Anomaly Detection System with Rule Modification Interface
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Solution Overview
Problem
Analyzing large datasets to identify anomalies and trends is challenging due to the complexity and volume of data, making it difficult to efficiently manage and visualize insights from assets like IoT devices.
Innovation Solution
A computer-implemented method for machine learning anomaly detection that analyzes assets using anomaly-detection rules, provides user interfaces for identifying anomalous states, and allows users to modify rules based on feedback for improved future analyses, integrating asset-specific and non-asset-specific signals for comprehensive data visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly-detection rules are applied to analyze large datasets, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments the anomaly detection process into distinct components: data collection from multiple sources, correlation engine for integrating data, machine learning model training, and rule-based anomaly detection. This modular segmentation allows each component to be optimized independently, improving detection capability while managing system complexity through organized modularity.
Solution Approach 2:
A correlation engine acts as an intermediary layer between raw data collection and anomaly detection. This intermediary component pre-processes and correlates data from multiple sources before feeding it to the anomaly detection system, reducing the complexity of direct analysis while enhancing detection precision through prepared, correlated inputs.
2Measurement precision
If user feedback is incorporated to modify anomaly-detection rules, then detection accuracy is improved, but operational complexity increases
Solution Approach 1:
The system implements a feedback mechanism where user responses to detected anomalies are automatically incorporated to refine detection rules. When users confirm or reject anomaly detections, this feedback loops back to adjust the machine learning models and rule parameters, continuously improving accuracy while automating the refinement process to minimize operational complexity.
Solution Approach 2:
The anomaly detection system performs self-refinement through automated model retraining and rule adjustment based on accumulated user feedback. This self-service capability allows the system to improve its own detection accuracy without requiring manual intervention for each rule modification, reducing operational complexity while maintaining high accuracy through continuous learning.
3Loss of information
If multiple data sources are correlated for comprehensive analysis, then insight quality is improved, but data processing complexity increases
Solution Approach 1:
The system merges multiple data sources including sensor data, operational data, and external data into a unified correlated dataset. By combining these diverse sources through the correlation engine, the system achieves comprehensive insights that capture complex relationships across different data types, while the unified processing approach manages the complexity of handling multiple sources through standardized integration protocols.
Data Source
AI summary
The disclosure generally describes methods, software, and systems, including a method for machine learning anomaly detection for a set of assets. Assets are analyzed using anomaly-detection analysis and a set of anomaly-detection rules. Each asset is associated with correlated records comprising characteristics of the particular asset and characteristic of non-asset-specific signals. Each anomaly-detection rule is associated with conditions determined to be indicative of a potential anomaly. At least a subset of the assets are provided for presentation in a user interface. Each asset is identified as being in a potential anomalous or non-anomalous state based on the anomaly-detection analysis. Input is received from a user identifying at least one asset as anomalous as a non-anomalous asset. Based on the received input, at least one anomaly-detection rule is modified that was applied to identify the asset as anomalous. The modified rule is stored for future analyses.