Structure Boundary Monitoring for Root Cause Anomaly Detection
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Solution Overview
Problem
Existing monitoring systems for complex structures fail to identify the root cause of issues due to inefficient data analysis, often detecting problems only when obvious symptoms appear, leading to reduced reliability and productivity, as they monitor individual elements as monolithic units that do not encapsulate the causing element and impacted element, resulting in missed detection and repeated issues.
Innovation Solution
A system utilizing a processing subsystem with a boundary creation module, relation identification module, and anomaly detection module, employing deep learning models to create structure boundaries, identify correlations and interrelations between elements, and detect anomalies by analyzing sensor data deviations from statistically significant thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If individual elements are monitored as monolithic units, then device complexity is reduced, but measurement precision and ability to identify root cause deteriorate
Solution Approach 1:
The system segments the complex structure into multiple elements and monitors each element's data separately. The anomaly detection module divides the overall anomaly into contributions from individual elements, allowing precise identification of root causes while maintaining manageable system complexity through structured data organization.
Solution Approach 2:
The system adds a new dimension of analysis by decomposing anomalies into element-wise contributions. Instead of treating the monolithic unit as a single entity, the system analyzes data from multiple dimensions (individual elements) and synthesizes the results to identify root causes, thereby improving measurement precision without proportionally increasing complexity.
2Ease of operation
If small monolithic units are monitored, then ease of operation is improved, but reliability deteriorates due to inability to capture causing and impacted elements
Solution Approach 1:
The system segments the monitoring scope into multiple elements within the complex structure. By analyzing data from individual elements and their interrelations, the system can reliably identify both causing and impacted elements while maintaining operational simplicity through automated anomaly decomposition and element contribution analysis.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring element data, detecting anomalies, identifying root causes, and providing actionable insights. This feedback loop enables reliable outage detection by tracing anomalies back to their source elements and tracking their impact on other elements, thereby improving reliability without compromising ease of operation.
3Measurement precision
If deep learning models are used to identify correlations across all elements, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the data processing task by analyzing correlations between individual elements rather than treating the entire complex structure as a single unit. The deep learning model processes element-level data separately and synthesizes results, improving correlation detection accuracy while managing complexity through structured, modular data organization and processing.
4Ease of operation
If multiple monolithic units are monitored separately, then ease of operation is maintained, but loss of information increases due to disjointed alarms
Solution Approach 1:
The system merges information from multiple element monitors by decomposing the overall anomaly into contributions from individual elements. This integration preserves complete information about alarm correlations and interrelations while maintaining operational simplicity through automated synthesis of element-level data into a unified anomaly assessment.
Data Source
AI summary
A system (10) for monitoring complex structures (15) is disclosed. The system includes a boundary creation module (50) to create a group of elements corresponding to the complex structures to define a structure boundary. The boundary creation module collects sensor data from sensors (55) coupled to the corresponding complex structures within the structure boundary. The system includes a relation identification module (60) to determine correlation across the sensor data corresponding to the sensors within the structure boundary using a deep learning model. The relation identification module identifies interrelations between the group of elements by tracking the correlation across the sensor data using the deep learning model. The system includes an anomaly detection module (70) to identify a set of characteristics of the sensor data based on the interrelations the group of elements. The anomaly detection module detects an anomaly in the group of elements by analyzing the set of characteristics.