Vehicle Sensor Efficiency Analysis for Predictive Fault Detection
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Solution Overview
Problem
Existing vehicle diagnostics systems only report faults after they occur, as indicated by diagnostic trouble codes, failing to predict impending issues before they become critical.
Innovation Solution
A computer system that analyzes time-series data from vehicle sensors to determine efficiency metrics, particularly energy consumption per distance traveled, and uses machine-learning algorithms to predict potential faults, allowing for proactive fault detection and notification before they develop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional OBD-II diagnostic systems are used, then fault detection is standardized and implementable, but faults can only be detected after they occur rather than predicted beforehand
Solution Approach 1:
The system performs preliminary analysis of sensor data to detect efficiency deviations before actual faults occur. By continuously monitoring parameters like energy consumption and comparing them against expected ranges, the system predicts impending faults before they manifest as critical failures, enabling proactive maintenance scheduling.
Solution Approach 2:
The system calculates efficiency metrics in advance by analyzing sensor data patterns and comparing them to predetermined efficiency ranges. This preliminary efficiency assessment allows the system to identify deteriorating conditions before they result in actual faults, providing early warning signals.
2Measurement precision
If continuous sensor monitoring and efficiency analysis are implemented, then fault prediction accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The system extracts only the critical efficiency parameters from the full sensor data set that are most indicative of potential faults. By focusing computation on key metrics like energy consumption efficiency rather than analyzing all sensor data, the system maintains high detection accuracy while reducing computational complexity.
Solution Approach 2:
The system transforms raw sensor data into simplified efficiency metrics and comparisons against predetermined ranges. This parameter transformation converts complex multi-sensor data into manageable efficiency scores that are easier to process and interpret, reducing computational burden while maintaining diagnostic value.
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive time-series data from sensors of a vehicle, determine an efficiency of the vehicle, and determine a probability of a fault occurring in the vehicle based on the time-series data and on the efficiency. The efficiency is energy consumption by the vehicle per distance traveled by the vehicle.