Fluid Controller Dynamic Threshold Scaling
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Solution Overview
Problem
Existing fluid controllers in steering systems are overly sensitive to minor errors, leading to unjustified alarms and operational interruptions, while being insufficiently sensitive to significant faults, due to fixed threshold values that do not account for the speed of reference changes.
Innovation Solution
The fluid controller adjusts the threshold value based on the gradient of the reference signal, scaling it proportionally to the speed of changes, thereby reducing false alarms and maintaining sensitivity to critical errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed threshold value is used for fault detection, then the system is sensitive to minor errors, but it produces unjustified alarms and operational interruptions
Solution Approach 1:
The threshold value is transformed from a fixed parameter to a dynamic parameter that adapts to changing operating conditions. The threshold is scaled proportionally to the gradient of the reference signal, allowing it to vary with the speed of reference changes. This resolves the contradiction by making the system sensitive to minor errors during slow changes while avoiding false alarms during rapid reference changes.
Solution Approach 2:
The threshold parameter is changed from a constant value to a variable value that depends on the reference gradient. By scaling the threshold with the gradient magnitude, the system adjusts its detection sensitivity dynamically. This allows the same detection mechanism to work correctly across different operating conditions without producing false alarms.
2Reliability
If a fixed threshold value is used for fault detection, then the system avoids false alarms during rapid changes, but it becomes insufficiently sensitive to significant faults
Solution Approach 1:
The dynamic threshold scaling ensures that during rapid reference changes, the threshold increases proportionally, preventing false alarms. During slow reference changes, the threshold remains low, maintaining high sensitivity to significant faults. This dynamic adaptation resolves the contradiction between avoiding false alarms and maintaining sensitivity.
Solution Approach 2:
By changing the threshold parameter from fixed to variable (scaled by gradient), the system achieves both goals: high reliability during rapid changes and high sensitivity during slow changes. The parameter change allows the detection system to adapt its behavior to the current operating condition.
3Reliability
If the threshold value is scaled based on reference gradient, then false alarms are reduced, but the system complexity increases
Solution Approach 1:
The threshold scaling mechanism uses feedback from the reference gradient to automatically adjust the threshold value. The gradient information feeds back into the threshold calculation, creating a self-adjusting system that reduces false alarms without requiring complex external control mechanisms. This feedback approach resolves the contradiction by using simple proportional scaling rather than complex adaptive algorithms.
Data Source
AI summary
The invention provides a fluid controller for controlling a machine, e.g. for steering a vehicle. The controller comprises a housing defining an inlet port connected to the source and an outlet port connected to the pressure operated device. The flow rate is controlled by movement of a valve member within the housing, and a processor provides a reference which is indicative of a desired position of the valve member relative to the housing. The fluid controller comprises a fault detection system based on an observer. The observer calculates a theoretically correct position of the valve member relative to the housing for a given reference, and compares this position to an obtained position of the valve member relative to the housing. The difference between the positions is compared with a threshold value. In order to dynamically change the sensitivity of the system, the threshold value is scaled based on a gradient of the reference.