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The integration of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT) has revolutionized data utilization in various sectors. Predictive maintenance (PDM) emerges as a pivotal strategy, leveraging data from diverse manufacturing and sensing sources to anticipate equipment failures. This paper presents a comprehensive overview of the application of machine learning (ML) techniques in PDM, categorizing advancements based on ML algorithms and data acquisition equipment. Through highlighting significant contributions and ongoing pilot projects, this review provides valuable insights for optimizing maintenance strategies and enhancing equipment performance and longevity.
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Agarwal et al. (2024) studied this question.
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