Motor / Turbine Current · ampsabnormal draw
Breakdowns rarely arrive without warning - the electrical signature usually drifts first. Munshi watches current and load continuously, times every stop from the machine itself, and ties each repair to the spare it consumed.
Your ERP logs the breakdown: downtime hours, the spare consumed.
It won't tell you the motor had been drawing abnormally for nine days first.
Why the gap existsWhere the losses hide
What Munshi does
Running, Idle, Off - derived continuously from current and voltage. Every stop is timestamped by the system, with its duration. Not reconstructed from what someone remembers at handover.
Continuous three-phase current and load monitoring. When draw drifts abnormal - a turbine pulling low, a motor pulling high - Munshi flags it while it is still a reading.
What was done - cleaning, inspection, lubrication, tightening, adjustment, repair, part replacement, calibration - the condition afterward, the trial run and result. Each linked to the downtime it resolved.
Every machine carries its bill of materials - assemblies, sub-assemblies, components - each tied to a spare in inventory. So ‘how often do we replace the bearing on Motor 3, and what has it cost this year’ becomes a question with an answer.
Running, idle and off per machine per period - automatically. The machines dragging the line down are visible without anyone tallying a thing.
Machine supervisor first. If the issue persists, operations head, then plant head - the right person at the right severity, automatically, so a small reading does not sit unseen until it is a stoppage.
Work Orders & Audit Trail
A maintenance job is a work order, not a note in a register. Munshi carries it through its full lifecycle and records every step, so months later you can see exactly what was done, by whom, and what happened next.
Every transition is timestamped and attributed - raised, started, completed, trial-run result, submitted, approved. Records are soft-deleted, never erased: if something is removed, who removed it and when stays on the trail.
What was done - cleaning, inspection, lubrication, tightening, adjustment, repair, part replacement, calibration - with activity details, equipment condition afterward, and whether a trial run was taken and how it went.
Created by, created at, start and end, status changes and approvals - all captured automatically against the job. The history is the record; no one reconstructs it later.
Machine status history and every logged deviation sit against the asset itself - so a work order can be read alongside what the machine was actually doing before and after.
Insights & Alerts
Live examples of the maintenance losses Munshi surfaces - teal is normal, maroon is flagged. Illustrative data.
Representative findings
Illustrative of what teams uncover once the data sits in one place.
Utilization read 27.8% - not what the shift logs implied. The idle hours were real all along; they had simply never been measured against running time.
Surfaced automatically from IoT
Running / idle / off, per machine
A motor / turbine's current drifted below its baseline days before the blades actually failed - the early signature was sitting in the data the whole time.
Continuous current monitoring
Abnormal draw flagged early
One machine accounted for most of the month's unplanned stops - in short bursts that no single shift noticed, but the pattern was unmistakable once pooled.
Every stop timed and reasoned
Frequency and duration, per asset
Calculated automatically
You know the machines best. Munshi makes sure nothing slips through.
Get started
Tell us one line or one machine where you suspect losses, and we will show you exactly what Munshi would surface there - mapped to your operation, not a generic demo.