Soft Failure

Soft failure, the gradual degradation of system performance rather than abrupt catastrophic failure, is increasingly studied across diverse fields, aiming to develop proactive detection and mitigation strategies. Current research focuses on applying machine learning, particularly deep learning models like encoder-decoders, variational autoencoders, and generative adversarial networks, to analyze various data sources (e.g., optical spectra, quality-of-transmission metrics) for early anomaly detection and prediction of failure evolution. This work is crucial for improving the reliability and efficiency of complex systems, such as optical networks and robotic systems, by enabling timely interventions and reducing costly downtime.