Multi-modal Anomaly Detection via Sensor Fusion
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Solution Overview
Problem
Current anomaly detection systems using audio analytics often report spurious anomalies due to environmental factors, such as conversations or movements, leading to undesirable false event reporting.
Innovation Solution
A multi-modal anomaly detection system that combines audio data with other sensor modalities like visual and physical data to create an anomaly dependency graph, which helps suppress or confirm anomalies based on relationships between different sensor types, reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio analytics are used to detect anomalies in machine sounds, then anomaly detection capability is improved, but false positive rate increases due to environmental factors
Solution Approach 1:
The system segments the anomaly detection task into multiple independent analysis streams by deploying different sensor types (audio sensors, visual sensors, physical sensors) to detect different aspects of device behavior. Each sensor type processes its own data independently and generates separate anomaly detections, which are then combined to form a comprehensive assessment. This segmentation allows the system to isolate and analyze specific anomaly sources without interference from environmental factors that affect only certain sensor modalities.
Solution Approach 2:
The system merges data from multiple sensor modalities (audio, visual, physical) to create a unified anomaly detection result. By combining the strengths of different sensor types and cross-validating their detections, the system achieves more reliable anomaly identification. The fusion of multi-modal data allows the system to distinguish true device anomalies from environmental disturbances, thereby reducing false positives while maintaining high detection accuracy.
2Reliability
If multiple sensors are deployed to reduce false positives, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The system employs a unified anomaly detection framework that processes data from multiple sensor modalities through a common analysis architecture. The same baseline comparison and anomaly detection algorithms are applied across different sensor types, allowing the system to handle diverse sensor inputs without requiring separate complex processing pipelines for each modality. This universal approach reduces overall system complexity while maintaining multi-modal capabilities.
Solution Approach 2:
The system performs self-calibration and self-adjustment by automatically adapting to environmental conditions and device-specific characteristics during operation. Through continuous baseline learning and automatic parameter tuning, the system reduces false positives without requiring manual configuration or intervention. This self-service capability simplifies deployment and operation of the multi-sensor system, offsetting the inherent complexity through automation.
3Measurement precision
If baseline training is performed to improve anomaly detection accuracy, then measurement precision is improved, but loss of time occurs during the training period
Solution Approach 1:
The system performs baseline training during the device commissioning phase or during scheduled maintenance windows before normal operation begins. By completing the time-consuming baseline learning process in advance, the system establishes accurate reference profiles for normal device behavior without interrupting subsequent operational monitoring. This preliminary action ensures high detection accuracy from the start of production monitoring while minimizing impact on operational time.
Solution Approach 2:
The system implements periodic baseline updates and re-training cycles that occur during scheduled maintenance intervals rather than continuously during operation. This periodic approach allows the system to maintain accurate baselines through regular updates while avoiding continuous training interruptions. The periodic re-training adapts to gradual device wear and environmental changes without requiring constant operational downtime, thus balancing accuracy maintenance with operational continuity.
Data Source
AI summary
An embodiment of the invention may include a method, computer program product and computer system for multi-modal anomaly detection. The method, computer program product and computer system may include computing device which may receive primary sensor data from a primary sensor for a first device and determining a baseline from the primary sensor data for the first device. The computing device may receive primary sensor data from a primary sensor and secondary sensor data from a secondary sensor for a second device. The computing device may determine an anomaly in the primary sensor data for the second device based on the determined baseline for the first device and compare the anomaly in the primary sensor data for the second device to the received secondary sensor data for the second device.