--- title: "Anomaly Detection on Industrial Sensor Data: Beyond Threshold Alerts" url: https://daily.dev/posts/anomaly-detection-on-industrial-sensor-data-beyond-threshold-alerts-13bgule7f source_url: https://cratedb.com/blog/anomaly-detection-industrial-sensor-data type: article source: "CrateDB" published: 2026-06-15T07:06:07.460Z updated: 2026-06-15T07:06:32.301Z tags: ["machine-learning", "data-science", "cratedb"] reading_time: 9 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Anomaly Detection on Industrial Sensor Data: Beyond Threshold Alerts **[CrateDB](https://daily.dev/sources/cratedb)** · 9 min read · 0 upvotes · 0 comments ## Summary Fixed vibration thresholds set years ago degrade over time as equipment changes, generating false alerts and missing real faults. Isolation Forest offers a better approach: trained only on healthy sensor readings, it produces a continuous anomaly score reflecting how statistically unusual a reading is relative to the current fleet baseline. Combined with an XGBoost fault classifier, the two scores create a four-quadrant decision framework — familiar faults, unfamiliar faults, novel anomalies, and normal operation — each mapping to a different operational response. The post walks through training the Isolation Forest with scikit-learn, persisting the model, writing scores to CrateDB alongside fault probabilities, and querying the four quadrants in SQL for Grafana dashboards or maintenance reports. The key insight is that threshold alerting, anomaly detection, and fault classification are complementary layers, not alternatives. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel](https://daily.dev/posts/the-machine-learning-advent-calendar-day-8-isolation-forest-in-excel-70giwwuwg) · Towards Data Science · 0 upvotes · 0 comments - [6 Best Practices for Your Anomaly Detection Platform Success](https://daily.dev/posts/6-best-practices-for-your-anomaly-detection-platform-success-d480tllja) · Decube · 0 upvotes · 0 comments - [Real-Time ML Inference on Live Sensor Data: Architecture with CrateDB and FastAPI](https://daily.dev/posts/real-time-ml-inference-on-live-sensor-data-architecture-with-cratedb-and-fastapi-j22gsdkkn) · CrateDB · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#cratedb](https://daily.dev/tags/cratedb) [View this post on daily.dev](https://daily.dev/posts/anomaly-detection-on-industrial-sensor-data-beyond-threshold-alerts-13bgule7f)