摘要：AbstractThe chemical process industry makes increasingly use of a diversity of data collectors, that should be properly integrated to build effective solutions for process monitoring, control and optimization. Concerning the assessment of products properties, one of the most common scenarios involve the collection of data from plant laboratories that provide more accurate measurements at lower rates, together with more frequent measurements or predictions of lower quality. Soft sensors and online analyzers are examples of viable alternatives for acquiring more frequent and updated information, although with a higher uncertainty. All of these data collectors have informative value and should be considered when it comes to estimate key product attributes. This is the goal of fusion methods, whose importance grows together with the increase in the number of sensors and data sources available. In this article, two fusion schemes that address prevailing characteristics of industrial data are proposed and compared: one version of the classic tracked Bayesian fusion scheme (TBF) and a novel modification of the track-to-track algorithm, designated as bias-corrected track-to-track fusion (BCTTF). The proposed methodologies are able to cope with the multirate nature of data and irregularly sampled measurements that present different uncertainty levels. An application to a real industrial case study shows that BCTTF presents better prediction performance, higher alarm identification sensitivity and leads to a smoother estimated signal.
关键词：KeywordsSensor fusionBayesian fusionKalman filterMachine learningIndustrial case study
