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Influxdb
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3 issues matching filters
Influxdb
- Field NotesJun 8, 2026
Cross-asset correlation on the open stack, and what it actually buys: fewer false alarms, not more catches
Two spindles, one PyOD model that scores them jointly, rendered on the same Grafana dashboard. The cross-asset feature the vendor quote priced at a premium, built for the cost of the second carrier the second asset needed anyway. The result is mostly a false-positive filter, which is the opposite of how the feature is sold.
Issue 06 deployed a single-asset Isolation Forest in 80 lines of Python and priced the vendor quote against it line by line. One line item survived as genuinely not built: cross-asset correlation, which the SaaS markets as ensemble-learning anomaly detection with cross-asset correlation and which issue 06 estimated at roughly one engineer-week. Issue 07 builds it. A second i.MX 8M Plus carrier on a second spindle publishes to the same broker, a PyOD model scores both assets in one joint feature frame, and a common-mode detector flags the case where both assets move together. The result reframes what cross-asset correlation is for. It catches almost nothing the single-asset model does not. Its real value is the inverse: it rejects the environmental false positives from issue 06, the cooling-system cross-talk and the floor-vibration bleed-through, by recognizing them as plant-wide rather than asset-specific. That is a meaningful feature, and it is not the feature the marketing describes.
Pyod·Cross Asset Correlation·Anomaly Detection·Ecod·Common Mode Rejection·Self Hosted - Field NotesJun 1, 2026
Isolation Forest in 80 lines of Python, against the vendor 'AI/ML machine health' pitch
The model layer on top of the open IIoT stack. Scikit-learn trained on InfluxDB data, anomaly scores written back, Grafana renders the result on the same dashboard. What the model catches that the threshold rules miss, and what the $1,200-per-asset SaaS sells on top.
Issue 05 stood up Grafana, InfluxDB, and Telegraf on the same $5.50/mo Hetzner VM that runs the Sparkplug B broker. The alerts in that issue were threshold rules: RMS velocity above ISO 10816 Class II, drive current above 110% nameplate, anomaly score above a fixed line. Issue 06 builds the model that produces the anomaly score, in 80 lines of Python. Scikit-learn Isolation Forest, trained nightly on the prior 14 days of telemetry from InfluxDB, scoring live frames every 10 seconds and writing the score back as its own measurement. The comparison: the same vendor SaaS quote from issue 05, this time looking at what its 'AI-driven machine health' line item actually buys on top of the open implementation. The model catches one real failure pattern that the threshold rules miss. It also produces a class of false positives that the threshold rules do not. The honest read on which of the two layers should sit in front of the maintenance team.
Isolation Forest·Scikit Learn·Anomaly Detection·Machine Learning·Self Hosted·Influxdb - Field NotesMay 25, 2026
Grafana plus InfluxDB on $5.50/mo, against the vendor 'machine health platform'
Self-hosted observability on the same Hetzner VM that runs the Sparkplug B broker. What the open stack delivers, what it does not, and the honest read on the $1,200-per-asset-per-year quotes.
Issue 04 put OPC UA and Sparkplug B on the carrier and pointed both at a HiveMQ broker on a $5.50/mo Hetzner VM. Issue 05 puts the dashboards, the historian, and the alerting on top of it. Grafana plus InfluxDB plus Telegraf, all self-hosted on the same VM, fed by the Sparkplug subscriber. The result is a working machine-health dashboard with anomaly alerts, vibration trend, and 90-day retention for under six dollars a month. The comparison: a representative vendor 'machine health' SaaS quote at $1,200 per asset per year. What the open stack delivers that the SaaS does, what it does not, and which of the two should sit on a real plant floor.
Grafana·Influxdb·Telegraf·Self Hosted·Observability·Machine Health