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Artificial intelligence-based data-driven prognostics in industry: A survey
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AI
DOI：10.1016/j.cie.2023.109605.png)
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摘要
En 中文
In the age of Industry 5.0, prognostics and health management (PHM) is very important for proactive and scheduled maintenance in industrial processes. The target of prognosis is the health state prediction of the system or machine under consideration, hence its Remaining Useful Life RUL. The life of a tool, a part, or a component of the system must be tracked to increase its productivity, reduce human effort and save lives. Data driven prog-nostics is highly relying on statistical or artificial intelligence AI methods including machine learning (ML) and deep learning (DL) models. AI is a massive enlarging field with encouraging outcomes in prognostics for modelling of data with complex representations and temporal dependencies. A sample of latest research in prognostics especially in industry applications has been collected during this research. About 76% of the collected research papers used data-driven prognostics in their model including 48% applied DL different ar-chitectures for prognostic purposes in industrial systems in the last few years. Therefore, this survey concentrate on presenting AI-based data-driven prognostics in industrial systems especially DL-based architectures. The study also puts spot on the main challenges with opportunities of future work in the DL-based PHM applications in the age of Industry 5.0.
Keyword:
Data driven prognostics
Prognostics and health management
Remaining useful life
Machine learning prognostics
Deep learning prognostics
Deep learning architectures
Artificial neural network
Performance metrics
期刊
IF:
6.5
论文数:
1.0W
被引数:
3.8W
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