Journal
2022 68TH ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM (RAMS 2022)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/RAMS51457.2022.9893939
Keywords
machine learning; linear regression; efficiency
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This paper presents constant-time linear regression algorithms and discusses the potential and limitations of applying machine learning techniques to real-time system monitoring.
Constant-time linear regression algorithms are presented in this paper. Machine learning techniques rely on a good amount of data to achieve high performance. However, processing big data is time-consuming and thus limits the potential of applying machine learning techniques to real-time R&M (Reliability and Maintainability) systems. Linear regression has a long history in R&M applications, which also serves as a basic machine learning method. Most linear regression applications conduct the learnings off-line, not in real time, which causes the resulting model to be outdated. Realizing that continuous learning is crucial in real-time operations, this study is devoted to constant-time algorithms findings. Both lifelong and moving-time-window constant-time linear regression algorithms are presented. The case study demonstrates the benefits of applying the constant-time algorithms to real-time system monitoring.
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