Predictive Maintenance and Fault Monitoring Enabled by Machine Learning: Experimental Analysis of a TA-48 Multistage Centrifugal Plant Compressor
نویسندگان
چکیده
In an increasingly competitive industrial world, the need to adapt any change at time has become a major necessity for every industry remain and survive in their environments. Industries are undergoing rapid perpetual changes on several levels. Indeed, latter requires companies be more reactive involved policies of continuous improvement order satisfy customers maximize quantity quality production, while keeping cost production as low possible. Reducing downtime is one objectives these industries future. This paper aimed apply machine learning algorithms TA-48 multistage centrifugal compressor failure prediction remaining useful life (RUL), i.e., reduce system using predictive maintenance (PdM) approach through adoption Industry 4.0 approaches. To achieve our goal, we followed methodology workflow that allows us explore process data model training. Thus, comparative study different was carried out arrive final choice, which based implementation LSTM neural networks. addition, its performance improved sets were fed incremented. Finally, deployed allow operators know times compressors subsequently ensure minimum rates by making decisions before failures occur.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13031790