Vehicle Fluidic Subsystem Fault Isolation via Controller Analysis
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Solution Overview
Problem
Fault isolation in vehicle fluidic subsystems, particularly in electric motors and fluidic pumps, is challenging due to the lack of relevant parameters and parametric analysis, affecting the ability to deliver pressurized fluid at desired pressures and flow rates.
Innovation Solution
A controller monitors and dynamically observes operating parameters of the fluidic subsystem, determining observed and estimated parameters to calculate fault isolation parameters, such as DC-equivalent resistance, speed ratios, and flow ratios, to isolate faults in the electric motor, fluidic pump, and pressure sensor, and communicates these to a malfunction indicator lamp or off-board controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring systems are used in fluidic subsystems, then the system structure remains simple, but fault isolation capability is insufficient due to lack of relevant parameters and parametric analysis
Solution Approach 1:
The monitoring system is segmented into multiple functional modules: a parameter determination module that calculates observed parameters from sensor data, an estimation module that predicts expected parameters based on system models, and a comparison module that generates fault isolation parameters by comparing observed versus estimated values. This segmentation allows comprehensive fault isolation capability while maintaining manageable system complexity through modular design.
2Measurement precision
If multiple observed and estimated parameters are calculated to improve fault isolation, then fault detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system transforms raw sensor measurements into multiple derived parameters including DC-equivalent resistance, speed ratios, current ratios, PWM ratios, and flow ratios. Each parameter transformation extracts specific diagnostic information from the sensor data, enabling accurate fault detection through comprehensive parametric analysis while managing computational complexity through systematic parameter derivation.
3Loss of time
If real-time dynamic observation of multiple parameters is implemented, then fault isolation speed improves, but energy consumption and processing load increase
Solution Approach 1:
The controller is pre-configured with system models and parameter relationships that enable rapid calculation of observed and estimated parameters when faults occur. By having the computational framework prepared in advance, the system can perform real-time fault isolation quickly without requiring complex on-the-fly computations, thus reducing both processing time and energy consumption during actual fault events.
Data Source
AI summary
A fluidic subsystem disposed on a vehicle includes an electric motor, a motor driver, and a fluidic pump that is disposed in a fluidic circuit that is monitored by a pressure sensor. A controller includes an instruction set that is executable to dynamically observe operation of the fluidic subsystem, from which it determines a plurality of observed parameters associated with the operation of the fluidic subsystem and a plurality of estimated parameters associated with the fluidic subsystem. A plurality of fault isolation parameters are determined based upon the observed parameters and the estimated parameters, and a fault in the fluidic subsystem is isolated based upon the fault isolation parameters. The isolated fault is communicated via the controller.