Manufacturing
18 columns
120 rows
8 formats

Factory Equipment Predictive Failure Logs

This dataset provides detailed predictive failure logs for manufacturing equipment, including sensor readings, predicted failure types, severity, and recommended actions. It is ideal for developing and benchmarking predictive maintenance models, optimizing part replacement schedules, and minimizing plant downtime through actionable insights.

opened 6 times
last updated 6 Jan 2026
by GoMask

The brief that made it

Benchmarking and training predictive maintenance algorithms

Sample rows

preview · 8 of 120 rows · all 18 columns

log_idstring	predicted_failure_severitystring	failure_probabilityfloat	failure_predictedboolean	predicted_failure_typestring	equipment_idstring	equipment_typestring	locationstring	predicted_failure_datetimedatetime	current_statusstring	last_maintenance_datedate	operating_hours_since_last_maintenancefloat	sensor_temperaturefloat	sensor_vibrationfloat	sensor_pressurefloat	recommended_actionstring	action_due_datedate	notesstring
LOG-1023A	critical	0.97	true	mechanical	EQ001	CNC Machine	Plant A - Machining Bay 1	2024-07-11T08:45:00	running	2024-06-20	1248.6	117.8	8.5	9.2	emergency shutdown and inspection	2024-07-11	Critical temperature and vibration detected; immediate shutdown required.
LOG-CRIT-821	critical	0.93	true	mechanical	EQ002	Hydraulic Press	Plant B - Pressing Section	2024-07-10T14:20:00	running	2024-06-15	1547.2	129.1	9.9	11.7	replace hydraulic fluid and overhaul	2024-07-10	Hydraulic fluid overheating and excessive vibration—risk of catastrophic failure.
LOG-2334B	moderate	0.43	true	electrical	EQ003	Conveyor Belt	Plant A - Assembly Line 3	2024-07-16T10:00:00	running	2024-06-28	816.5	52.6	3.7	4.4	inspect electrical wiring	2024-07-15	Voltage spike detected in conveyor motor; inspection recommended.
LOG-EDGE-001	blank	blank	false	blank	EQ004	Air Compressor	Plant C - Utility Area	blank	idle	blank	blank	blank	blank	blank	blank	blank	Sensor offline; no recent data available.
LOG-4041C	blank	blank	false	blank	EQ001	CNC Machine	Plant A - Machining Bay 2	blank	running	2024-06-30	221.9	68.2	2.2	5.1	blank	blank	Performance nominal; no anomalies detected.
LOG-INV-033	moderate	0.39	true	software	EQ005	Injection Molder	Plant B - Plastics Area	2024-07-12T12:10:00	maintenance	2024-07-05	56.2	41.7	1.3	2.5	update firmware and monitor	2024-07-12	Detected software bug; update scheduled.
LOG-ERR-548	minor	0.07	true	software	EQ006	3D Printer	Plant D - Prototyping Lab	2024-07-20T09:15:00	idle	2024-07-06	22.8	28.4	0.6	blank	continue operation; monitor	2024-07-22	Minor software anomaly detected; monitoring advised.
LOG-2337E	blank	blank	false	blank	EQ007	Conveyor Belt	Plant B - Packaging Line	blank	maintenance	2024-07-09	0	19.5	0.3	0	blank	blank	Routine maintenance completed; system reset.
LOG-5542F	critical	1	true	mechanical	EQ002	Hydraulic Press	Plant B - Pressing Section	2024-07-13T16:50:00	offline	2024-06-18	1759.3	132.5	10	12	full system overhaul	2024-07-13	Complete system shutdown due to exceeding all thresholds.
LOG-4412G	critical	0.91	true	electrical	EQ008	Laser Cutter	Plant D - Cutting Zone	2024-07-12T19:30:00	running	2024-06-25	1146.8	101.2	7.4	8.1	replace power supply and inspect relay	2024-07-12	Voltage instability and excessive heat; urgent intervention required.
LOG-MIN-291	blank	blank	false	blank	EQ009	3D Printer	Plant C - Prototype Area	blank	running	2024-07-07	19.4	24.9	1.2	blank	blank	blank	Post-maintenance operation nominal.
LOG-EDGE-002	blank	blank	false	blank	EQ010	Conveyor Belt	Plant A - Receiving Dock	blank	offline	blank	9999.9	blank	blank	blank	blank	blank	System offline; data unavailable.
LOG-1123H	moderate	0.51	true	electrical	EQ011	Injection Molder	Plant B - Plastics Area	2024-07-18T13:05:00	running	2024-07-01	812.4	61.4	4.9	7.7	inspect circuit boards	2024-07-17	Electrical fluctuations detected; preventive maintenance recommended.
LOG-3367J	moderate	0.64	true	mechanical	EQ012	CNC Machine	Plant E - Fabrication Area	2024-07-21T08:00:00	running	2024-06-29	914.6	87.5	5.3	4.1	lubricate spindle and inspect bearings	2024-07-19	Spindle heating and moderate vibration detected.
LOG-1198K	minor	0.02	true	other	EQ013	Air Compressor	Plant C - Utility Area	2024-07-30T06:30:00	maintenance	2024-07-10	2.5	15.4	0	blank	monitor sensor calibration	2024-07-31	Sensor calibration warning; minimal risk.
LOG-6573L	minor	0.04	true	mechanical	EQ014	Laser Cutter	Plant D - Cutting Zone	2024-07-28T17:40:00	idle	2024-07-09	7.1	25.3	0.2	0.2	continue operation	2024-07-29	Minor anomaly detected during idle; no action required.
LOG-7743M	critical	0.96	true	mechanical	EQ015	Hydraulic Press	Plant E - Metal Forming	2024-07-17T22:10:00	offline	2024-06-14	1327.6	110.5	8.7	10.8	complete hydraulic overhaul	2024-07-17	Hydraulic pressure and temperature above safe threshold.
LOG-4532N	blank	blank	false	blank	EQ016	Injection Molder	Plant A - Plastics Line 1	blank	running	2024-07-08	33.7	48.6	1.5	3.2	blank	blank	Routine operation; all sensor readings nominal.
LOG-5624P	moderate	0.56	true	electrical	EQ017	CNC Machine	Plant F - Machining Cell	2024-07-22T09:10:00	running	2024-06-27	1187.2	77.9	6.2	6.8	replace relay and wiring check	2024-07-21	Current spikes and relay overheating detected.
LOG-MIN-023	blank	blank	false	blank	EQ018	Air Compressor	Plant B - Utility Zone	blank	idle	2024-07-09	0	21.5	0	blank	blank	blank	Idle, recently serviced.

