Brake Assembly Temperature Correlation for Steady-State Fault Detection
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Solution Overview
Problem
Existing brake system monitoring methods fail to reliably identify performance issues in brake assemblies due to insufficient correlation analysis and temperature variations, especially in steady-state conditions, leading to potential operational failures.
Innovation Solution
A method involving correlation coefficient analysis between brake assembly temperatures, sensor checks, and mean temperature comparisons to detect deviations, providing alert notifications when correlation values fall below a threshold, ensuring proper brake assembly operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If temperature correlation monitoring is used for brake assemblies, then the monitoring system can operate with simple temperature sensors, but the system fails to detect anomalies in steady-state conditions where temperature changes are low
Solution Approach 1:
The system dynamically adapts its monitoring strategy based on operating conditions. During steady-state conditions with low temperature changes, it switches to mean temperature comparison mode. During dynamic braking conditions, it uses correlation analysis. This dynamic adaptation resolves the contradiction by optimizing detection capability for each operational phase without requiring permanently complex hardware.
Solution Approach 2:
The system changes the monitoring parameter based on conditions: using temperature correlation during dynamic phases and mean temperature comparison during steady-state phases. This parameter switching enables reliable anomaly detection across all operating conditions while maintaining simple sensor requirements, as the same temperature sensors serve multiple monitoring functions.
2Ease of manufacture
If only temperature correlation is used to monitor brake assemblies, then the monitoring method is simple to implement, but it cannot reliably identify performance issues in steady-state conditions
Solution Approach 1:
The monitoring method dynamically switches between correlation-based monitoring and mean temperature comparison based on detected operating conditions. This dynamic approach maintains implementation simplicity while improving measurement precision across different operational phases, particularly enabling steady-state anomaly detection without complex additional sensors.
Solution Approach 2:
The monitoring approach is segmented into different strategies for different operational phases: correlation analysis for dynamic braking phases and mean temperature comparison for steady-state phases. This segmentation allows each method to be optimized for its specific phase, maintaining ease of implementation while achieving high detection accuracy across all conditions.
3Reliability
If mean temperature comparison is always conducted between brake assemblies, then steady-state anomalies can be detected, but the system complexity increases and computational requirements rise
Solution Approach 1:
The system dynamically activates mean temperature comparison only during steady-state conditions rather than continuously. This conditional activation achieves reliable steady-state anomaly detection while minimizing system complexity and computational burden during dynamic phases where correlation monitoring suffices.
Solution Approach 2:
The monitoring system periodically evaluates operating conditions and switches between monitoring strategies. Mean temperature comparison is periodically applied during steady-state intervals rather than continuously, reducing overall system complexity while maintaining detection reliability when needed.
4Reliability
If continuous monitoring of all brake assemblies is performed, then comprehensive brake performance can be ensured, but the computational load and processing time increase
Solution Approach 1:
The monitoring system dynamically adjusts its processing intensity based on operating conditions. During steady-state conditions, it performs comprehensive mean temperature comparisons. During dynamic braking, it relies on correlation monitoring. This dynamic adjustment ensures comprehensive monitoring reliability while minimizing processing time through conditional processing intensity.
Solution Approach 2:
The system applies partial monitoring (correlation only) during dynamic phases and comprehensive monitoring (mean temperature comparison) during steady-state phases. This partial action during appropriate phases reduces overall computational load and processing time while maintaining comprehensive reliability when most needed.
Data Source
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AI summary
A brake system and a method of monitoring and control. The method may include obtaining data indicative of temperature of a plurality of brake assemblies and determining at least one correlation value based on the temperature data. A mean temperature comparison may be conducted when a correlation value is less than a threshold correlation value.