Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery

Ietezaz Ul Hassan, Krishna Panduru () and Joseph Walsh ()
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Ietezaz Ul Hassan: IMaR Research Centre, Munster Technological University, V92 CX88 Tralee, Ireland
Krishna Panduru: IMaR Research Centre, Munster Technological University, V92 CX88 Tralee, Ireland
Joseph Walsh: IMaR Research Centre, Munster Technological University, V92 CX88 Tralee, Ireland

Data, 2024, vol. 9, issue 5, 1-38

Abstract: Vibration-based condition monitoring plays an important role in maintaining reliable and effective heavy machinery in various sectors. Heavy machinery involves major investments and is frequently subjected to extreme operating conditions. Therefore, prompt fault identification and preventive maintenance are important for reducing costly breakdowns and maintaining operational safety. In this review, we look at different methods of vibration data processing in the context of vibration-based condition monitoring for heavy machinery. We divided primary approaches related to vibration data processing into three categories–signal processing methods, preprocessing-based techniques and artificial intelligence-based methods. We highlight the importance of these methods in improving the reliability and effectiveness of heavy machinery condition monitoring systems, highlighting the importance of precise and automated fault detection systems. To improve machinery performance and operational efficiency, this review aims to provide information on current developments and future directions in vibration-based condition monitoring by addressing issues like imbalanced data and integrating cutting-edge techniques like anomaly detection algorithms.

Keywords: predictive maintenance; vibration-based condition monitoring; heavy machinery predictive maintenance; operational safety; machinery performance (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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