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Profile Abstract: An Optimization-Based Subset Selection and Summarization Method for Profile Data Mining
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AI
DOI：10.1109/TII.2022.3227642.png)
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Abstract
En 中文
Nowadays, profile data mining techniques facilitate effective process monitoring, quality control, fault diagnosis, etc., with considerable benefits to manufacturing industry. However, regarding the complex system in modern manufacturing industry, there are two significant challenges for application development based on profile data mining. First, the staggering data volume leads to high memory and computational requirements. Second, the noisy signals in collected data may deteriorate useful information and model performance. This article proposes a novel algorithm for profile data mining called profile abstract, which simultaneously enables profile data compression and segmentation. The proposed algorithm mainly considers the scenario of fault diagnosis and can be utilized as a pre-processing step to address the above challenges. Profile abstract seeks to find a subset of raw data or a group of models as representatives that preserve the essential characteristics of raw data. Finding the data representatives helps reduce data redundancy while maintaining the model performance. Model representatives assist in describing the complex pattern of the profile, which can be used for pattern-based data segmentation. After data segmentation, information gain is adopted to determine the critical primitives for model improvement. In this article, validation of the proposed method's superiority is performed with two datasets from a real production line and one simulation dataset.
Keywords:
Data compression
fault diagnosis
optimization
primitive extraction
profile data
subset selection
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