Read the faint signals your equipment sends —
before they become failures
LINE SIGNAL learns the multivariate time-series of normal operation — vibration, current, temperature, pressure — and quantifies deviation as a real-time anomaly score. From chemical reactors to rotating machinery, it starts working without a single failure label.
Sensor · DCS/PLC streams
Read-only tag ingestion via OPC-UA · Modbus · MQTT
Normal-state model
Multivariate model trained on normal operation only
Real-time anomaly score
Deviation quantified with per-channel attribution
Alerts · control hooks
Alerts, CMMS work orders, correction proposals
Learn what normal looks like, and every abnormal becomes visible
Threshold-based monitoring misses the moment when the relationship between channels breaks down. LINE SIGNAL models the multivariate pattern of normal operation itself, catching states where every value is in range — but the combination is wrong.
Collect & clean
Second-level ingestion over OPC-UA/Modbus/MQTT with gap filling, noise filtering and operating-mode tagging
Learn normal
Reconstruction-error-based multivariate model trained on normal windows only — no labels needed
Score anomalies
Anomaly score decomposed into per-sensor contributions: which channel, and by how much
Respond & feed back
Duration-filtered alerts, CMMS integration, and (LV.2+) control correction proposals
* Verdicts and operator feedback flow back as training data, so the model keeps adapting to your plant.
Start without failure data
An unsupervised approach trained on normal data only. New equipment and low-failure critical assets can be monitored from the first month.
Cross-channel correlation
Temperature normal, pressure normal — but their relationship is off? We detect correlation breakdown that single-threshold monitoring cannot see.
Scores that show the cause
Every anomaly score is decomposed into sensor contributions — "bearing #3 vibration, 62%" — cutting root-cause time dramatically.
Drift & false-alarm control
Seasonality, feedstock lots and operating-condition changes are absorbed by periodic retraining; duration and mode filters suppress noise-driven alarms.
Use Case Analysis
From continuous chemical processes to discrete manufacturing — wherever time-series exists, it applies.
Batch quality deviation, flagged 5 days early
Intermittent reactor trips and quality drift kept forcing batch write-offs, and single-instrument alarms showed no warning signs.
Read-only collection of 24 DCS channels (temperature, pressure, flow, agitation) and a multivariate model built from golden-batch profiles.
Average 5.2-day early warning enabled planned maintenance; unplanned stops fell 37%. Now extended to golden-batch correction proposals (LV.1).
From sudden breakdowns to planned maintenance
Bearings and gears in presses, motors and reducers failed without warning, stopping entire lines for hours.
Fused vibration-spectrum and load-current analysis detected the growth trend of fault-frequency components early.
Sudden stops became planned interventions, and component life is now managed on evidence — cutting spare-part inventory as well.
Abnormal cells screened from a single curve
Defective cells were only identified late in formation, so full process resources were spent on cells destined to fail.
Anomaly detection on the shape of charge/discharge voltage-current curves screened abnormal cells in early cycles.
Early isolation cut downstream waste, and curve-shape data now feeds cell quality grading.
* Figures and scenarios reflect representative deployments and may vary by process and data conditions.
Specs & Integration
Frequently Asked Questions
We have almost no failure data. Is that a problem?
Won't alarms fire too often?
Do we have to replace our DCS/PLC?
How many assets should we start with?
Find out if this asset can warn you before it stops
Tell us about your process and data environment — we will come back with a feasibility summary and expected impact.