Atomization Acoustic Diagnostics for Nozzle Wear and Blockages
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Solution Overview
Problem
Atomization systems face challenges in maintaining process stability and product quality due to variations in process attributes such as nozzle wear, material composition, and fluid flow rates, leading to deviations from nominal specifications and potential nozzle blockages, which can result in defective particulates.
Innovation Solution
An atomization system equipped with acoustic sensors generates time-dependent acoustic data signals that are analyzed by a computing device to transform into frequency-domain spectra, allowing for real-time monitoring and control of process attributes by identifying characteristics in these spectra to adjust or maintain process parameters within acceptable ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If acoustic sensors and signal processing are added to monitor process attributes, then manufacturing precision and process stability are improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical measurement systems with acoustic sensing. Acoustic sensors detect process attributes (nozzle wear, blockages, material flow) through sound wave analysis, transforming physical process monitoring into acoustic signal processing. This substitution maintains manufacturing precision while reducing mechanical complexity.
Solution Approach 2:
The patent introduces acoustic signals as an intermediary between the atomization process and measurement systems. Instead of directly measuring physical parameters, the system uses acoustic emissions as a mediator to indirectly detect process attributes, enabling non-intrusive monitoring that simplifies system integration.
2Reliability
If real-time acoustic monitoring is implemented, then reliability is improved by preventing nozzle blockages and defects, but use of energy increases due to continuous signal processing
Solution Approach 1:
The atomization system performs self-diagnosis through acoustic monitoring. The system automatically detects its own operational state (nozzle wear, blockages, material flow issues) and can trigger alerts or adjustments without external intervention, improving reliability while minimizing the need for additional monitoring infrastructure.
Solution Approach 2:
The patent enables rapid detection and response to process anomalies by analyzing acoustic spectra in real-time. When deviations are detected (indicating potential blockages or defects), the system can immediately take corrective action, skipping the traditional lengthy inspection and diagnosis process, thereby improving reliability efficiently.
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 real-time or near-real-time control of atomization processes, reducing defects by ensuring process attributes remain within specifications, thereby improving product quality and preventing unscheduled downtime.
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 an atomization system
Data Source
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 atomization system 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.