Transportation
21 columns
120 rows
8 formats

Fleet Predictive Maintenance Logs

This dataset offers comprehensive, timestamped logs of fleet vehicle sensor readings, maintenance events, and AI-predicted component failures. It enables transportation and logistics teams to analyze maintenance cycles, predict failures, and optimize interventions, ultimately reducing downtime and operational costs. The dataset supports advanced analytics for sustainability, reliability, and predictive maintenance strategies.

last updated 1 Feb 2026
by GoMask

The brief that made it

Predictive maintenance scheduling and optimization

Sample rows

preview · 8 of 120 rows · all 21 columns

log_idstring	maintenance_outcomestring	odometer_reading_kmfloat	failure_predictedboolean	maintenance_actionstring	vehicle_idstring	vehicle_makestring	vehicle_modelstring	vehicle_yearinteger	log_datetimedatetime	sensor_typestring	sensor_valuefloat	sensor_unitstring	component_servicedstring	maintenance_datetimedatetime	failure_probabilityfloat	predicted_failure_componentstring	intervention_requiredboolean	intervention_outcomestring	technician_idstring	notesstring
L0001	successful	45231.5	false	oil_change	V101	Ford	Transit	2018	2024-05-02T09:15:00	engine_temp	88.3	C	engine	2024-05-02T10:05:00	0.07	blank	false	blank	T201	Routine scheduled oil change performed. No anomalies detected.
L0002	successful	62345.2	false	inspection	V102	Toyota	Hiace	2016	2024-05-03T11:45:00	tire_pressure	32.1	psi	tires	2024-05-03T12:20:00	0.06	blank	false	blank	T202	Tire pressure inspected as part of scheduled maintenance.
L0003	successful	75820.8	false	battery_replacement	V103	Mercedes	Sprinter	2015	2024-05-05T13:30:00	battery_voltage	12.5	V	battery	2024-05-05T14:15:00	0.1	blank	false	blank	T203	Battery replaced due to scheduled maintenance cycle.
L0004	successful	164321.7	true	oil_change	V104	Chevrolet	Express	2012	2024-05-07T08:45:00	oil_pressure	18.2	psi	engine	2024-05-07T09:15:00	0.75	oil_pump	true	prevented_failure	T204	Low oil pressure detected. AI flagged likely oil pump failure. Oil changed and pump inspected.
L0005	successful	120943.9	true	brake_replacement	V105	Volkswagen	Crafter	2014	2024-05-09T14:55:00	brake_pad_wear	2.1	mm	brakes	2024-05-09T15:45:00	0.83	brake_pads	true	prevented_failure	T205	Brake pad wear below threshold. AI predicted imminent failure. Pads replaced promptly.
L0006	successful	193450.2	true	battery_replacement	V106	Renault	Master	2010	2024-05-11T07:30:00	battery_voltage	8.6	V	battery	2024-05-11T08:00:00	0.62	battery	true	prevented_failure	T206	AI flagged low battery voltage. Battery replaced to prevent breakdown.
L0007	successful	18512	false	inspection	V107	Nissan	NV350	2020	2024-05-13T15:40:00	engine_temp	94.7	C	cooling_system	2024-05-13T16:15:00	0.09	blank	false	blank	T207	Engine temperature within normal range. Cooling system inspected as precaution.
L0008	successful	7709.4	false	tire_rotation	V108	Fiat	Ducato	2022	2024-05-15T10:20:00	tire_pressure	34.2	psi	tires	2024-05-15T11:00:00	0.03	blank	false	blank	T208	Routine tire rotation completed. Tire pressures checked.
L0009	successful	238732.1	true	oil_change	V109	Peugeot	Boxer	2008	2024-05-17T08:00:00	oil_pressure	9.7	psi	engine	2024-05-17T08:45:00	0.78	oil_pump	true	prevented_failure	T209	Oil pressure critically low. Maintenance performed to prevent engine damage.
L0010	successful	189120	true	brake_replacement	V110	Ford	Transit	2011	2024-05-19T14:30:00	brake_pad_wear	1.6	mm	brakes	2024-05-19T15:15:00	0.82	brake_pads	true	prevented_failure	T210	Brake pads worn. AI recommended immediate replacement.
L0011	successful	38221.5	false	inspection	V111	Toyota	Hiace	2019	2024-05-21T09:10:00	engine_temp	89.6	C	engine	2024-05-21T09:45:00	0.08	blank	false	blank	T211	Engine temperature in normal range. Routine inspection performed.
L0012	blank	52903.2	false	blank	V112	Mercedes	Sprinter	2017	2024-05-23T13:55:00	battery_voltage	12.4	V	blank	blank	0.11	blank	false	blank	T212	Battery voltage stable. No maintenance required.
L0013	successful	5108.3	false	inspection	V113	Chevrolet	Express	2023	2024-05-25T16:20:00	tire_pressure	33.7	psi	tires	2024-05-25T17:05:00	0.02	blank	false	blank	T213	New vehicle. Tire pressures inspected as part of standard delivery.
L0014	blank	74567.5	false	blank	V114	Volkswagen	Crafter	2016	2024-05-27T08:05:00	engine_temp	92.5	C	blank	blank	0.12	blank	false	blank	T214	Engine temperature within normal range. No action required.
L0015	follow-up required	298124.9	true	inspection	V115	Renault	Master	2005	2024-05-29T14:30:00	oil_pressure	13.4	psi	oil_pump	2024-05-29T14:55:00	0.53	oil_pump	true	no_action	T215	High-mileage vehicle. Oil pressure lower than optimal. Follow-up scheduled.
L0016	successful	314562.7	false	inspection	V116	Nissan	NV350	2001	2024-06-01T11:50:00	brake_pad_wear	3.3	mm	brakes	2024-06-01T12:25:00	0.18	blank	false	blank	T216	Periodic brake inspection for high-mileage vehicle.
L0017	blank	13249.1	false	blank	V117	Fiat	Ducato	2021	2024-06-03T09:10:00	engine_temp	87.2	C	blank	blank	0.04	blank	false	blank	T217	Engine temperature normal. No service required.
L0018	blank	801.3	false	blank	V118	Peugeot	Boxer	2024	2024-06-05T16:00:00	tire_pressure	35.3	psi	blank	blank	0.02	blank	false	blank	T218	New vehicle. No maintenance necessary.
L0019	successful	312784.6	true	overhaul	V119	Ford	Transit	2003	2024-06-07T09:30:00	engine_temp	93.5	C	engine	2024-06-07T11:00:00	0.42	engine	false	blank	T219	Engine nearing overhaul interval. Preventive maintenance performed.
L0020	successful	345921	true	battery_replacement	V120	Toyota	Hiace	1999	2024-06-09T15:25:00	battery_voltage	10.2	V	battery	2024-06-09T16:00:00	0.56	battery	true	prevented_failure	T220	Old battery flagged by AI. Replaced as precaution.

