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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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Abstract
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.
Keywords:
Data driven prognostics
Prognostics and health management
Remaining useful life
Machine learning prognostics
Deep learning prognostics
Deep learning architectures
Artificial neural network
Performance metrics
Journal
IF:
6.5
Papers:
1.0W
Citations:
3.8W
Organization
Cited Papers
ShareSaveEarly prediction of lithium-ion battery lifetime via a hybrid deep learning model.png)
MEASUREMENT
MEASUREMENTIF5.6
PREAI
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ShareSaveA data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM networkMEASUREMENT
MEASUREMENTIF5.6
PREAI
ShareSaveShareSaveShareSaveRemaining useful life estimation of engineered systems using vanilla LSTM neural networksNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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