IoT Sensor Anomaly Alerts

This dataset provides detailed event logs of anomaly alerts generated by distributed IoT sensors, including sensor readings, anomaly types, severity levels, location data, and resolution status. It is ideal for predictive maintenance, security event detection, and operational analytics in environments with large-scale sensor deployments.

  • last updated 5 Sept 2025
  • by GoMask
The brief that made it

Predictive maintenance for industrial equipment

Sample rows

preview · 8 of 201 rows · all 17 columns
alert_idstringsensor_typestringsensor_valuefloatmaintenance_requiredbooleanunit_of_measurementstringsensor_idstringlocation_namestringlocation_latitudefloatlocation_longitudefloatalert_timestampdatetimeanomaly_typestringseverity_levelstringexpected_valuefloatresolvedbooleanresolved_timestampdatetimealert_descriptionstringresolution_notesstring
TMP-5A7G9K2Xtemperature87.2falseCelsiusTMP-21294XZone A - Main Plant34.0522-118.24372024-06-05T08:13:44Zthreshold_breachmedium41.5true2024-06-05T10:31:16ZTemperature exceeded daily safe operating limits in Zone A.Adjusted cooling system set-point to restore normal temperature.
PRS-9K4YB2W7pressure0truekPaPRS-88120CWarehouse Deck 337.7749-122.41942024-06-06T14:26:11Zdevice_failurecritical101.2falseblankNo pressure reading detected; device offline.blank
VIB-42N6M8Q1vibration39.7trueHzVIB-98345LLine 2 - Assembly Area40.7128-74.0062024-06-06T09:03:27Zunexpected_patternhigh28.4falseblankDetected abnormal vibration frequency sequence during operation.blank
HMD-8L3D7T6Qhumidity72.8false%RHHMD-30571GTesting Lab - Room 751.5074-0.12782024-06-04T11:44:02Zthreshold_breachmedium55true2024-06-04T13:22:19ZHumidity exceeded recommended threshold in Room 7.Dehumidifier activated to lower humidity; values returned to normal.
MOT-1R9T5K8Jmotion15.2truem/s^2MOT-15479ECorridor B - East Wing48.85662.35222024-06-07T21:35:55Zsecurity_eventcritical0falseblankHigh acceleration detected in restricted corridor; possible intrusion.blank
OTHER-3B2F8J7Cother263.5falseunitsOTHER-11193SRemote Outpost 901802024-06-03T03:18:42Zotherlow260true2024-06-03T03:45:08ZMiscellaneous sensor reported minor signal fluctuation.Signal stabilized after remote diagnostic reset.
TMP-6W3P8V2Ntemperature32.1falseCelsiusTMP-49712BMain Floor - Zone C34.6937135.50232024-06-05T17:19:33Zthreshold_breachlow29.7true2024-06-05T17:46:50ZTemperature slightly above expected in Zone C.Minor adjustment to air flow resolved issue.
PRS-1E8U7L3Tpressure309.7truekPaPRS-66105FBay 5 - Loading Dock35.6895139.69172024-06-07T06:25:14Zthreshold_breachhigh102.5falseblankPressure spike detected at Bay 5; possible valve malfunction.blank

What the 201 rows show

from the 201-row sample

Other (sensor type) stands out: mean sensor_value is 3,336,841, against 69.4 for the rest.

  • 41%maintenance_required = true
  • 39.1median sensor_value
  • 4severity levels
  • 5anomaly types
  • 38.6median expected_value
Mean sensor_value by sensor_type201 rows
02M4M79.2tempe…42 rows21.6vibra…34 rows58.6humid…35 rows152.2press…39 rows10.7motion28 rows3.3Mother23 rows
sensor_value201 rows, in bands of 2M
09519041891007-2M4M10Msensor_value →

Median 39.1, from -20.1 to 9,999,999.

unit_of_measurement201 rows · 7 values
  1. Celsius42
  2. kPa39
  3. %RH35
  4. Hz34
  5. m/s^228
  6. units13
  7. lux10
17 columns by typefrom the column list below
  • string 9
  • float 4
  • datetime 2
  • boolean 2

Columns

17 columns in four groups
blueprint · 17 columns
columntypedescriptionexample
Text 9 columns
alert_idstringUnique identifier for each anomaly alert eventuniqueTMP-5A7G9K2X
sensor_idstringUnique identifier for the IoT sensor reporting the anomalyTMP-21294X
sensor_typestringType of IoT sensor (e.g., temperature, vibration, humidity)6 valuestemperature
location_namestringHuman-readable name or code for the sensor's locationZone A - Main Plant
anomaly_typestringCategory or type of detected anomaly (e.g., threshold breach, unexpected pattern, security event)5 valuesthreshold_breach
severity_levelstringSeverity level of the anomaly (e.g., low, medium, high, critical)low · medium · high · criticalmedium
unit_of_measurementstringUnit of measurement for the sensor value (e.g., Celsius, Hz, %RH)7 measurementsCelsius
alert_descriptionstringDetailed description or notes about the anomaly eventoptionalTemperature exceeded dail…
resolution_notesstringNotes or description of the resolution actions takenoptionalAdjusted cooling system s…
Numbers 4 columns
location_latitudefloatLatitude coordinate of the sensor's location-90 to 90 · optional34.0522
location_longitudefloatLongitude coordinate of the sensor's location-180 to 180 · optional-118.2437
sensor_valuefloatSensor reading value at the time of anomaly87.2
expected_valuefloatExpected or normal value for the sensor at the time of anomalyoptional41.5
Dates and times 2 columns
alert_timestampdatetimeTimestamp when the anomaly was detected2024-06-05T08:13:44Z
resolved_timestampdatetimeTimestamp when the anomaly was marked as resolvedoptional2024-06-05T10:31:16Z
True or false 2 columns
maintenance_requiredbooleanIndicates if immediate maintenance is required as a result of the anomalyfalse
resolvedbooleanIndicates if the anomaly has been resolvedtrue

Use it for

  • maintenance re…41%83 of 201 rowsmean sensor value by …79.2temp…21.6vibr…58.6humi…152.2pres…

    A manufacturing dashboard

    The maintenance_required rate, sensor_value by sensor_type and a breakdown of unit_of_measurement. Excel, Power BI or Tableau.

  • Why do the 23 other rows have a mean sensor_value of 3,336,841?

    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 alerts with sensor_id, sensor_type and location_name to fill a screen in front of a buyer.

Not quite right?

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This dataset201 rows17 columns
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blueprint · iot-sensor-anomaly-alerts

Behind this dataset

Same schema. As many rows as you need.

These 201 rows came out of a blueprint — 17 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
  • Timestamp every anomaly event
  • Categorize anomaly type
  • Include sensor location
  • Flag critical alerts
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
iot-sensor-anomaly-alerts

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