Sensor Degradation Detection Using AI Signal Analysis
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Solution Overview
Problem
Automotive sensors and actuators, such as oxygen sensors, degrade over time, leading to delayed responses in emissions control systems, resulting in over- or under-compensation, and current systems lack effective methods to detect degradation before failure, which can degrade emissions performance.
Innovation Solution
A method using an artificial intelligence program to analyze output signal data from sensors and actuators, comparing patterns to nominal data, identifying degradation thresholds, and modifying control signals to maintain system performance, with a fault box simulating degrading conditions to predict and mitigate failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current sensor monitoring systems use simple fault indicators, then the system complexity is low, but the system cannot detect sensors which are degrading but have not yet failed
Solution Approach 1:
The system performs preliminary analysis of sensor signal patterns to detect degradation trends before actual failure occurs. By continuously monitoring signal characteristics and comparing them against nominal patterns, the system identifies early signs of sensor degradation, enabling preventive maintenance before the sensor completely fails.
Solution Approach 2:
The system implements a feedback mechanism where sensor output signals are continuously analyzed and compared against expected patterns. When deviations are detected, the system provides feedback about the degradation state, allowing for adaptive responses such as adjusting control parameters or alerting operators before complete failure occurs.
2Productivity
If the control system is tuned based on nominal oxygen sensor performance, then the system operates optimally when the sensor is new, but emissions performance degrades when the sensor degrades from nominal
Solution Approach 1:
The system transitions from a static control approach (tuned for nominal sensor performance) to a dynamic approach that continuously adapts to changing sensor conditions. By monitoring signal patterns in real-time and detecting degradation trends, the control system can dynamically adjust parameters to maintain optimal emissions performance despite sensor aging.
Solution Approach 2:
The system detects changes in sensor signal characteristics and uses this information to adjust control parameters accordingly. When degradation is detected, the system modifies operating parameters to compensate for the deteriorating sensor performance, maintaining emissions control efficiency throughout the sensor's operational life.
3Ease of operation
If the fuel control system reacts to time delays in sensor response, then the control system responds to apparent sensor behavior, but the system over-compensates and under-compensates when the sensor is degraded
Solution Approach 1:
The system introduces an intermediary analysis layer between the raw sensor signal and the control response. Instead of directly reacting to sensor output, the system first analyzes signal patterns to distinguish between normal response variations and degradation-induced delays. This intermediary analysis prevents inappropriate control actions based on misleading sensor signals.
Data Source
AI summary
A method to detect and mitigate sensor degradation in an automobile system includes: collecting output signal data from at least one of a sensor and an actuator which is outputting the signal data related to operational parameters of a vehicle system; placing the sensor or the actuator in communication with a fault box used to purposely corrupt the output signal data; analyzing patterns of the output signal data compared to signal data from a nominal operating sensor or actuator using an artificial intelligence program; identifying when a statistical range of the patterns exceeds a first threshold level; and modifying a control signal to change the operational parameters of the vehicle system.