Real-time intelligent fault diagnosis of rotating machines based on Archimedes algorithm optimised Gradient Boosting

ABSTRACT

Intelligent fault diagnostics is an essential part of the concept of Industry 4.0. Most of the developed techniques are usually tested by the same dataset whose data is acquired under stable conditions and not generally assessed in terms of essential metrics that set the robustness and real-time suitability of a model. Based on such drawbacks, this study aims to propose an effective fault diagnosis technique to be employable in real-world settings. For this purpose, numerous ensemble learning methods and signal processing techniques are utilized and compared to find the ultimate fault analysis model suitable for real-time settings. The best approach is optimized by employing Archimedes Optimisation Algorithm (ArchOA) to eliminate the redundant features and therefore, reduce the computational load and time consumption. It is concluded that the Gradient Boosting Decision Tree (GBDT) is the most accurate model for all fault types and the most suitable one for potential real-time analysis considering its low time consumption and model-built time. Employing ArchOA reduces time consumption by 25% by eliminating redundant features by 36%. The proposed approach successfully identified the faulty state including the fault type with an average accuracy of 99.16% for the testing set and 97.50% for the real-time analysis.

KEYWORDS:

• Fault diagnosis
• rotating machine
• ensemble learning
• signal decomposition
• Archimedes optimisation algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).