Flow Control Diagnostics via Signal Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mass flow control systems face operational impairments such as flow obstructions, corrosion, and electromechanical deterioration, which are difficult to diagnose directly, leading to inefficient maintenance practices that rely on indirect indicators like service hours and valve cycles, failing to account for variable operating conditions.
Innovation Solution
A flow control system comprising a flow sensor, valve controller, signal processor, control processor, and interface that generates diagnostic signals directly indicative of operational conditions through statistical analysis and control algorithms, providing real-time indicators of flow obstructions, corrosion, and electromechanical deterioration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If maintenance is based on service hours and valve cycles, then maintenance scheduling is simplified, but maintenance efficiency deteriorates due to inability to detect actual operational conditions
Solution Approach 1:
The patent replaces mechanical/direct measurement methods with signal processing and statistical analysis. The system processes sensor signals through spectral analysis and statistical computations to generate diagnostic indicators, substituting physical inspection methods with electronic signal processing to detect operational conditions.
Solution Approach 2:
The patent introduces diagnostic indicators as intermediary variables between the physical operational conditions and the maintenance decision-making process. These indicators serve as mediators that translate complex sensor data into actionable maintenance information, enabling efficient scheduling without direct system intervention.
2Device complexity
If indirect indicators like service hours are used, then diagnostic simplicity is improved, but measurement precision deteriorates due to lack of direct operational condition data
Solution Approach 1:
The patent replaces direct physical measurement with signal processing techniques. Instead of mechanically measuring wear or obstruction, the system uses spectral analysis and statistical processing of sensor signals to infer operational conditions, achieving precise measurement without complex mechanical diagnostic tools.
Solution Approach 2:
The patent transforms physical operational parameters into statistical parameters through signal processing. By converting sensor signals into spectral components and statistical indicators, the system changes the parameter representation from direct physical measurements to processed statistical data, enabling precise condition assessment.
3Reliability
If regular component replacement is implemented, then system reliability is improved, but resource utilization deteriorates due to replacement of fully serviceable components
Solution Approach 1:
The patent enables the system to self-diagnose its operational conditions through continuous signal monitoring and analysis. By allowing the system to assess its own state and provide diagnostic indicators, maintenance can be performed only when actually needed, preventing unnecessary replacement of serviceable components while maintaining reliability.
Solution Approach 2:
The patent implements continuous feedback through diagnostic indicators that provide real-time information about actual operational conditions. This feedback loop enables dynamic maintenance decision-making, allowing components to remain in service until diagnostic indicators indicate actual deterioration, thereby optimizing resource utilization while maintaining system 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
Enables efficient, cost-effective maintenance by providing direct indicators of operational conditions, reducing unnecessary downtime and resource utilization, and facilitating individualized maintenance without requiring substantial modifications to existing systems.
Implementation Method 1
The thermal mass flow meter comprises a heat source and (usually two) temperature sensors, such as thermocouples or resistance-temperature devices (RTDs), arranged along a sensor tube. The heat source imparts thermal energy to the fluid in the sensor tube, creating a differential signal across the temperature sensors.
Implementation Method 2
The thermal mass flow meter comprises a heat source and (usually two) temperature sensors, such as thermocouples or resistance-temperature devices (RTDs), arranged along a sensor tube.
Implementation Method 3
The thermal mass flow meter comprises a heat source and (usually two) temperature sensors, such as thermocouples or resistance-temperature devices (RTDs), arranged along a sensor tube.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A flow control system (10, 20) comprises a flow sensor (12), a valve controller (13), a signal processor (14, 16), a control processor (14, 16) and an interface (14, 17). The flow sensor (12) generates a sensor signal characterizing a flow rate. The valve controller (13) controls the flow rate as a function of a control output. The signal processor (14, 16) converts the sensor signal into a flow signal characterizing the flow rate as a function of time, and the control processor (14, 16) generates the control output as a function of a setpoint and the flow signals. The interface (14, 17) receives an input representative of the setpoint, transmits a flow output representative of the flow signals, and transmits a diagnostic output directly indicative of an operational condition of the flow control system (10, 20).