Datasets:
Dataset Viewer
text
stringlengths 0
253
|
|---|
Input: Generate comprehensive vehicle diagnostic for commercial transmission system, config=automatic
|
Output: Diagnostic ID=17f869b8-179e-4c02-bfad-6837b0f0d4b0
|
Vehicle Type: commercial
|
System: transmission
|
Configuration: automatic
|
Status: normal (level 1)
|
Fault Code: P0796
|
Performance: 0.9 efficiency
|
Material: steel
|
Measurements:
|
fluid-level=0.99 ratio
|
torque=261.78 Nm
|
shift-time=0.14 seconds
|
fluid-temperature=87.01 °C
|
Parameters:
|
efficiency=0.83 percent
|
load=68.82 percent
|
temperature=79.68 °C
|
power=33.78 kW
|
vibration=2.93 mm/s
|
health_index=76.58 percent
|
Temporal Data:
|
temp_over_time=[77.2, 69.7, 73.0] °C
|
power_trend=[43.5, 33.3, 32.8] kW
|
performance_trend=[0.9, 1.0, 0.8] efficiency
|
Notes: Observed symptoms: hard shifting. Diagnostic trouble code: P0796. Diagnostic scan performed at 1751287335055. Recommended maintenance interval: 9 months. Recommended actions: filter replacement, fluid change, seal inspection.
|
Maintenance Recommendations: filter replacement, fluid change, seal inspection
|
Input: Generate comprehensive vehicle diagnostic for electric battery system, config=supercapacitor
|
Output: Diagnostic ID=55d09231-272f-4c4c-bfa5-af46a9ca6dd2
|
Vehicle Type: electric
|
System: battery
|
Configuration: supercapacitor
|
Status: degraded (level 2)
|
Fault Code: P0A80
|
Performance: 357.0 kWh
|
Material: lithium-ion
|
Measurements:
|
current=24.97 amps
|
state-of-charge=43.62 percent
|
voltage=728.16 volts
|
capacity=92.10 percent
|
Parameters:
|
efficiency=0.89 percent
|
load=47.83 percent
|
temperature=24.10 °C
|
power=375.42 kW
|
vibration=4.65 mm/s
|
health_index=73.48 percent
|
Sensors:
|
coolant_flow=0.8 L/min
|
cell_voltage=3.52 V
|
insulation=73.0 MΩ
|
Temporal Data:
|
temp_over_time=[23.0, 20.9, 19.2, 19.3, 19.4, 22.6] °C
|
power_trend=[428.4, 372.8, 440.5, 442.8, 402.7, 333.0] kW
|
performance_trend=[418.6, 328.3, 389.4, 348.5, 359.4, 312.7] kWh
|
Notes: Observed symptoms: slow charging. Diagnostic trouble code: P0A80. Diagnostic scan performed at 1751287335055. Recommended maintenance interval: 5 months. Recommended actions: charge test.
|
Maintenance Recommendations: charge test
|
Input: Generate comprehensive vehicle diagnostic for suv brakes system, config=disc
|
Output: Diagnostic ID=bb21d636-33be-4cd3-acd1-60b632ad85ee
|
Vehicle Type: suv
|
System: brakes
|
Configuration: disc
|
Status: degraded (level 2)
|
Fault Code: C0050
|
Performance: 0.9 deceleration_g
|
Material: carbon-ceramic
|
Measurements:
|
fluid-moisture=0.34 percent
|
rotor-thickness=10.17 mm
|
pad-thickness=5.63 mm
|
pressure=550.34 psi
|
Parameters:
|
efficiency=0.87 percent
|
load=79.78 percent
|
temperature=69.87 °C
|
power=27.55 kW
|
vibration=3.72 mm/s
|
health_index=58.03 percent
|
Sensors:
|
abs_status=active
|
wear_sensor=1.0 mm
|
pressure_sensor=1091.0 psi
|
Temporal Data:
|
temp_over_time=[45.8, 40.0, 42.6, 40.0] °C
|
power_trend=[31.2, 25.7, 25.2, 33.8] kW
|
performance_trend=[1.0, 0.8, 1.0, 1.1] deceleration_g
|
Notes: Observed symptoms: soft pedal. Diagnostic trouble code: C0050. Diagnostic scan performed at 1751287335055. Recommended maintenance interval: 3 months. Recommended actions: fluid flush, caliper inspection.
|
Maintenance Recommendations: fluid flush, caliper inspection
|
Input: Generate comprehensive vehicle diagnostic for electric battery system, config=nickel-metal
|
Output: Diagnostic ID=e68bb6d0-ee99-4ecb-80b6-b34f8f7796eb
|
Vehicle Type: electric
|
System: battery
|
Configuration: nickel-metal
|
Status: normal (level 1)
|
Fault Code: P0AC0
|
Performance: 222.9 kWh
|
End of preview. Expand
in Data Studio
Vehicle Diagnostic Sample Dataset
🧩 Dataset Summary
This dataset contains a sample subset of structured vehicle diagnostic logs generated for various vehicle types and subsystems, such as transmissions, battery systems, brakes, and engines. Each entry includes detailed parameters such as fault codes, performance metrics, measurements, temporal trends, and maintenance recommendations.
This subset (500 examples) is meant to demonstrate the structure and potential use cases of the full dataset available commercially on Gumroad for industrial, machine learning, and predictive maintenance applications.
💡 Use Cases
- Train models for fault prediction and diagnosis generation
- Fine-tune text-to-text models on structured industrial reports
- Build synthetic data generators for simulation platforms
- Analyze parameter trends for telemetry-driven maintenance planning
📁 Dataset Structure
Each entry follows the structure:
{
"input": "Generate comprehensive vehicle diagnostic for <vehicle> <system> system, config=<config>",
"output": "<structured diagnostic report with parameters, metrics, fault code, and recommendations>"
}
🧪 Example
{
"input": "Generate comprehensive vehicle diagnostic for commercial transmission system, config=automatic",
"output": "Diagnostic ID=17f869b8... torque=261.78 Nm... Maintenance Recommendations: filter replacement, fluid change, seal inspection"
}
📥 Loading the Dataset in Python
from datasets import load_dataset
dataset = load_dataset("cjjones/vehicle-diagnostic-sample")
print(dataset["train"][0])
- Downloads last month
- 157