Timeseries Feature Extraction for Dataset Creation in Prognostic Health Management: A Case Study in Steel Manufacturing

Abstract

This study focuses on a critical aspect of implementing prognostics and health management (PHM) for assets: the creation of a descriptive dataset. In real-world applications, dealing with sparse and unlabelled big data is common, particularly in industries like production lines where complex subprocesses are monitored by multiple sensors. Moreover, selective application of quality control means that much of the data lacks information about end properties, making datasets provided by manufacturers unsuitable for PHM frameworks. This work aims to bridge the gap between raw production data and PHM frameworks, focusing on steel manufacturing management. In the context of steel manufacturing, compromised surface quality, characterized by thicker oxide layers chipping during milling, has been observed. We propose inferring compromised coils by analyzing temperature profiles directly before the coiling station to address this. Deviations from the goal temperature profile can indicate compromised surface quality, eliminating the need for tedious oxide layer thickness measurements, which are not feasible for continuous hot strip milling processes. The available dataset comprised multiple years of production, with no direct indication of the surface quality. Exploratory clustering analysis was the first step in the lack of labels. Even though indicative of the underlying pattern of the healthy/damaged coils distinction, three shortcomings were identified. Clustering was solely based on the similarity between the temperature profiles of the coils, so no domain knowledge was included regarding the goal temperature profile. Additionally, since different steel grades have different goal profiles, the model needs to be specifically trained for each grade. Also, a soft classification between healthy and damaged can provide more detailed information about the surface quality. Coils with low-confidence classifications can be identified and treated accordingly, thereby improving PHM framework performance by providing a dataset with only high-confidence samples. To tackle these issues, an expert-knowledge-based normalization technique and feature engineering, paired with synthetic labelling, contributed to the creation of a soft neural network classifier. This study presents the reality of handling real-world data for PHM applications and highlights the need for careful and informed feature extraction. This ensures the seamless integration of PHM frameworks into real-world systems, ultimately enhancing production yield by improving end-product quality.