Factory IoT Sensor Event Logs

This dataset provides detailed, timestamped logs of events captured by factory IoT sensors, including machine uptime, downtime, predictive maintenance alerts, and anomaly detection. Each record links sensor readings to specific machines, event types, and operator actions, enabling in-depth analysis of equipment performance, incident resolution, and operational efficiency. Ideal for predictive maintenance, downtime analysis, and continuous improvement initiatives in manufacturing environments.

  • last updated 8 Jan 2026
  • by GoMask
The brief that made it

Analyzing equipment uptime and downtime trends

Sample rows

preview · 8 of 120 rows · all 15 columns
event_idstringevent_typestringanomaly_scorefloatpredicted_failurebooleansensor_unitstringevent_timestampdatetimesensor_idstringmachine_idstringevent_severitystringsensor_valuefloatresolvedbooleanresolved_timestampdatetimeoperator_idstringlocationstringevent_descriptionstring
EVT00001uptimeblankfalseCelsius2024-06-12T06:05:10ZSENS-A123MACH-01blank38.5true2024-06-12T06:09:02ZOPR-101Zone 1AMachine started, operational parameters normal.
EVT00002downtimeblankfalseRPM2024-06-12T14:32:45ZSENS-B231MACH-02blank0falseblankblankLine 4Unexpected stop detected during shift change.
EVT00003maintenance_alertblankfalsePSI2024-06-12T15:17:31ZSENS-C789MACH-03warning390.7falseblankblankBasementPressure reading approaching upper limit, maintenance advised.
EVT00004anomaly0.99falseCelsius2024-06-12T17:05:12ZSENS-D456MACH-04critical97.2falseblankblankRoofTemperature spike detected, possible overheating.
EVT00005infoblankfalseblank2024-06-12T08:21:33ZSENS-E102MACH-05blankblanktrue2024-06-12T08:23:44ZOPR-102Assembly WestRoutine sensor check completed successfully.
EVT00006downtimeblankfalseblank2024-06-12T00:02:17ZSENS-F982MACH-06blankblankfalseblankblankPacking AreaEquipment power failure detected.
EVT00007uptimeblankfalseRPM2024-06-12T09:15:05ZSENS-G563MACH-07blank6200true2024-06-12T09:18:04ZOPR-103Zone 2BNormal operation resumed after break.
EVT00008maintenance_alertblanktrueHours2024-06-12T18:11:54ZSENS-H341MACH-08critical4.2true2024-06-12T18:15:36ZOPR-104Loading DockCritical maintenance required: runtime threshold exceeded.

What the 120 rows show

from the 120-row sample

Maintenance_alert (event type) stands out: 10 of its 25 rows have predicted_failure = true, against 0 of 95 for the rest.

  • 8%predicted_failure = true
  • 0.99median anomaly_score
  • 2event severities
  • 30locations
  • 32.2median sensor_value
Predicted failure rate by event_typepredicted_failure = true
0%50%100%0%uptime0 of 260%downti…0 of 2540%mainte…10 of 250%anomaly0 of 240%info0 of 20
anomaly_score24 rows, in bands of 0.01
0482040507060.960.981anomaly_score →

Median 0.99, from 0.96 to 1.0.

sensor_unit88 rows with a value · 32 left blank
  1. RPM23
  2. Celsius16
  3. Amps15
  4. PSI11
  5. mm/s8
  6. Volts7
  7. Hours5
  8. mm3
15 columns by typefrom the column list below
  • string 9
  • float 2
  • datetime 2
  • boolean 2

Columns

15 columns in four groups
blueprint · 15 columns
columntypedescriptionexample
Text 9 columns
event_idstringUnique identifier for each sensor event log entryuniqueEVT00001
sensor_idstringUnique identifier for the IoT sensor reporting the eventSENS-A123
machine_idstringUnique identifier for the machine or equipment associated with the sensorMACH-01
event_typestringType of event detected (e.g., uptime, downtime, maintenance_alert, anomaly, info)uptime · downtime · maintenance_alert · anomaly · infouptime
event_severitystringSeverity level of the event (e.g., info, warning, critical)info · warning · critical · optionalwarning
event_descriptionstringDetailed description of the event or alertoptionalMachine started, operatio…
sensor_unitstringUnit of measurement for the sensor value (e.g., Celsius, RPM, Amps)8 units · optionalCelsius
operator_idstringIdentifier for the operator or technician who handled the eventoptionalOPR-101
locationstringPhysical location or zone of the machine/sensor within the factoryoptionalZone 1A
Numbers 2 columns
sensor_valuefloatSensor reading value at the time of the event (e.g., temperature, vibration, etc.)optional38.5
anomaly_scorefloatScore indicating the degree of anomaly detected (higher means more anomalous)0 or more · optional0.99
Dates and times 2 columns
event_timestampdatetimeDate and time when the event was recorded by the sensor2024-06-12T06:05:10Z
resolved_timestampdatetimeDate and time when the event was marked as resolvedoptional2024-06-12T06:09:02Z
True or false 2 columns
predicted_failurebooleanIndicates if the event is a predictive maintenance alert for potential failureoptionalfalse
resolvedbooleanIndicates whether the event or incident has been resolvedoptionaltrue

Use it for

  • predicted fail…8%10 of 120 rowspredicted failure by …0%upti…0%down…40%main…0%anom…

    A manufacturing dashboard

    The predicted_failure rate, anomaly_score by event_type and a breakdown of sensor_unit. Excel, Power BI or Tableau.

  • Why do 10 of 120 rows have predicted_failure = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable events with event_timestamp, sensor_id and machine_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 15 columns, your size and your rules. See 20 rows before you pay.

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This dataset120 rows15 columns
Yours1,000 rows15 columnslocation: UK only

blueprint · factory-iot-sensor-event-logs

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Each row represents a unique sensor event with a timestamp.
  • Event types include 'normal', 'maintenance required', 'anomaly detected', and 'downtime'.
  • Downtime events must include duration in minutes.
  • Maintenance events must be linked to a machine ID.
  • Sensors may report multiple events per day; event frequency varies by equipment type.
  • Anomaly events require a severity score.
Rows
Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
factory-iot-sensor-event-logs

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