Vehicle Diagnostics Using Sensor Fusion and Machine Learning
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Solution Overview
Problem
Vehicles often experience false positive state detection due to sensor fluctuations, leading to increased maintenance costs and occupant frustration, as existing systems struggle to accurately diagnose issues like low tire pressure when the actual problem is a different component, such as a broken damper.
Innovation Solution
Implementing sensor fusion techniques using machine learning to process multiple sensor signals and identify the correct component causing changes in vehicle vibrations, thereby reducing false positives and improving diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-sensor detection is used, then the system is simple and easy to implement, but false positive detection increases and diagnostic accuracy decreases
Solution Approach 1:
The patent combines multiple sensor signals (accelerometer, gyroscope, wheel speed sensors, steering angle sensors) into a unified diagnostic system. By merging data from these different sensors, the system achieves more accurate component fault identification while reducing false positives, directly resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The diagnostic system is designed to handle multiple vehicle components (tires, dampers, wheel hubs, brakes) using a single multi-functional platform that processes various sensor inputs. This universal approach improves diagnostic accuracy across different components without proportionally increasing system complexity.
2Reliability
If multiple sensors are integrated for better diagnosis, then diagnostic accuracy improves, but system complexity and processing requirements increase
Solution Approach 1:
The patent replaces complex mechanical diagnostic procedures with an electronic sensor fusion system that uses signal processing and pattern recognition algorithms. This substitution maintains high diagnostic reliability while managing system complexity through software-based solutions rather than additional mechanical components.
Solution Approach 2:
The system introduces signal processing algorithms and data fusion techniques as intermediaries between raw sensor data and diagnostic conclusions. These intermediaries process and integrate multiple sensor inputs, improving diagnostic reliability while keeping the overall system architecture manageable through standardized processing pipelines.
3Measurement precision
If sensor fusion with machine learning is implemented, then false positives are reduced and diagnostic accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary processing of sensor signals by continuously monitoring and pre-processing data streams before full diagnostic analysis is required. This preliminary action prepares data in advance, reducing the computational burden during actual diagnostic events and minimizing processing time while maintaining high accuracy.
Solution Approach 2:
The patent implements a layered diagnostic approach where basic sensor fusion provides continuous partial monitoring, and full machine learning-based analysis is activated only when anomalies are detected. This partial action strategy maintains high state detection accuracy while reducing overall processing time by avoiding continuous full-scale computational analysis.
Data Source
AI summary
In general, techniques are described by which provide vehicle diagnostics and maintenance. A device comprising an interface and a processor may be configured to perform the techniques. The interface may be configured to communicate with a plurality of sensors to obtain a plurality of sensor signals representative of one or more states of a vehicle. The processor may be configured to apply a trained classifier with respect to the plurality of sensor signals to identify one or more components of the vehicle that result in a change of the vibration during operation of the vehicle.