INDUSTRIALS

Industrials

Every production line streams temperature, pressure, vibration and throughput around the clock. TSLMs turn that telemetry into plain-language findings and recommendations, with no data scientist between the line and the decision.

INDUSTRIALS · EXAMPLE USE CASE

Predictive Asset Operations

The factory that sees failure before it happens.

[01] SENSE & ENCODE

Every sensor on the machine streams live data: current, temperature, pressure, flow, vibration. The TSLM reads these raw signals together with everything the plant already knows, like manuals, past failures and today’s conditions.

[02] REASON & EXPLAIN

When something changes, the machine speaks up on its own. The operator asks questions in plain language, and the TSLM answers with the likely cause, backed by the signal data and the plant’s history.

[03] ACT & EXECUTE

The TSLM proposes one concrete fix with the expected result. The operator approves, adjusts or declines. Once approved, the line is stopped on purpose and a technician swaps the worn impeller.

[04] SYSTEM IMPLICATIONS

Before the decision, every option is priced for the whole plant: hours of downtime, cost, and risk to neighboring lines if the fix is deferred versus executed right away.

LINE 3 RUNNING · ALL SIGNALS NOMINAL

CONTROL BOARD

! LINE 3 · PUMP TRAIN · SELF-REPORTED · 06:52

Flagging myself: drive-end bearing vibration up 41% since 06:40, discharge pressure drifting −0.3 bar/h. Something changed. I need a look.

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THE SEQUENCE PLAYS AUTOMATICALLY · REPLY OR DECIDE ON THE CONTROL BOARD · CLICK A STAGE TO JUMP · ↺ RESET TO REPLAY

HOW IT WORKS

A factory rarely stops because information is completely unavailable. More often, warning signs are distributed across machine measurements, alarm histories, work orders and technician observations. Maintenance teams must determine whether unusual behavior requires immediate intervention, can wait for the next planned stop or is simply normal variation.

A Time Series Language Model reviews the asset’s current condition alongside its operating and maintenance history. It explains which signals have changed, connects them with similar past failures and identifies the most plausible cause. The output includes a recommended inspection or maintenance action, the required expertise and parts, and the evidence supporting that recommendation.

By coordinating maintenance with production schedules, the factory can address problems at the least disruptive moment rather than reacting after a breakdown. The maintenance team reviews and approves the recommendation before any work order is issued. This increases OEE and MTBF while reducing unplanned downtime, emergency work and maintenance cost per operating hour.

EXEMPLARY DATA SOURCES

VIBRATIONTEMPERATUREPRESSUREPOWER CONSUMPTIONACOUSTIC SIGNALSCYCLE COUNTSMACHINE ALARMSPLC AND MES RECORDSMAINTENANCE WORK ORDERSTECHNICIAN REPORTSFAILURE HISTORYEQUIPMENT MANUALSSPARE-PARTS INVENTORYPRODUCTION SCHEDULE

Industrials: the signals a TSLM reads natively

BACK TO USE CASES

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