Manufacturing IoT Predictive Maintenance

This dataset provides granular, real-time sensor readings and predictive maintenance insights from manufacturing machinery, including operational status, issue forecasts, and recommended actions. It enables operations managers to proactively schedule maintenance, minimize unplanned downtime, and optimize supply chain resilience through data-driven decision-making.

  • last updated 24 Oct 2025
  • by GoMask
The brief that made it

Predictive maintenance scheduling for manufacturing equipment

Sample rows

preview · 8 of 140 rows · all 18 columns
machine_idstringmachine_typestringsensor_valuefloatmaintenance_performedbooleanlocation_namestringlocation_idstringsensor_idstringsensor_typestringtimestampdatetimeunitstringoperational_statusstringpredicted_issuestringissue_probabilityfloatrecommended_actionstringscheduled_maintenance_datedatedowntime_estimate_minutesintegerlast_maintenance_datedateoperator_idstring
MCH-101Hydraulic Press X25.82blankPlant NorthLOC-01SEN-2001pressure2024-05-02T09:17:36barrunningblankblankblankblankblank2024-04-10OP-301
MCH-102Robotic Arm RX0.007blankPlant EastLOC-02SEN-2002vibration2024-05-03T11:05:22grunningblankblankblankblankblank2024-03-28OP-412
MCH-103Conveyor Belt CB10037.5blankPlant WestLOC-03SEN-2003temperature2024-05-03T14:24:11Celsiusrunningblankblankblankblankblank2024-04-09OP-520
MCH-104Assembly Robot Z210.45blankPlant CentralLOC-04SEN-2004current2024-05-02T16:39:20Ampererunningblankblankblankblankblank2024-03-30OP-105
MCH-105CNC Mill Series 765.4blankPlant SouthLOC-05SEN-2005temperature2024-05-01T10:55:40Celsiusrunningblankblankblankblankblank2024-04-02OP-301
MCH-106Hydraulic Press X20.01falsePlant NorthLOC-06SEN-2006pressure2024-05-02T13:28:55barfaultPressure drop detected0.96Emergency shutdown and inspection2024-05-041202024-03-29OP-105
MCH-107Robotic Arm RX5falsePlant EastLOC-02SEN-2007vibration2024-05-03T08:12:01gfaultAbnormal vibration spike0.89Replace vibration sensor2024-05-03902024-04-04OP-301
MCH-108Conveyor Belt CB10099.9falsePlant WestLOC-03SEN-2008humidity2024-05-02T12:36:43percentfaultHumidity spike detected0.91Check for leaks and dry area2024-05-04752024-04-06OP-412

What the 140 rows show

from the 140-row sample

Prototype X (machine type) stands out: mean sensor_value is 2,596,658, against 86,467 for the rest.

  • 30%maintenance_performed = true
  • 5.0median sensor_value
  • 5operational statuses
  • 6sensor types
  • 7units
  • 16operators
Mean sensor_value by machine_type140 rows
02M4M123.3Hydr…34 ro…68.7Robo…30 ro…19,129Conv…24 ro…640.8kAsse…16 ro…2.6MProt…16 ro…157.6Asse…14 ro…350.1CNC …6 rows
sensor_value140 rows, in bands of 2M
07014031320005-2M4M10Msensor_value →

Median 5.0, from -40.0 to 9,999,999.

location_name125 rows with a value · 15 left blank
  1. Plant North27
  2. Plant Central27
  3. Plant South22
  4. Plant East21
  5. Plant West21
  6. Plant Test7
18 columns by typefrom the column list below
  • string 11
  • integer 1
  • float 2
  • date 2
  • datetime 1
  • boolean 1

Columns

18 columns in four groups
blueprint · 18 columns
columntypedescriptionexample
Text 11 columns
machine_idstringUnique identifier for each manufacturing machineuniqueMCH-101
location_idstringIdentifier for the physical location or plant where the machine is installedLOC-01
location_namestringHuman-readable name of the machine's location or plant6 names · optionalPlant North
machine_typestringType or model of the manufacturing machine7 typesHydraulic Press X2
sensor_idstringUnique identifier for the sensor collecting data from the machineSEN-2001
sensor_typestringType of sensor (e.g., vibration, temperature, pressure)6 valuespressure
unitstringUnit of measurement for the sensor value (e.g., Celsius, RPM, psi)7 unitsbar
operational_statusstringCurrent operational status of the machinerunning · idle · maintenance · fault · shutdownrunning
predicted_issuestringPredicted maintenance issue based on sensor analyticsoptionalPressure drop detected
recommended_actionstringRecommended maintenance or operational actionoptionalReplace vibration sensor
operator_idstringIdentifier for the operator responsible for the machine at the time of readingoptionalOP-301
Numbers 3 columns
sensor_valuefloatMeasured value from the sensor at the given timestamp5.82
issue_probabilityfloatProbability (0-1) that the predicted issue will occur0 to 1 · optional0.96
downtime_estimate_minutesintegerEstimated downtime in minutes if maintenance is performed0 or more · optional120
Dates and times 3 columns
timestampdatetimeDate and time when the sensor reading was recorded2024-05-02T09:17:36
scheduled_maintenance_datedateDate when maintenance is scheduled based on predictive analyticsoptional2024-05-04
last_maintenance_datedateDate when the machine was last maintainedoptional2024-04-10
True or false 1 column
maintenance_performedbooleanIndicates if maintenance was performed as scheduledoptionalfalse

Use it for

  • maintenance pe…30%17 of 56 rowsmean sensor value by …123.3Hydr…68.7Robo…19.1kConv…640.8kAsse…

    A manufacturing dashboard

    The maintenance_performed rate, sensor_value by machine_type and a breakdown of location_name. Excel, Power BI or Tableau.

  • Why do the 16 Prototype X rows have a mean sensor_value of 2,596,658?

    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 machines with location_id, location_name and machine_type to fill a screen in front of a buyer.

Not quite right?

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This dataset140 rows18 columns
Yours1,000 rows18 columnslocation_id: UK only

blueprint · manufacturing-iot-predictive-maintenance

Behind this dataset

Same schema. As many rows as you need.

These 140 rows came out of a blueprint — 18 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 machine's daily sensor summary.
  • Includes vibration, temperature, and energy usage readings.
  • Flags potential failure risk based on threshold crossings.
  • Tracks maintenance events and downtime per machine.
  • Links predicted maintenance need to supply chain impact.
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
manufacturing-iot-predictive-maintenance

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