Unsupervised machine learning framework for early machine failure detection in an industry. (4th July 2021)

• Record Type: Journal Article
• Authors: Hasan, Nabeela; Chaudhary, Kiran; Alam, Mansaf
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 24:Number 5(2021)
• Page Start: 1497
• Page End: 1508
• Publication Date: 2021-07-04
• Subjects: 68Txx -- 68M11 -- 68-XX; Industry -- Predictive maintenance -- Machine learning; Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2021.1951434
• Languages: English
• ISSNs: 0972-0529

Abstract

Abstract: This article describes a credible and prognostic analysis in this study for failure detection of a machine in the industry. An interpretable methodology and an informative functionality are portrayed, trained with the dataset and their explicatory implementation is compared and evaluated. In this paper, we will design a deployable end-to-end grading model to forecast whether or not the machine will fail. We will train state-of-the-art algorithms for gradient enhanced decision trees (GBDT) and compare their performance.