Acquisition of physical kinetics in reciprocating machines using reservoir computing for anomaly detection

In this study, we propose a method for acquiring dynamical models of machines performing repetitive motions using Reservoir Computing (RC), with a view toward future applications in anomaly detection for real-world systems. Unlike conventional approaches that utilize random noise as input, the proposed method employs cyclic input signals, enabling safe and stable training of RC under real machine conditions. In this framework, RC is trained on output time-series data under normal operating conditions, and anomalies are assumed to be detected based on the discrepancy between the predicted and observed outputs. This RC configuration based on cyclic inputs has the potential to serve as a core component in anomaly detection systems for future real-world implementations. To investigate the effect of input diversity, five levels of cyclic input patterns with varying degrees of randomness were defined. Furthermore, two methods were employed to determine the parameter randomness. Simulation results demonstrated that even simple generated randomness maintains the prediction accuracy, confirming that RC can successfully acquire the dynamical behavior of machines exhibiting cyclic motions within a reduced complexity.