Deep Convolutional Factor Analyzer for Time Series Anomaly Detection
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Solution Overview
Problem
Existing deep generative models face challenges in training due to non-linear activation units leading to impractically slow approximation of posterior distributions, especially when applied to deep models for multivariate time series data, which complicates anomaly detection and classification.
Innovation Solution
A deep convolutional factor analyzer (DCFA) is introduced, which extends the factor analyzer by replacing matrix multiplication with convolution to model both spatial and temporal dependencies, reducing complexity through down-sampling and using linear Gaussian nodes, allowing for efficient estimation of posterior distributions using variational Bayes algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep generative models use non-linear activation units to model complex patterns, then the model can capture richer temporal and spatial dependencies, but the posterior distribution approximation becomes impractically slow
Solution Approach 1:
The patent changes the activation function parameter from non-linear (e.g., ReLU, sigmoid) to linear Gaussian nodes. This parameter change allows the model to maintain the ability to capture temporal and spatial dependencies through the deep convolutional architecture while enabling efficient exact inference through variational Bayes algorithms, resolving the contradiction between model capacity and training time.
2Adaptability or versatility
If the factor analyzer uses matrix multiplication to model dependencies, then the computation is straightforward, but it cannot efficiently model both spatial and temporal dependencies in multivariate time series
Solution Approach 1:
The patent replaces the traditional matrix multiplication operation with convolution operations in the factor analyzer. This substitution enables the model to efficiently model both spatial and temporal dependencies in multivariate time series data through the convolutional layers, while the overall model structure remains manageable through the factor analysis framework.
Solution Approach 2:
The patent introduces temporal dimension modeling through convolutional layers that operate on both spatial and temporal dimensions simultaneously. By adding this temporal dimension capability to the traditional spatial factor analysis, the model can capture dependencies across multiple dimensions without excessive complexity increase.
3Adaptability or versatility
If deep convolutional models are used to increase modeling capability, then the model can capture more complex patterns, but the training becomes significantly more complex and time-consuming
Solution Approach 1:
The patent changes the activation function parameter to linear Gaussian nodes throughout the deep convolutional architecture. This parameter change simplifies the training algorithm by enabling exact inference through variational Bayes methods, reducing training complexity while maintaining the deep convolutional model's capability to capture complex patterns.
Solution Approach 2:
The patent employs variational Bayes algorithms that can efficiently compute posterior distributions without requiring complex iterative approximation methods. The linear Gaussian nodes enable the model to self-service its inference needs through analytical solutions, reducing the need for complex external optimization algorithms.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for generating and training a deep convolutional generative model for multivariate time series modeling and utilizing the model to assess time series data indicative of a machine or machine component's operational state over a period of time to detect and localize potential operational anomalies.