Sensor Array Anomaly Detection via Dependency Models
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Solution Overview
Problem
Current sensor management systems are inefficient and cost-ineffective in managing the lifecycle of entire sensor arrays, as individual sensor failures can affect the entire array and require manual management, which is not feasible.
Innovation Solution
A self-organizing sensor lifecycle management system that monitors and manages entire sensor arrays, allowing for proactive adjustments and recalibrations based on dependency models, anomaly detection, and automatic configuration of new sensors to maintain optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual management of each sensor is performed, then individual sensor failures can be addressed, but the management process becomes inefficient and cost-ineffective
Solution Approach 1:
The system implements self-service through automated anomaly detection and dependency analysis that proactively identifies sensor failures and their impacts without human intervention. The automated configuration adjustment mechanism modifies sensor settings based on detected anomalies, enabling the system to manage itself and maintain reliability without manual intervention in each sensor failure case.
Solution Approach 2:
The system establishes continuous feedback loops where sensor data is monitored, anomalies are detected, and corrective actions are automatically implemented. The dependency model provides feedback about how sensor failures affect other sensors, enabling the system to adjust configurations proactively and maintain overall array reliability through automated closed-loop control.
2Reliability
If the entire sensor array is monitored and managed as a system, then proactive adjustments can be made, but the system complexity increases
Solution Approach 1:
The system segments the complex task of array-wide management into distinct functional modules: anomaly detection components, dependency modeling components, and automated configuration adjustment components. This segmentation allows each module to handle specific aspects of sensor array management independently, reducing overall system complexity while maintaining comprehensive monitoring and management capabilities.
Solution Approach 2:
The system introduces intermediary components including dependency models that mediate between sensor failures and their impacts, and automated configuration mechanisms that serve as intermediaries between anomaly detection and corrective actions. These intermediaries simplify the management process by handling the complexity of system-wide interactions automatically.
3Reliability
If dependency models and anomaly detection are implemented, then corrective actions can be initiated across multiple sensors, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing dependency models that map relationships between sensors before failures occur. When anomalies are detected, the system can immediately query these pre-built models to identify affected sensors and initiate corrective actions without performing complex real-time analysis, significantly reducing processing time while maintaining reliable system-wide management.
Data Source
AI summary
In example implementations, an apparatus is provided. The apparatus includes an interface, an inheritance engine, an anomaly detection module and a processor. The interface communicates with a sensor array. The inheritance engine is used to create a model of the sensor array based on information collected from the each one of a plurality of nodes in the sensor array over the interface. The anomaly detection engine is used to monitor the sensor array in accordance with the model to detect an anomaly and initiate a corrective action to correct the anomaly in two or more of the plurality of nodes within the sensor array simultaneously. The processor is in communication with the inheritance engine and the anomaly detection engine is used to execute instructions associated with the inheritance engine and the anomaly detection engine.