Gripper Audio Sensor Verification for Noisy Connector Mating
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Solution Overview
Problem
Conventional connector mating assurance systems, such as those using audio gloves with microphones, struggle to reliably filter out background noise and maintain signal fidelity, leading to inconsistent performance in real-world scenarios due to variable technician grip and environmental noise.
Innovation Solution
A device with a gripper-mounted plurality of audio sensors and a neural network using independent component analysis, akin to the 'cocktail party effect,' isolates connection sounds from background noise, maintaining signal fidelity and adapting to sensor array geometry changes, with AI-based signal classification for effective connection verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional filtering techniques (beam-forming, noise subtraction) are used to filter background noise from connection sounds, then some noise reduction is achieved, but signal fidelity is significantly degraded and reliability remains inconsistent
Solution Approach 1:
The system performs preliminary training in a controlled environment to build a neural network model that learns the characteristics of connection sounds before actual use. This preliminary action allows the system to have prior knowledge of what connection sounds look like, enabling it to reliably identify and isolate these sounds from background noise during actual connector mating operations
Solution Approach 2:
The patent replaces conventional digital filtering methods (beam-forming, noise subtraction) with a neural network-based independent component analysis approach. This substitution transitions from traditional signal processing mechanics to AI-based pattern recognition, allowing the system to isolate connection sounds without significantly degrading signal fidelity or requiring fixed microphone geometries
2Object-affected harmful factors
If beam-forming directional filtering is used to filter connection sounds, then some noise reduction is achieved, but the system becomes ineffective when relative positions of microphones and connectors are not fixed
Solution Approach 1:
The system transitions from static, geometry-dependent filtering to a dynamic, learning-based approach. The neural network is trained to recognize connection sounds regardless of the specific spatial configuration, allowing the system to adapt to varying grip positions and technician differences without requiring fixed microphone-connector geometries
Solution Approach 2:
The patent changes the fundamental parameter for noise filtering from spatial geometry (fixed microphone positions and orientations) to acoustic pattern recognition (learning the temporal and spectral characteristics of connection sounds). This parameter change enables the system to maintain effectiveness across variable positions and conditions
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 solution ensures accurate and reliable connection verification in noisy environments by maintaining signal fidelity and adaptability, enabling effective classification of connection sounds, even in high-noise conditions, thus improving the reliability of connector mating processes.
Implementation Method 1
a neural network for isolating the connection sound from the audio signals received from the plurality of audio sensors using independent component analysis based on training audio data
Data Source
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AI summary
A device (1) for verifying the connection of components (10,11) by a gripper (8), wherein connecting two or more components (10,11) produces a connection sound. The device (1) comprises a plurality of audio sensors (3,4), a fastener (2) for securing the plurality of audio sensors (3,4) at different positions on the gripper (8), and a controller (6). The controller (6) comprises an input for receiving the audio signals from the plurality of audio sensors (3,4), a neural network for isolating the connection sound from the audio signals received from the plurality of audio sensors (3,4) using independent component analysis based on training audio data obtained from audio signals received during a plurality of training connections made in a controlled environment; and an output (7) for indicating a desired connection status based on the isolated connection sound.