The field of predictive maintenance and anomaly detection is moving towards the development of more sophisticated and automated methods for identifying potential issues in complex systems. Researchers are exploring the use of machine learning algorithms and unsupervised techniques to improve the accuracy and efficiency of predictive maintenance. One of the key areas of focus is the development of dynamic health indicators that can capture the temporal dependence of degradation processes, allowing for more accurate predictions of future failures. Another area of research is the use of machine learning to generate residual stress distributions and detect anomalies in industrial equipment, such as motors and bearings. These advances have the potential to significantly improve the reliability and performance of complex systems. Noteworthy papers include:

• A novel unsupervised framework for dynamic health indicator construction, which outperforms existing methods in prognostic tasks.
• A machine learning approach to generate residual stress distributions using sparse characterization data, which achieves excellent predictive accuracy and generalization.
• A signature-guided data augmentation methodology for induction-motor diagnostics, which leverages the strengths of both supervised ML and unsupervised signature analysis.