What the 120 rows show

from the 120-row sample

Successful (maintenance outcome) stands out: mean odometer_reading_km is 147,226, against 513,824 for the rest.

39%failure_predicted = true
101,152median odometer_reading_km
3intervention outcomes
8sensor units
10sensor types
10predicted failure components

Mean odometer_reading_km by maintenance_outcome73 rows

odometer_reading_km120 rows, in bands of 100k

Median 101,152, from 0.0 to 1,000,000.

maintenance_action73 rows with a value · 47 left blank

inspection23
battery_replacement10
oil_change9
brake_pad_replacement6
tire_rotation5
firmware_update5
brake_replacement4
overhaul4
airbag_replacement2
oil_pressure_sensor_replacement1

21 columns by typefrom the column list below

string 13
integer 1
float 3
datetime 2
boolean 2

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 13 columns
`log_id`	string	Unique identifier for each maintenance log entryunique	L0001
`vehicle_id`	string	Unique identifier for the vehicle	V101
`vehicle_make`	string	Manufacturer of the vehicleoptional	Ford
`vehicle_model`	string	Model of the vehicleoptional	Transit
`sensor_type`	string	Type of sensor that generated the reading (e.g., engine_temp, oil_pressure)10 types	engine_temp
`sensor_unit`	string	Unit of measurement for the sensor value (e.g., C, psi, km/h)8 units · optional	C
`maintenance_action`	string	Type of maintenance action performed (e.g., oil_change, brake_replacement, inspection)optional	oil_change
`component_serviced`	string	Name of the vehicle component serviced or inspectedoptional	engine
`maintenance_outcome`	string	Result of the maintenance action (e.g., successful, failed, follow-up required)successful · failed · follow-up required · optional	successful
`predicted_failure_component`	string	Component for which failure was predicted10 components · optional	oil_pump
`intervention_outcome`	string	Outcome of the intervention taken based on AI prediction (e.g., prevented_failure, no_action, failure_occurred)prevented_failure · no_action · failure_occurred · optional	prevented_failure
`notes`	string	Additional notes or comments about the log entryoptional	Routine scheduled oil cha…
`technician_id`	string	Identifier for the technician who performed the maintenanceoptional	T201
Numbers 4 columns
`vehicle_year`	integer	Year the vehicle was manufactured1,980 to 2,100 · optional	2018
`odometer_reading_km`	float	Odometer reading in kilometers at the time of log entry0 or more	45231.5
`sensor_value`	float	Value recorded by the sensor	88.3
`failure_probability`	float	Probability (0-1) of component failure as predicted by AI0 to 1 · optional	0.07
Dates and times 2 columns
`log_datetime`	datetime	Date and time when the log entry was recorded	2024-05-02T09:15:00
`maintenance_datetime`	datetime	Date and time when the maintenance action was performedoptional	2024-05-02T10:05:00
True or false 2 columns
`failure_predicted`	boolean	Indicates if an AI-driven failure was predicted for this log entryoptional	false
`intervention_required`	boolean	Indicates if immediate intervention was recommended by the AIoptional	false

Use it for

A transportation dashboard
The failure_predicted rate, odometer_reading_km by maintenance_outcome and a breakdown of maintenance_action. Excel, Power BI or Tableau.
Why do the 56 successful rows have a mean odometer_reading_km of 147,226?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Logs120
L000145231.5successf…
L0004164321.7successf…
L0005120943.9successf…
A software demo
Believable logs with vehicle_id, vehicle_make and vehicle_model to fill a screen in front of a buyer.

This dataset120 rows21 columns

Yours10,000 rows21 columns

blueprint · fleet-predictive-maintenance-logs

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 21 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 vehicle maintenance event.
Sensor readings are recorded prior to maintenance actions.
AI failure prediction score must be included for each event.
Maintenance action type and technician notes are logged.
Downtime duration and component replaced must be specified.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: fleet-predictive-maintenance-logs

What should your data show?

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Sample rows

What the 120 rows show

Columns

Use it for

A transportation dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Transportation

What should your data show?