Manufacturing
15 columns
120 rows
8 formats

Factory IoT Equipment Failure Prediction

This dataset provides granular, time-stamped sensor readings and maintenance logs for factory equipment, enabling predictive analysis of machine failures and operational downtime. It is ideal for manufacturing operations managers seeking to optimize maintenance schedules, reduce unexpected breakdowns, and improve overall equipment efficiency through data-driven insights.

last updated 17 Dec 2025
by GoMask

The brief that made it

Predictive maintenance modeling to reduce equipment downtime

Sample rows

preview · 8 of 120 rows · all 15 columns

machine_idstring	maintenance_typestring	downtime_minutesinteger	is_failureboolean	failure_typestring	machine_typestring	locationstring	timestampdatetime	sensor_temperaturefloat	sensor_vibrationfloat	sensor_pressurefloat	sensor_humidityfloat	maintenance_eventboolean	operator_idstring	maintenance_descriptionstring
CNC_101	none	0	false	blank	CNC	Zone A	2024-06-07T08:15:23Z	74.2	19.5	24.1	51.8	false	blank	blank
PRESS_202	corrective	105	true	mechanical	press	Line 3	2024-06-09T03:42:55Z	88.4	34.7	30.6	39.2	true	ENG_015	Mechanical jam detected and cleared; replaced faulty actuator.
CONV_303	none	0	false	blank	conveyor	Zone B	2024-06-08T12:28:16Z	48.6	11.2	blank	64.5	false	blank	blank
ROBOT_XTREM	corrective	230	true	electrical	robotic_arm	Cell 7	2024-06-05T23:56:03Z	121.7	42.8	18	29.7	true	SR-TECH_03	Electrical fault in main controller. Replaced relay and performed system reset.
LASER_2	preventive	18	false	blank	laser_cutter	Bay 5	2024-06-10T16:42:29Z	192.3	53	40.1	blank	true	TECH_402	Laser optics inspected and cleaned. Cooling fluid replaced as scheduled.
BLEND_X5	inspection	11	false	blank	blender	Zone C	2024-06-06T09:28:11Z	65	13.1	19.7	48.3	true	INSPECTOR_27	Routine inspection performed; blade assembly checked and lubricated.
PACK-002	corrective	62	true	software	packaging	Line 5	2024-06-09T19:11:44Z	57.1	25	blank	40.4	true	ADMIN_007	Software error caused packaging halt. Updated firmware and cleared queue.
WELD-04	inspection	9	false	blank	welder	Cell 2	2024-06-07T14:01:39Z	134.2	21.7	25.8	36.2	true	QC-LEAD	Weld tips checked for wear. No issues found during scheduled inspection.
DRYER-X	corrective	85	true	unknown	dryer	Zone D	2024-06-08T06:14:13Z	198.9	17.2	3.2	9.4	true	TECH_317	Unexplained shutdown; inspected heating coils and reset system.
INSPECT-9000	inspection	10	false	blank	inspector	Bay 9	2024-06-09T11:37:26Z	41	6.3	6.7	53	true	INSPECTOR_32	Sensor alignment checked; minor recalibration performed.
ROBOT_R17	corrective	320	true	mechanical	robotic_arm	Cell 1	2024-06-06T04:33:15Z	115.5	67.8	8.2	24.9	true	ENG_009	Joint failure replaced; recalibration performed on arm motors.
CNC_102	preventive	22	false	blank	CNC	Zone E	2024-06-07T18:20:53Z	62.3	15.9	34.5	55.7	true	TECH_221	Lubrication system refilled and spindle checked for wear as per schedule.
PRESS_203	none	0	false	blank	press	Line 4	2024-06-08T15:18:49Z	80.7	27.3	29	44.1	false	blank	blank
GRIND-EDGE	none	0	false	blank	grinder	Bay 3	2024-06-05T19:08:32Z	40.2	12.5	21.8	60.3	false	blank	blank
STORAGE_44	none	0	false	blank	storage_silo	Zone F	2024-06-10T02:50:55Z	31.8	6.1	blank	73.2	false	blank	blank
CNC_103	none	0	false	blank	CNC	Zone A	2024-06-06T20:22:34Z	98.9	27.7	38.9	41.6	false	blank	blank
ROBOT_C21	corrective	77	true	software	robotic_arm	Cell 3	2024-06-09T22:39:11Z	128.7	54	11.2	33.4	true	SR-TECH_21	Robotic software crash resolved with updated drivers and motor test.
PAINT_001	none	0	false	blank	paint_sprayer	Line 7	2024-06-07T04:55:42Z	69.1	17.2	27.3	58.6	false	blank	blank
CONV_304	none	0	false	blank	conveyor	Zone B	2024-06-09T13:04:58Z	53	20.6	blank	44	false	blank	blank
PACK_003	preventive	14	false	blank	packaging	Line 6	2024-06-08T18:10:27Z	61.3	24.5	20.2	38.1	true	TECH_452	Packaging sensors checked and cleaned. No issues found.

