A systematic literature review of machine learning methods applied to predictive maintenance

A Survey of Artificial Neural Network in Wind Energy Systems

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading

A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics

Wind energy is an emerging, clean and renewable source of energy. It is estimated that by year 2035, wind energy will be generating more than 25% of the world's electricity according to International Energy Agency (IEA). With the increase in demand for wind energy, its maintenance issues are becoming more prominent. The scheduled maintenance is more economical than unscheduled repair resulting from failure. So a continuous condition monitoring of various critical components like bearings, gearbox, and shafts of wind turbine is essential in order to enable predictive maintenance. 10% of the total failure is contributed by the bearings, shaft and gear box failures, but the downtime is more than 50% of the total downtime. This paper discusses the development of a bench-top test rig which is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).

Design and development of a wind turbine test rig for condition monitoring studies

Fault Size Estimation Using Vibration Signatures in a Wind Turbine Test-rig☆

Determination of fracture toughness and traction-separation relation in Mode I/II of a natural quasi-brittle orthotropic composite using multi-specimen approach

Sailendu Biswal

Papers

Design and development of a wind turbine test rig for condition monitoring studies

Fault Size Estimation Using Vibration Signatures in a Wind Turbine Test-rig☆

Determination of fracture toughness and traction-separation relation in Mode I/II of a natural quasi-brittle orthotropic composite using multi-specimen approach