AI Anomaly Detection for Physical Assets
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Solution Overview
Problem
Current methods for monitoring physical assets, such as buildings and equipment, rely on manual and time-consuming efforts, including sensor configurations and visual inspections, which are inefficient and may not detect anomalies in a timely manner, leading to potential damage from leaks or other issues.
Innovation Solution
An anomaly detection method using artificial intelligence that receives image data from cameras to determine the probability of physical anomalies, employing trained AI models to classify specific types of anomalies and generate alerts, with the option to incorporate audio and vibration data, and update models based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual sensor configuration and visual inspection are used to monitor physical assets, then the system can be implemented with simple equipment, but the monitoring process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual visual inspection and sensor configuration with an AI-based image analysis system. The system automatically processes images captured by cameras to detect anomalies, eliminating the need for manual inspection while maintaining comprehensive monitoring coverage across multiple physical assets.
Solution Approach 2:
The AI model performs self-learning and automatic anomaly detection without requiring manual intervention. The system continuously processes images, identifies patterns, and triggers alerts autonomously, enabling the monitoring system to serve itself rather than requiring constant human operation.
2Measurement precision
If physical sensors are deployed to detect leaks, then specific anomalies can be detected, but the sensors require manual configuration and have limited tracked surface area
Solution Approach 1:
The patent replaces complex physical sensor configurations with a camera-based visual inspection system processed by AI. Instead of configuring multiple sensors for different anomaly types, the system uses image processing to detect various anomalies including leaks, fire, and equipment failures, simplifying the overall system complexity while maintaining high detection accuracy.
Solution Approach 2:
The AI-based image analysis system serves multiple detection functions simultaneously. A single camera system can detect various types of anomalies (leaks, fire, equipment failures, unauthorized access) by training the AI model on different anomaly patterns, eliminating the need for separate specialized sensors for each anomaly type.
3Reliability
If technicians perform periodic visual inspections, then manual monitoring can be performed, but anomalies are not detected in a timely manner and significant damage can occur
Solution Approach 1:
The AI-based system operates continuously and automatically processes images without interruption. Unlike periodic manual inspections, the system maintains constant monitoring, immediately detecting anomalies as they occur and triggering real-time alerts, thereby eliminating time delays and preventing significant damage.
Solution Approach 2:
The patent replaces manual periodic inspection with automated continuous image analysis. The system continuously captures and processes images, providing real-time anomaly detection that is both reliable and timely, eliminating the delays inherent in scheduled manual inspections.
4Area of stationary object
If multiple sensors and cameras are deployed to monitor wide areas, then coverage area increases, but the system complexity and installation requirements increase
Solution Approach 1:
The patent employs a universal AI-based image analysis system that can process images from multiple cameras and monitor diverse physical assets (buildings, equipment, infrastructure) using the same technological platform. This multi-functional approach enables wide area coverage without proportionally increasing system complexity, as the same AI model handles various anomaly types across different locations.
Data Source
AI summary
In accordance with the present disclosure, anomaly detection systems and methods are disclosed for automatically detecting physical anomalies using image data. The anomaly detection systems and methods disclosed herein can be used to detect anomalies in physical assets, such as anomalies present within a building, at a site, and/or anomalies associated with a piece of equipment. Image data is received from one or more cameras that are configured to capture image data of a physical asset. A probability of a physical anomaly being present in the image data is determined using an artificial intelligence model. The image data may be further analyzed to determine that the physical anomaly is a specific type of anomaly. An alert is output when the probability of the physical anomaly being present in the image data exceeds a threshold. A method of training an anomaly detection model is also disclosed.