What the 120 rows show

from the 120-row sample

Minor (predicted failure severity) stands out: mean failure_probability is 0.04, against 0.73 for the rest.

62%failure_predicted = true
0.56median failure_probability
4current statuses
7equipment types
267.8median operating_hours_since_last_maintenance
47.6median sensor_temperature

Mean failure_probability by predicted_failure_severity74 rows

failure_probability74 rows, in bands of 0.1

Median 0.56, from 0.01 to 1.0.

predicted_failure_type74 rows with a value · 46 left blank

mechanical35
electrical17
software12
other10

18 columns by typefrom the column list below

string 9
float 5
date 2
datetime 1
boolean 1

Columns

18 columns in four groups

blueprint · 18 columns
column	type	description	example
Text 9 columns
`log_id`	string	Unique identifier for each predictive failure log entryunique	LOG-1023A
`equipment_id`	string	Unique identifier for the equipment or machine	EQ001
`equipment_type`	string	Type or category of the equipment (e.g., CNC Machine, Conveyor Belt)7 types	CNC Machine
`location`	string	Physical location or section of the plant where the equipment is installed	Plant C - Utility Area
`predicted_failure_type`	string	Type of failure predicted (e.g., mechanical, electrical, software)mechanical · electrical · software · other · optional	mechanical
`predicted_failure_severity`	string	Severity level of the predicted failure (e.g., minor, moderate, critical)minor · moderate · critical · optional	critical
`current_status`	string	Current operational status of the equipment (e.g., running, idle, maintenance)running · idle · maintenance · offline	running
`recommended_action`	string	Recommended maintenance or operational action (e.g., inspect, replace part, continue operation)optional	full system overhaul
`notes`	string	Additional notes or comments from the predictive system or maintenance teamoptional	Idle, recently serviced.
Numbers 5 columns
`operating_hours_since_last_maintenance`	float	Number of operating hours since the last maintenance0 or more · optional	1248.6
`sensor_temperature`	float	Current temperature reading from the equipment's sensor (in Celsius)optional	117.8
`sensor_vibration`	float	Current vibration reading from the equipment's sensor (in mm/s)optional	8.5
`sensor_pressure`	float	Current pressure reading from the equipment's sensor (in bar)optional	9.2
`failure_probability`	float	Probability score (0-1) that a failure will occur within the predicted window0 to 1 · optional	0.97
Dates and times 3 columns
`predicted_failure_datetime`	datetime	Date and time when the failure is predicted to occuroptional	2024-07-11T08:45:00
`last_maintenance_date`	date	Date when the equipment last underwent maintenanceoptional	2024-06-20
`action_due_date`	date	Date by which the recommended action should be performedoptional	2024-07-11
True or false 1 column
`failure_predicted`	boolean	Indicates whether a failure is predicted for the equipment	true

Use it for

A manufacturing dashboard
The failure_predicted rate, failure_probability by predicted_failure_severity and a breakdown of predicted_failure_type. Excel, Power BI or Tableau.
Why do the 17 minor rows have a mean failure_probability of 0.04?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Logs120
LOG-1023A0.97critical
LOG-CRIT-8210.93critical
LOG-EDGE-001—
A software demo
Believable logs with equipment_id, equipment_type and location to fill a screen in front of a buyer.

This dataset120 rows18 columns

Yours1,000 rows18 columnslocation: UK only

blueprint · factory-equipment-predictive-failure-logs

Behind this dataset

Same schema. As many rows as you need.

These 120 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 unique equipment event
Include timestamp, equipment type, alert severity, and predicted failure cause
No duplicate event timestamps per equipment
Only high-severity events contain technician recommendations
Probability of failure must align with age and usage hours of equipment

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-equipment-predictive-failure-logs

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?