What the 120 rows show

from the 120-row sample

Corrective (maintenance type) stands out: mean downtime_minutes is 132.1, against 5.5 for the rest.

21%is_failure = true
7median downtime_minutes
13machine types
69.4median sensor_temperature
15.9median sensor_vibration
19.5median sensor_pressure

Mean downtime_minutes by maintenance_type120 rows

downtime_minutes120 rows, in bands of 50

Median 7, from 0 to 320.

failure_type25 rows with a value · 95 left blank

mechanical11
software7
electrical4
unknown3

15 columns by typefrom the column list below

string 7
integer 1
float 4
datetime 1
boolean 2

Columns

15 columns in four groups

blueprint · 15 columns
column	type	description	example
Text 7 columns
`machine_id`	string	Unique identifier for each factory machineunique	CNC_101
`machine_type`	string	Type or category of the machine (e.g., CNC, conveyor, press)	CNC
`location`	string	Physical location of the machine within the factory (e.g., Zone A, Line 3)	Zone A
`failure_type`	string	Type of failure detected (e.g., mechanical, electrical, software)mechanical · electrical · software · unknown · optional	mechanical
`maintenance_type`	string	Type of maintenance performed (e.g., preventive, corrective, inspection)preventive · corrective · inspection · none · optional	none
`maintenance_description`	string	Detailed description of the maintenance activityoptional	Mechanical jam detected a…
`operator_id`	string	Identifier for the operator or technician responsible for the machine or maintenance eventoptional	ENG_015
Numbers 5 columns
`sensor_temperature`	float	Temperature reading from the machine's sensor (in Celsius)-40 to 200	74.2
`sensor_vibration`	float	Vibration level from the machine's sensor (in mm/s)0 to 100	19.5
`sensor_pressure`	float	Pressure reading from the machine's sensor (in bar)0 to 50 · optional	24.1
`sensor_humidity`	float	Humidity reading from the machine's sensor (percentage)0 to 100 · optional	51.8
`downtime_minutes`	integer	Duration of machine downtime in minutes due to failure or maintenance0 or more · optional	0
Dates and times 1 column
`timestamp`	datetime	Date and time when the sensor reading or maintenance event was recorded	2024-06-07T08:15:23Z
True or false 2 columns
`is_failure`	boolean	Indicates whether a failure occurred at this timestamp	false
`maintenance_event`	boolean	Indicates whether a maintenance event occurred at this timestamp	false

Use it for

A manufacturing dashboard
The is_failure rate, downtime_minutes by maintenance_type and a breakdown of failure_type. Excel, Power BI or Tableau.
Why do the 25 corrective rows have a mean downtime_minutes of 132.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Machines120
CNC_1010none
PRESS_202105correcti…
ROBOT_XTREM230correcti…
A software demo
Believable machines with machine_type, location and timestamp to fill a screen in front of a buyer.

This dataset120 rows15 columns

Yours1,000 rows15 columnslocation: UK only

blueprint · factory-iot-equipment-failure-prediction

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 equipment entry must have unique sensor readings with timestamp.
Failure events are flagged if vibration or temperature exceed set thresholds.
Maintenance records linked to specific failure events when applicable.
Include simulated equipment age and usage metrics for each asset.
Environmental factors (humidity, dust) periodically impact failure probability.

Rows1,000 rows10,000 rows100,000 rows1,000,000 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-equipment-failure-prediction

What should your data show?

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Sample rows

What the 120 rows show

Columns

Use it for

A manufacturing dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Manufacturing

What should your data show?