately optimising maintenance schedules and operational efficiency.