A predictive maintenance toolkit expands anomaly detection and lifecycle insights across industrial assets. The release combines signal processing, machine learning, and domain heuristics to surface actionable findings rather than noisy alerts. It also includes guidance on how to start small, label data effectively, and measure impact in ways that translate to scheduling and operations planning. The aim is practical, defensible improvement rather than black‑box predictions.