Abnormal Sound Localization for Maintenance-Aware Equipment Diagnosis
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Solution Overview
Problem
Existing abnormal-sound detection techniques often result in incorrect detection of non-abnormal sounds, particularly during maintenance activities, due to threshold-based methods and camera direction alignment issues.
Innovation Solution
An abnormal-sound detection device comprising imaging, operation range identification, sound collection, abnormal-sound sensing, and derivation determination units, which captures video, identifies operation ranges, collects sounds, senses abnormalities, and determines if the abnormal sound originates from the diagnosis object by comparing the operation range with the sound source position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If threshold-based abnormality sensing is used, then abnormal sounds can be detected, but incorrect detection occurs when non-abnormal maintenance work exceeds the threshold
Solution Approach 1:
The patent transitions from one-dimensional threshold-based sound level detection to multi-dimensional detection by integrating spatial information from microphone arrays and visual information from cameras. This allows the system to distinguish between abnormal sounds and maintenance activities by analyzing the spatial and visual context, not just sound intensity.
Solution Approach 2:
The patent introduces an operation range identification unit that acts as an intermediary between sound detection and abnormality determination. This unit defines the spatial and operational context within which sounds should be evaluated, serving as a mediator that filters out sounds from authorized maintenance activities while preserving detection of actual abnormalities.
2Measurement precision
If camera direction is aligned with sound source direction, then sound source can be located, but incorrect monitoring occurs when maintenance work is the sound source
Solution Approach 1:
The patent merges audio signal processing from microphone arrays with video data from cameras to create a unified detection system. By combining these modalities, the system can cross-validate information and distinguish between abnormal sounds requiring attention and normal maintenance activities, improving both localization accuracy and monitoring reliability.
Solution Approach 2:
The operation range identification unit serves as an intermediary that processes both audio and visual data to determine whether a detected sound source represents an actual abnormality or authorized maintenance work. This mediator layer prevents incorrect monitoring by contextualizing the detected sound within operational parameters.
3Device complexity
If simple threshold detection is used, then detection process is simple, but cannot differentiate between abnormal sounds and external sounds
Solution Approach 1:
The patent segments the detection process into distinct functional units: operation range identification, sound collection, abnormality sensing, position identification, and derivation determination. This segmentation allows the system to systematically analyze multiple parameters (spatial, temporal, spectral) without overwhelming complexity, achieving high differentiation accuracy through structured processing.
Solution Approach 2:
The system moves beyond simple amplitude thresholding by incorporating spatial dimensions through microphone array processing and camera positioning, as well as temporal and spectral dimensions. This multi-dimensional approach enables accurate differentiation between abnormal and external sounds while maintaining manageable system complexity through modular architecture.
Data Source
AI summary
An abnormal-sound detection device has an imaging unit, an operation range identification unit, a sound collection unit, an abnormal-sound detection unit, an abnormal-sound generation position identification unit, and an abnormal-sound source determination unit. The operation range identification unit identifies and stores the operation range of a diagnosis object on the basis of the image captured by an imaging unit. The abnormal-sound detection unit detects abnormalities in sounds included in the sounds collected by the sound collection unit, the sounds arriving from the diagnosis object. When an abnormality in a sound is detected by the abnormal-sound detection unit, the abnormal-sound generation position identification unit identifies the position at which the abnormality of the sound was generated. The abnormal-sound source determination unit compares the operation range and the abnormal-sound generation position of the diagnosis object, and determines whether the abnormality of the sound is derived from an abnormality of the diagnosis object.