Autonomous 3D Asset Inspection With Edge-Based Anomaly Detection
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Solution Overview
Problem
Conventional in-person inspections of manufactured articles and facilities are time-consuming, prone to human error, and costly, with limited digital data capture and storage capabilities, making it difficult to efficiently compare and analyze data across different inspections and times.
Innovation Solution
An autonomous sensor apparatus traverses a site to collect data from multiple sensors, processes it in real-time using edge computing to reduce data size, and autonomously identifies anomalies by comparing with pre-existing models, communicating criticality and instructions for human operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-person inspections are performed manually, then detailed observations can be made using human senses and specialized equipment, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual inspection processes with an autonomous sensor apparatus that uses optical sensors, LIDAR, and other detection devices to automatically capture and analyze data about objects, eliminating the need for human inspectors to physically traverse sites and perform manual measurements
Solution Approach 2:
The system performs self-inspection by autonomously navigating through the site, automatically capturing data from multiple sensors, processing the data through machine learning algorithms, and generating inspection reports without human intervention, allowing the inspection process to serve itself
2Measurement precision
If comprehensive data is captured from multiple sensors, then inspection accuracy improves, but data transmission and processing costs increase
Solution Approach 1:
The system extracts only the essential and relevant features from the comprehensive sensor data using machine learning algorithms, separating critical inspection information from redundant data, and transmits only the extracted key findings rather than the complete raw data set
Solution Approach 2:
The data processing is segmented into multiple stages: initial filtering at the sensor level, feature extraction through machine learning models, and final analysis at the processing server, allowing comprehensive data to be handled efficiently in distributed steps rather than transmitted as a single large data set
3Reliability
If repeated inspections are performed at different times, then changes and anomalies can be detected, but data organization and comparison become increasingly complex
Solution Approach 1:
The system creates standardized digital copies of inspection data in a uniform format with consistent metadata structures, allowing historical inspection data to be replicated and compared across different time periods without the complexity of organizing disparate data formats from manual inspections
Solution Approach 2:
The system implements automated feedback loops where inspection results from previous visits are fed into the machine learning models for subsequent inspections, enabling automatic comparison and highlighting of changes over time, with the system learning from historical data to improve anomaly detection
Data Source
AI summary
A method for performing an autonomous inspection comprises traversing, by an autonomous sensor apparatus, a path through a site having three-dimensional objects located therein. The method comprises obtaining, by a plurality of sensors on-board the autonomous sensor apparatus, one or more data sets throughout the path. Each of the one or more data sets are associated with an attribute of one or more three-dimensional objects. The method comprises generating, by the first, second, or third processor, a working model from a collocated data set; and comparing, by the first, second, or third processor, the working model with one or more pre-existing models; to determine the presence and/or absence of anomalies. The presence and/or absence of anomalies are communicated as human-readable instructions.