Vibronic Sensor State Monitoring via Neural Network
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Solution Overview
Problem
Vibronic sensors face challenges in accurately monitoring their state due to complex interference from external and internal influences, making it difficult to differentiate between regular and irregular variables, which affects measurement accuracy and reliability.
Innovation Solution
A computer-implemented method using a neural network to analyze the spectrum of a vibronic sensor, allowing for comprehensive state monitoring by distinguishing between various influencing factors without prior knowledge of interfering influences, and enabling continuous operation without removing the sensor from the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectrum analysis methods are used to monitor sensor state, then the analysis process becomes complex and difficult to interpret, but removing the sensor from the process to perform detailed analysis causes interruption of continuous monitoring
Solution Approach 1:
A neural network is introduced as an intermediary between the raw sensor spectrum and the state monitoring decision. The neural network processes the complex spectral data and outputs simplified state information, enabling accurate monitoring without requiring complex manual analysis or sensor removal from the process
Solution Approach 2:
Traditional manual or rule-based spectrum analysis methods are replaced with a neural network-based system. This substitution transforms the analysis process from a complex mechanical/algorithmic procedure into an intelligent system that automatically interprets spectral patterns and provides state monitoring results
2Reliability
If the sensor remains in the process for continuous monitoring, then uninterrupted measurement is achieved, but external and internal interferences make it difficult to differentiate between regular and irregular variables
Solution Approach 1:
The neural network is trained using feedback from labeled spectral data, learning to distinguish between regular and irregular variations in the sensor signal. This feedback mechanism enables the system to differentiate between normal process variations and actual sensor state changes, maintaining reliability during continuous operation
Solution Approach 2:
The system analyzes changes in spectral parameters (frequency, amplitude, phase) to identify sensor state transitions. By monitoring parameter variations rather than absolute values, the system can differentiate between regular process variations and irregular sensor conditions while maintaining continuous operation
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 method simplifies the interpretation of sensor spectra, enabling reliable detection of complex patterns and differentiation between regular and irregular influences, thus providing comprehensive and independent state monitoring of the sensor.
Implementation Method 1
the drive/reception unit is either a separate drive unit and a separate reception unit, or a combined drive/reception unit... the drive/reception unit is part of an electrical resonant feedback circuit by means of which the excitation of the mechanically vibratable unit to produce mechanical vibrations takes place
Implementation Method 2
the resonant circuit condition according to which the amplification factor is ≥1 and all phases occurring in the resonant circuit result in a multiple of 360° must be fulfilled for a resonant vibration
Implementation Method 3
the viscosity of a medium can be determined by means of a vibronic sensor on the basis of the frequency-phase curve (φ=g(ω)). This procedure is based on the dependence of the damping of the vibratable unit on the viscosity of the respective medium
Data Source
AI summary
A computer-implemented method for monitoring the state of a vibronic sensor comprising a mechanically vibratable unit and a drive/reception unit that is designed to excite the mechanically vibratable unit to vibrate mechanically, and to receive the mechanical vibrations of the mechanically vibratable unit and to convert them into a reception signal includes the following method steps: recording a spectrum of the vibronic sensor as input data, providing the input data to a neural network designed to determine a statement about the state of the vibronic sensor on the basis of the input data, and outputting the statement about the state of the vibronic sensor.