Fluid Filter Abnormality Detection via Kalman Filter
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Solution Overview
Problem
Conventional methods for determining when to replace a clogged fluid filter in vehicles are inaccurate due to instability in fluid flow rates, leading to increased maintenance costs and safety risks, as they rely on pressure differences that are unstable during dynamic vehicle movements.
Innovation Solution
A method and system that detect the flow rate and pressure difference in fluid filters, construct an operating model based on geometry, physical characteristics, porosity, and impurity density, and use a Kalman filter to estimate the time-dependent impurity accumulative status, allowing for accurate determination of when the filter needs replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pressure difference is used to determine filter clogging, then the detection method is simple, but the measurement precision deteriorates due to flow rate instability
Solution Approach 1:
The system continuously monitors both flow rate and pressure difference, using feedback loops to update the impurity accumulative quantity estimation in real-time. The Kalman filter processes incoming measurements and adjusts estimates based on the difference between predicted and actual values, improving accuracy despite flow rate variations
Solution Approach 2:
The system transitions from using only pressure difference as a parameter to using both flow rate and pressure difference simultaneously. By changing the parameter set and using an operating model that incorporates multiple variables, the system achieves more accurate clogging estimation while accounting for dynamic flow conditions
2Reliability
If filter replacement is performed periodically, then the filter operates reliably, but the loss of time and productivity increase due to premature replacement
Solution Approach 1:
The system performs preliminary estimation of impurity accumulative quantity and predicts remaining filter life before actual clogging occurs. By providing advance warning and scheduling maintenance based on predicted failure time rather than fixed intervals, the system avoids both premature and delayed replacement
Solution Approach 2:
The filter condition monitoring system enables the filtration system to self-diagnose its own status by continuously estimating impurity accumulation. This self-monitoring capability allows the system to determine its own maintenance needs without external intervention or fixed schedules
3Productivity
If filter replacement is delayed, then productivity is maintained, but the harmful factors increase due to filter failure
Solution Approach 1:
The system takes preliminary action by predicting filter failure time based on current impurity accumulation rates and historical data. This allows maintenance to be scheduled just before predicted failure, maximizing productivity while preventing harmful filter breakdown
Solution Approach 2:
The continuous monitoring and estimation system provides feedback on filter health status, allowing dynamic adjustment of maintenance scheduling. The system can extend operation between maintenance intervals when filter condition deteriorates slowly, or trigger earlier maintenance when degradation accelerates, optimizing the balance between productivity and reliability
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a high-accuracy method for determining if a fluid filter operates normally by estimating impurity accumulative quantities, reducing the risk of premature or delayed filter replacement and ensuring efficient and safe vehicle operation.
Implementation Method 1
estimating a time dependent impurity accumulative status through a Kalman filter in accordance with the initial impurity accumulative quantity and the pressure difference
Implementation Method 2
the impurities in the fluid filter are filtered through a filter cartridge
Data Source
AI summary
A method for detecting an abnormality of a fluid filter includes: detecting a flow rate of a fluid in the fluid filter; detecting a pressure difference in the fluid filter; constructing an operating model of the fluid filter in accordance with a geometry of the fluid filter, a physical characteristic of the fluid, a porosity of the fluid filter, an impurity density, the flow rate and the pressure difference; obtaining an initial impurity accumulative quantity through the operating model; estimating a time dependent impurity accumulative status through a Kalman filter in accordance with the initial impurity accumulative quantity and the pressure difference; obtaining an impurity accumulative quantity in an estimated time in accordance with the time dependent impurity accumulative status, and then comparing the impurity accumulative quantity with a pre-determined value to determine if the fluid filter operates normally.