Mobile Wiring Harness Inspection Using ML Anomaly Detection
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Solution Overview
Problem
Inspecting complex wiring harnesses in large aerospace vehicles is difficult due to their extensive length and complexity, necessitating more efficient and accurate anomaly detection methods.
Innovation Solution
A mobile personal computing device equipped with a camera system and a machine learning model trained to detect anomalies in wiring harness images, utilizing a controller to generate and analyze images for anomaly detection, with specialized training datasets and feedback loops for increased accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual inspection methods are used for wiring harnesses in aerospace vehicles, then inspectors can visually examine the wiring, but the inspection process becomes time-consuming and prone to human error due to the extensive length and complexity of the wiring harnesses
Solution Approach 1:
The patent replaces manual visual inspection with an automated machine learning-based image analysis system. A camera captures images of the wiring harness, and a trained machine learning model automatically detects anomalies in these images, eliminating the need for time-consuming manual examination while improving detection accuracy and consistency
Solution Approach 2:
The system creates visual copies (images) of the wiring harness using a camera, allowing the machine learning model to analyze multiple images of the wiring harness. This enables parallel processing of multiple sections simultaneously, reducing overall inspection time while maintaining high detection precision through automated image analysis
2Reliability
If comprehensive inspection of complex wiring harnesses is performed to ensure high reliability, then anomaly detection accuracy improves, but the complexity of the inspection system and process increases
Solution Approach 1:
The patent replaces complex manual inspection procedures with a streamlined automated system consisting of a camera and machine learning model. This substitution maintains high reliability through consistent automated analysis while reducing operational complexity by eliminating the need for trained inspectors to manually examine complex wiring configurations
Solution Approach 2:
The machine learning model performs self-service by automatically analyzing wiring harness images and detecting anomalies without requiring human intervention. The system trains on specialized datasets and uses feedback loops to continuously improve its detection capabilities, maintaining high reliability while keeping the inspection process simple and automated
3Measurement precision
If detailed anomaly detection is performed on wiring harnesses to improve maintenance quality, then detection precision increases, but the computational resources and processing time required increase
Solution Approach 1:
The machine learning model is pre-trained on specialized datasets containing wiring harness images and anomaly examples before deployment. This preliminary training enables the model to perform detailed anomaly detection with high precision during actual inspections without requiring excessive computational resources during the inspection process itself
Solution Approach 2:
The system uses image copying to capture multiple views of the wiring harness, allowing the machine learning model to analyze detailed features across multiple images. This approach distributes the computational workload across multiple smaller image processing tasks rather than requiring intensive processing of a single complex scene, optimizing energy efficiency while maintaining detection precision
Data Source
AI summary
An aerospace wiring harness inspection system comprises a mobile personal computing device; a camera system connected to the mobile personal computing device, a machine learning model running in the mobile personal computing device; and a controller. The machine learning model is trained to detect anomalies in a set of images of a wiring harness in an aerospace vehicle. The controller configured to control the camera system to generate a set of images of the wiring harness; send the set of images of the wiring harness to the machine learning model; and receive a result from the machine learning model indicating whether an anomaly is present in the wiring harness.