Nuclear Plant Acoustic Diagnostics for Harsh-Environment Monitoring
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Solution Overview
Problem
Traditional sensors are ineffective in monitoring process attributes in nuclear power plants due to high radiation and temperature environments, limiting operators' visibility into reactor processes and component health.
Innovation Solution
Implementing acoustic sensors to generate acoustic data signals that are processed by a computing device to determine process attributes through frequency analysis, allowing for remote monitoring of nuclear power plant operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensors are used to monitor process attributes in nuclear power plants, then measurement capability is provided, but the sensors cannot operate reliably in high radiation and temperature environments
Solution Approach 1:
The patent replaces traditional physical sensors that directly contact the harsh environment with acoustic sensing technology. Acoustic sensors detect process attributes by listening to acoustic emissions from equipment, allowing remote monitoring without physical exposure to radiation and extreme temperatures, thus resolving the reliability issue while maintaining measurement capability
Solution Approach 2:
The patent introduces acoustic waves as an intermediary medium to transfer information from the harsh environment to sensors located in safe zones. Acoustic emissions carry diagnostic information about equipment state without requiring sensors to be physically present in high radiation and temperature areas, enabling reliable monitoring while protecting sensor integrity
2Loss of information
If acoustic sensors and frequency analysis are implemented to monitor process attributes, then operator visibility into reactor processes is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service through automated frequency analysis and pattern recognition algorithms that automatically process acoustic signals and identify equipment states. The system performs self-diagnosis by analyzing acoustic emission patterns and automatically determining process attributes without requiring manual intervention or complex operator interpretation, thus improving visibility while managing system complexity through automation
Solution Approach 2:
The patent transforms acoustic signals from the time domain to the frequency domain through Fourier transformation, changing the parameter representation to make process attributes more discernible. This parameter transformation enables automatic identification of equipment states by analyzing frequency spectra characteristics, improving operator visibility while using standardized signal processing techniques to manage complexity
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 safer and cost-effective monitoring of process attributes, including temperature, pressure, and component health, improving operational control and maintenance scheduling.
Implementation Method 1
at least one acoustic sensor configured to generate at least one time-dependent acoustic data signal indicative of an acoustic signal generated by a nuclear power plant
Implementation Method 2
transform the at least one time-dependent acoustic data signal to a frequency-domain spectrum, wherein each process attribute of the plurality of process attributes is associated with at least one respective frequency band
Data Source
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AI summary
An example system includes at least one acoustic sensor configured to generate at least one time-dependent acoustic data signal indicative of an acoustic signal generated by an nuclear power plant performing a process possessing a plurality of process attributes, and a computing device including an acoustic data signal processing module configured to receive the at least one time-dependent acoustic data signal, and transform the at least one time-dependent acoustic data signal to a frequency-domain spectrum, wherein each process attribute of the plurality of process attributes is associated with at least one respective frequency band, and a correlation module configured to determine a process attribute of the plurality of process attributes by identifying at least one characteristic of the frequency-domain spectrum.