Sound Spectrogram Diagnosis for Portable Equipment Fault Detection
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Solution Overview
Problem
Existing equipment failure diagnosis methods are inefficient due to the mobility issues and operational challenges of heavy examination equipment.
Innovation Solution
An apparatus and method that utilize a sensor unit to collect sound data from equipment, convert it into a spectrogram image, and perform machine learning to diagnose equipment failures and classify their causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heavy examination equipment is disposed at a site for failure diagnosis, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent replaces heavy mechanical examination equipment with a portable terminal device equipped with a microphone and processor. The terminal collects sound data through the microphone, converts it to spectrogram images, and performs AI-based analysis to diagnose equipment failures, thereby eliminating the need for cumbersome mechanical diagnostic devices while maintaining or improving diagnostic accuracy.
Solution Approach 2:
The patent creates a digital copy of the sound spectrum through spectrogram images, which represent the acoustic characteristics of equipment operation. This digital representation allows AI algorithms to analyze equipment status without requiring physical contact or heavy measurement instruments, simplifying the diagnostic process while preserving measurement precision.
2Measurement precision
If heavy examination equipment is disposed at a site for failure diagnosis, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces complex mechanical examination equipment with a portable terminal device that operates through simple sound collection and AI processing. The terminal automatically performs sound data collection, spectrogram generation, and failure diagnosis without requiring manual operation of complex mechanical components, significantly improving ease of operation while maintaining diagnostic precision.
Solution Approach 2:
The terminal device autonomously performs the entire diagnosis process: collecting sound data through its microphone, converting the data into spectrogram images, analyzing the images using AI algorithms, and generating diagnosis results. This self-service capability eliminates the need for operators to manually operate complex examination equipment, making the process extremely convenient.
3Reliability
If traditional examination equipment is used for failure diagnosis, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces traditional mechanical examination equipment with an AI-based terminal system that processes sound data and spectrogram images computationally. This substitution enables rapid automated analysis of equipment status, significantly improving diagnostic productivity while maintaining reliability through sophisticated AI algorithms that analyze acoustic patterns indicative of equipment failures.
Solution Approach 2:
The terminal device continuously collects and pre-processes sound data in the form of spectrogram images, preparing the data for rapid AI analysis. This preliminary action allows the system to immediately diagnose equipment failures when they occur, improving both productivity and response time compared to traditional methods that require manual setup and analysis.
Data Source
AI summary
Disclosed are an equipment malfunction diagnosis apparatus using a sound spectrogram image, and a method therefor. In other words, the present invention collects sound data through a sensor unit provided at one side of equipment, converts the collected sound data into a spectrogram image, and determines malfunction of the equipment and classifies a cause of the malfunction by performing machine learning using the spectrogram image as an input value, thereby quickly and accurately identifying the cause of the malfunction when it is identified that there is the malfunction in the equipment, and providing a counterplan in response thereto.