Time-Series Cluster Classification for New Machine Behavior Labels
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Solution Overview
Problem
Existing AI-based monitoring mechanisms for machines, particularly for electric submersible pumps, face challenges in formulating and maintaining rules, lack transparency in machine learning outputs, require large amounts of manually prepared training data, and struggle to address new behavior types immediately.
Innovation Solution
A device and method that segments time series data into clusters based on probabilistic models, utilizes existing label information for classification, and allows user input to generate new label information, resolving conflicts through probabilistic models and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used for classification, then classification accuracy is improved, but the system becomes a black-box approach offering no explanations and requiring large amounts of manually prepared training data
Solution Approach 1:
The system performs preliminary clustering of time series data into behavior patterns before classification. This preliminary segmentation organizes the data structure, allowing the classification stage to work with pre-grouped patterns rather than raw data, reducing the need for extensive manual training data preparation while maintaining accuracy.
Solution Approach 2:
The time series data is segmented into clusters representing different behavior patterns. This segmentation divides the complex classification problem into smaller, more manageable pattern recognition tasks, reducing the computational complexity and training data requirements while preserving classification accuracy.
2Ease of operation
If pre-defined rules are used for classification, then transparency and explainability are improved, but the system complexity increases and requires domain expert formulation
Solution Approach 1:
The system automatically learns behavior patterns from the data through clustering and classification stages, eliminating the need for manual rule formulation by domain experts. The system serves itself by automatically discovering patterns and creating classification rules from the clustered data, maintaining transparency while reducing complexity.
3Adaptability or versatility
If machine learning is used, then new behavior types can be learned, but the system cannot address new behavior types immediately until the machine learning process is completed
Solution Approach 1:
The system dynamically adapts to new behavior types through an iterative process. When new time series data is received, it is clustered into existing or new behavior patterns, and the classification model is continuously refined. This dynamic approach allows the system to immediately address new behavior types by clustering them and progressively improving classification accuracy over time.
4Measurement precision
If manual labeling of time segments is required, then classification accuracy is improved, but the productivity decreases due to tedious data preparation
Solution Approach 1:
Instead of requiring manual labeling of all time segments, the system applies partial action by automatically clustering the majority of data and only requiring minimal user input for ambiguous cases. This approach maintains high classification accuracy while dramatically improving productivity by eliminating the need for exhaustive manual labeling.
Data Source
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AI summary
For analyzing time series data monitored on a machine, a device (200) segments the time series data into multiple time segments (230). Further, the device (200) determines a cluster of time segments estimated to have the same dynamics of the time series data. The device (200) then classifies the cluster based on label information associated with at least one of the time segments. If none of the time segments of the cluster has associated label information, the device (200) presents at least a part of the time series data of the cluster to a user (10). Based on a user input received in response to said presenting the time series data, the device (200) classifies the cluster and generates label information associated with the time segments of the cluster. The generated label information indicates a result of classifying the cluster.