Fluid Leakage Diagnosing Device Using Adaptive Signal Processing
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Solution Overview
Problem
Existing methods for diagnosing fluid leakage in pipelines face challenges in accurately detecting leakage amidst disturbance vibrations, requiring efficient suppression of disturbance components to enhance diagnostic accuracy.
Innovation Solution
A fluid leakage diagnosing device employing adaptive signal processing with a least mean square (LMS) algorithm, which includes an acquiring unit for distribution information, an estimating unit for probability density function, and a determining unit to optimize parameter settings for the LMS algorithm, such as filter order and step size, to effectively suppress disturbance vibrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If adaptive signal processing is used to suppress disturbance vibrations, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by optimizing the filter order and step size parameters in the LMS algorithm. The determining unit selects appropriate parameter values based on the characteristics of the pipeline and disturbance sources, allowing the adaptive signal processing to achieve high diagnostic accuracy without requiring excessively complex system configurations.
Solution Approach 2:
The patent implements feedback through the adaptive nature of the LMS algorithm, where the filter coefficients are continuously adjusted based on the error between the actual and desired signals. This feedback mechanism enables the system to automatically adapt to changing disturbance conditions, improving diagnostic accuracy while maintaining a relatively simple device structure.
2Reliability
If multiple vibration sensors are installed to detect leakage, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The patent merges the functions of multiple vibration sensors into a unified adaptive signal processing framework. By combining the signals from multiple sensors and applying the LMS algorithm with optimized parameters, the system achieves high detection reliability while avoiding the complexity of independently processing each sensor signal through separate complex systems.
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
The device enhances diagnostic accuracy by efficiently setting parameter values in adaptive signal processing, leading to improved disturbance suppression performance and precise leakage detection.
Implementation Method 1
employing adaptive signal processing with a least mean square (LMS) algorithm, which includes an acquiring unit for distribution information, an estimating unit for probability density function, and a determining unit to optimize parameter settings for the LMS algorithm
Implementation Method 2
utilizing a vibration propagating through the pipelines. In this method, using a vibration that has been produced by fluid leakage from a pipeline and is propagating through the pipeline, the fluid leakage is detected by finding the vibration that has propagated to a prescribed location with a vibration sensor
Data Source
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AI summary
A fluid leakage diagnosing device 40 is provided with: when fluid leakage relating to a pipeline 50 is being diagnosed by employing adaptive signal processing to suppress a component 52 of a disturbance vibration contained in a vibration 51 measured at a prescribed location in the pipeline 50, an acquiring unit 41 that acquires distribution information 410 that is associated with a characteristic of the pipeline 50 and represents an actual distribution result of a value of a parameter in the adaptive signal processing when performance of suppressing the component 52 of the disturbance vibration satisfies a criterion; an estimating unit 42 that, based on the distribution information 410, estimates a probability density 420 at which the value of the parameter with which the performance satisfies the criterion exists; and a determining unit 43 that, based on the probability density 420 estimated by the estimating unit 42, determines a range 430 to search for the value of the parameter in the adaptive signal processing, thereby efficiently enhancing diagnostic accuracy when fluid leakage in a pipeline is being diagnosed by employing adaptive signal processing to suppress a component of a disturbance vibration contained in a vibration measured in the pipeline.