Rotational Machine Fault Diagnosis Using Artificial Intelligence (AI) Strategies for the Operational Challenges Under Variable Speed Condition: A Review

Abstract

Rotational machines in industries often encounter uncertainties during operation, are monitored and diagnosed through machine condition monitoring. Particularly when speed varies, Artificial Intelligence (AI) provides a great deal of support in recognizing machine faults efficiently. This paper reviews various articles that focused on fault identification in industrial rotating machines under varying speed condition using AI techniques. It investigates various machinery faults, experimental datasets and reputed institutions datasets, complex signals from faulty components and their processing method. Further, the involvement of intelligent based techniques in extracting and selecting suitable features to classify defects are comprehensively reviewed. In addition, as multi-signal fusion strategy is evolving recently for fault diagnosis, the articles comprised of this tactic are also discussed. The objective of this article is to provide a comprehensive understanding of these scenarios to the institutions, industries and researchers, thereby facilitating further exploration.

Keywords

• Intelligent fault diagnosis
• multi-component faults
• multi-sensor fusion
• rotating machines
• signal processing
• variable speed condition