Vehicle Failure Detection Using State Matrix Estimation
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Solution Overview
Problem
Existing methods struggle to accurately detect and identify failures in vehicle components, particularly before they completely fail, and differentiate between instant and gradual failures.
Innovation Solution
A method involving a failure detection module that uses onboard sensors to determine system states, calculates present state transition and input matrices, and detects failures by comparing these estimates to nominal values, issuing alarms or estimating time to failure based on component parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional failure detection methods are used, then detection simplicity is maintained, but measurement precision and ability to detect gradual failures deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously estimating system states and calculating deviation metrics before actual failures occur. The state estimation module proactively computes expected component behavior using system models, and the deviation detection module prepares to identify anomalies by comparing actual sensor readings against these pre-calculated expectations, enabling early failure detection before critical thresholds are reached.
Solution Approach 2:
The patent introduces intermediary modules between raw sensor data and failure detection. The state estimation module acts as an intermediary that processes sensor inputs through system models to generate expected state values. The deviation detection module then serves as another intermediary layer that compares actual versus expected states, isolating the complex analysis from direct sensor input and enabling precise failure detection without requiring direct complex processing of raw data.
2Reliability
If detailed component monitoring is implemented, then reliability improvement is achieved, but loss of time for data processing increases
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: state estimation module that processes sensor data, deviation detection module that identifies anomalies, and failure classification module that categorizes failure types. Each module handles specific aspects of the monitoring task independently, allowing parallel processing of different monitoring functions and reducing overall data processing time while maintaining comprehensive reliability monitoring.
Solution Approach 2:
The system implements partial monitoring by focusing computational resources on detecting deviations beyond normal tolerance ranges rather than continuously analyzing every parameter at maximum detail. The deviation detection module calculates metrics only when necessary to determine if anomalies exist, and the failure classification module activates selectively when deviations are detected, reducing unnecessary processing time while maintaining reliable failure detection capability.
3Speed
If real-time failure detection is implemented, then response time is improved, but device complexity increases
Solution Approach 1:
The state estimation module serves multiple functions simultaneously: it processes sensor inputs, predicts system states using embedded models, and provides baseline values for deviation comparison. The deviation detection module also performs multiple roles by comparing actual versus expected states, identifying anomalies, and preparing failure classification data. This multi-functionality reduces the need for separate dedicated components, achieving real-time detection speed without proportionally increasing overall system complexity.
Data Source
AI summary
A system and method of detecting and responding to a failure of one or more vehicle components, the method including: receiving system input at a failure detection module regarding the one or more vehicle components; determining a system state through use of one or more onboard vehicle sensors; obtaining a nominal state transition matrix and a nominal state input matrix; calculating a present state transition matrix estimate and a present state input matrix estimate based on the nominal state transition matrix, the nominal state input matrix, the system input, and a sampled state derivative; detecting a failure of at least one of the vehicle components based on one or more component parameters of the present state transition matrix estimate and/or the present state input matrix estimate; and performing a vehicle action in response to the detection of the failure.