Datasets:

CJJones
/

Vehicle_Diagnostics_LLM_Training_Sample

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.

The full CJ Jones' synthetic dataset catalog is available at: https://datadeveloper1.gumroad.com

Note: This Vehicle Diagnostic dataset has had 2 purchases, after 6, the license is going to change, it will be fully free and open source to the world and a new version 2.0 will be developed.

Want more? 🚀 Get the AI Startup Bundle from Gumroad.

💡 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"
}


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