Machine Operation Anomaly Detection Using Hyperbolic Embeddings
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Solution Overview
Problem
Existing anomaly detection systems face challenges in efficiently detecting anomalous operation of machines due to high-dimensional embeddings that exceed computational resources and fail to distinguish between anomalous signals and domain shifts.
Innovation Solution
The use of hyperbolic embeddings generated by a deep neural network, which projects Euclidean embeddings into a hyperbolic space, allowing for more efficient anomaly detection with fewer dimensions, thus reducing computational burden and improving resilience to domain shifts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensional embeddings are used for anomaly detection, then measurement precision is improved, but device complexity increases and productivity decreases
Solution Approach 1:
The patent transforms the embedding space from Euclidean to hyperbolic geometry, changing the dimensional structure in which anomalies are detected. This dimensionality change allows the system to maintain high detection accuracy while reducing the computational burden associated with traditional high-dimensional Euclidean embeddings, as hyperbolic space can represent hierarchical data more efficiently with fewer dimensions.
2Measurement precision
If high-dimensional embeddings are used for anomaly detection, then measurement precision is improved, but productivity decreases
Solution Approach 1:
By transitioning to hyperbolic embedding space, the system achieves the same anomaly detection accuracy with reduced dimensional requirements, directly improving processing speed and computational efficiency while maintaining measurement precision.
3Measurement precision
If high-dimensional embeddings are used for anomaly detection, then measurement precision is improved, but loss of energy increases
Solution Approach 1:
The hyperbolic embedding approach reduces the dimensional complexity of the feature space, which directly decreases the computational operations required for anomaly detection, thereby reducing energy consumption while preserving detection accuracy.
4Ease of operation
If conventional Euclidean embeddings are used, then ease of operation is maintained, but reliability decreases due to domain shift sensitivity
Solution Approach 1:
The patent changes the geometric parameters of the embedding space from Euclidean to hyperbolic, fundamentally altering how data is represented. This parameter change makes the anomaly detection system more robust to domain shifts while maintaining ease of operation, as the hyperbolic structure naturally accommodates hierarchical variations in data.
Data Source
AI summary
A computer-implemented method for detecting anomaly of an operation of a machine based on a signal indicative of the operation of the machine performing a task, comprises collecting hyperbolic embeddings of the signal indicative of the operation of the machine. The hyperbolic embeddings lie in a hyperbolic space. The method further comprises performing the detection of the anomaly of the operation of the machine based on the hyperbolic embeddings to determine an anomaly score and rendering the anomaly score. The machine operation is controlled based on the rendered anomaly score.