Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study

• Published in: Journal of big data 2024-11, Vol.11 (1), p.162-22, Article 162
• Main Authors: Koković, Vladimir, Pavlović, Kosta, Mijanović, Andjela, Kovačević, Slavko, Mačužić, Ivan, Božović, Vladimir
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Big Data; Big data analytics; Big Data and Artificial Intelligence in Emerging Engineering Fields; Business metrics; Communications Engineering; Computational Science and Engineering; Computer Science; Corporate learning; Data Mining and Knowledge Discovery; Database Management; Downtime; Information Storage and Retrieval; Lean industrial systems; Machine learning; Manufacturing; Mathematical Applications in Computer Science; Modelling; Networks; Paper manufacturing; Parameter estimation; Performance measurement; Predictive maintenance; Probability density functions; Production downtime; Statistical analysis; Statistical methods; Unsupervised learning; Vibration measurement
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-024-01030-4

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.