Measurement Signal Embedding for Automatic Anomaly Detection
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Solution Overview
Problem
Existing methods for detecting anomalies in measurement signals require manual setup and prior knowledge of signal characteristics, making them inefficient and user-unfriendly.
Innovation Solution
A method that applies a model to digitized measurement signals to create a manifold where similar segments are closer and dissimilar segments are farther apart, enabling automatic and user-friendly anomaly detection, using a metric tensor and machine learning models for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setup methods are used for anomaly detection, then detection accuracy can be achieved, but setup time and complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically determining analysis parameters and creating the manifold structure before actual anomaly detection begins. The machine learning model pre-processes the measurement signal to establish the manifold, eliminating the need for manual parameter setup during operation.
Solution Approach 2:
The system serves itself by automatically selecting analysis parameters and configuring the anomaly detection process without user intervention. The machine learning model autonomously processes the measurement signal, determines optimal parameters, and performs classification, making the system self-sufficient in the anomaly detection task.
2Adaptability or versatility
If manual parameter adjustment is required, then detection conditions can be customized, but ease of operation deteriorates
Solution Approach 1:
The system automatically determines analysis parameters and configures detection conditions based on the measurement signal characteristics, eliminating the need for users to manually adjust parameters. The machine learning model adapts to different signal types and automatically optimizes detection conditions.
Solution Approach 2:
The system dynamically changes analysis parameters based on the characteristics of the measurement signal being analyzed. The machine learning model adjusts parameters such as segment duration, overlap, and classification thresholds automatically according to the signal properties, providing adaptability without requiring user intervention.
3Measurement precision
If complex parameter setup is required, then analysis precision can be improved, but device complexity increases
Solution Approach 1:
The machine learning model automatically determines all necessary analysis parameters including segment duration, overlap percentage, and classification thresholds without requiring user configuration. The system self-configures the complete anomaly detection pipeline, eliminating complex parameter setup while maintaining high analysis precision.
Solution Approach 2:
The manifold-based anomaly detection system is designed to be universal and can analyze different types of measurement signals (audio, sensor data, communication signals) using the same underlying architecture. The machine learning model adapts to various signal types without requiring complex parameter adjustments, providing multi-functionality with simple operation.
4Productivity
If automated model application is used, then productivity increases, but measurement precision may be compromised
Solution Approach 1:
The system replaces manual mechanical parameter adjustment with an automated machine learning model that processes measurement signals. The model automatically segments the signal, creates manifold embeddings, and performs anomaly classification, achieving both high productivity through automation and high precision through intelligent algorithmic processing.
Solution Approach 2:
The machine learning model dynamically adjusts analysis parameters based on signal characteristics to optimize both processing speed and detection accuracy. The system changes parameters such as segment duration and classification thresholds automatically according to the specific measurement signal being analyzed, maintaining precision while achieving automated high-speed processing.
Data Source
AI summary
A method of analyzing a measurement signal is described. The method includes receiving a measurement signal, wherein the measurement signal is a digitized signal; and applying a model to the measurement signal, thereby creating an embedding of signal segments of the measurement signal in a manifold where signal segments having a higher degree of similarity between each other are positioned closer to one another in the manifold and signal segments having a lower degree of similarity between each other are positioned more distant to one another in the manifold.