# Asset Health Monitor > Predictive maintenance for process plants: regime-aware early warnings, sensor faults kept apart from machine faults, and remaining useful life shown as a range. Catch the fault while it is still a trend. - URL: https://weaveward.com/work/predictive-maintenance-asset-health - Year: 2026 - Industry: Manufacturing - Capabilities: AI solutions, Data & dashboards - Technologies: Python, scikit-learn, Isolation Forest, DuckDB, Apache Parquet, FastAPI, Pydantic, asyncio, React, TypeScript, Vite ## The challenge Process plants run hundreds of rotating assets watched by thousands of sensors, and unplanned downtime is expensive. Fertiliser and cement plants, palm-oil mills and power stations all depend on pumps, compressors and fans that degrade slowly before they fail. Fixed alarm limits either fire constantly or too late, and a drifting or stuck sensor can look like a failing bearing. Maintenance teams need early, explainable warnings per asset, an estimate of how long a degrading asset can keep running, and a loop that learns from what technicians find. ## What we built Asset Health Monitor turns plant telemetry into early, explainable warnings, with a Python analytics pipeline, a FastAPI service and a React dashboard for reliability teams. - **Telemetry ingestion.** A batch HTTP endpoint and an asyncio streaming consumer write vibration, temperature, current, pressure and flow readings to time-partitioned DuckDB and Parquet storage - **Sensor health kept apart.** Data-quality rules catch stuck values, flatlines, drift and spikes, so a faulty instrument is flagged as a sensor issue and does not page a mechanic - **Regime-aware anomaly detection.** Operating regimes are found by clustering on load features, and normal behaviour is learned per asset and per regime. An Isolation Forest and a reconstruction model score every window against automatic per-asset thresholds - **Explainable alerts with feedback.** Contribution scores show which signals drove each anomaly. Alerts carry a severity, open and clear with hysteresis, are de-duplicated per asset and close with the technician's verdict of confirmed fault or false alarm - **Remaining useful life as a range.** A health indicator per asset feeds an exponential degradation fit with bootstrap uncertainty, which gives a distribution of remaining life to a failure threshold - **Plant overview and asset detail.** A health grid for the whole plant, asset pages with signals, anomaly score, regime shading and contributing signals, an alert inbox and a remaining-life chart with an uncertainty band - **Fault-injection simulator and evaluation.** A simulated plant of pumps, compressors and fans produces regime-dependent signals with injected machine faults, sensor faults and run-to-failure trajectories. An evaluation report measures event-based precision, recall, detection lead time, false alarms per asset-day and sensor-fault rejection against the injected faults ## How it works ### Ingest and check sensors Readings arrive in batches over HTTP or through the asyncio streaming consumer and land in time-partitioned DuckDB and Parquet storage. Sensor-health rules check every signal, so a stuck, flatlined, drifting or spiking reading is flagged as an instrument issue instead of an asset fault. ### Learn normal per regime Windowed features are computed per asset, and clustering on load features assigns each window to an operating regime. Normal behaviour is learned per asset and per regime, so a load change or a start-up is not mistaken for wear. ### Score against learned limits An Isolation Forest and a reconstruction model score each window. Each asset's alert threshold comes from a peaks-over-threshold fit at a target false-alarm rate, and contribution scores record which signals pushed the score up. ### Alert, review and learn Alerts open and clear with hysteresis, merge per asset and carry a severity. Technicians acknowledge and resolve them in the alert inbox and mark each one as a confirmed fault or a false alarm, and that feedback is tracked as precision over time. ### Estimate remaining life When an asset's health indicator degrades, an exponential degradation fit with bootstrap resampling gives a distribution of remaining life to a failure threshold. The asset page draws it as a chart with an uncertainty band. ## Engineering insights A warning is only useful when the reliability team trusts it, so every stage is built to avoid the false alarms that make fixed limits easy to ignore. - **A stuck sensor never pages a mechanic.** Sensor health and asset health are separate checks: stuck values, flatlines, drift and spikes are caught by data-quality rules and reported as instrument issues, so a bad reading is not reported as a failing machine - **Normal depends on the operating regime.** Start-ups, shutdowns and load changes move vibration, temperature and current more than early wear does. Clustering windows on load features first means each window is judged against the same asset in the same regime - **Two detectors with different blind spots.** An Isolation Forest flags windows that are rare in feature space, while a reconstruction model flags readings that are each normal on their own but break their usual relationships. Contribution scores then name the signals behind each anomaly - **Alert limits from extreme value theory.** Instead of hand-set limits, the peaks-over-threshold method fits a generalised Pareto distribution to the upper tail of each asset's anomaly scores and places the threshold at a target false-alarm rate. Hysteresis and per-asset de-duplication keep one developing fault from becoming a stream of repeated alerts - **Remaining life as a range, not a date.** An exponential degradation fit with bootstrap resampling turns the crossing of a failure threshold into a distribution instead of one falsely precise number. The method is scored on simulated run-to-failure trajectories with RMSE and prognostic horizon ## Design system Industrial clarity follows the high-performance HMI approach associated with ANSI/ISA-101: neutral grey surfaces keep a healthy plant visually quiet and colour is reserved for abnormal states, so the few assets that need attention stand out. - **Colour.** Plant grey backgrounds, white surfaces, hairline borders and ink text carry normal operation. Alarm red, warning amber and advisory blue appear only when something is abnormal - **Typography.** Inter with tabular numerals, so live values, anomaly scores and remaining-life ranges line up in columns and keep their width as they update - **Layout.** A compact, sparkline-first layout fits many assets on one screen and leads with each signal's recent trend - **Components.** A plant health grid, signal charts with regime shading, contribution bars, an alert inbox with feedback actions and a remaining-life chart with an uncertainty band ## Where this fits Plants with critical rotating equipment can apply the same pattern: trustworthy sensor data first, baselines learned per operating regime and warnings a technician can confirm. Explore our [AI solutions](/services/ai-solutions), [analytics dashboards](/services/data-analytics-dashboards) and [manufacturing work](/industries/manufacturing), or [talk to us about your assets](/contact).