@article{sajid_data_2021,
	title = {Data science applications for predictive maintenance and materials science in context to {Industry} 4.0},
	issn = {2214-7853},
	url = {https://www.sciencedirect.com/science/article/pii/S221478532100448X},
	doi = {10.1016/j.matpr.2021.01.357},
	abstract = {With the revolutionising of the industry to the next generations, machines have become more complicated. If they are not put to regular maintenance then there is more breakdown and disruption in the production line. These days, data science techniques have applications over almost every field and likewise are being applied to Industry 4.0. In this advanced setup, massive data is created and stored every second. Experts with expertise in advanced mathematical and computational skills are in demand to identify root causes of failures and quality deviations of a machine, contributing to minimising a loss in time and money. Moreover, new elements with tailored properties can be discovered with material theories and computational skills. The integration of data science with industry 4.0 will increase efficiency and will be helpful to predict the quality of material minimising the production line cost and time. Different research articles on industry 4.0, data science and predictive maintenance are identified and studied. This paper identifies five critical processes of data scientists for predictive maintenance and discussed briefly through a literature review. Data science uses various processes, scientific methods, and algorithms to extract knowledge from a large amount of data. It can collect a massive amount of industrial data, which is further used to improve the manufacturing systems' efficiency and reliability. It helps analyse the data and become essential for Industry 4.0.},
	language = {en},
	urldate = {2021-02-22},
	journal = {Materials Today: Proceedings},
	author = {Sajid, Sufiyan and Haleem, Abid and Bahl, Shashi and Javaid, Mohd and Goyal, Tarun and Mittal, Manoj},
	month = feb,
	year = {2021},
	keywords = {Data science, Decision making, Industry 4.0, Machine learning, Materials, Predictive maintenance},
}

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Experts with expertise in advanced mathematical and computational skills are in demand to identify root causes of failures and quality deviations of a machine, contributing to minimising a loss in time and money. Moreover, new elements with tailored properties can be discovered with material theories and computational skills. The integration of data science with industry 4.0 will increase efficiency and will be helpful to predict the quality of material minimising the production line cost and time. Different research articles on industry 4.0, data science and predictive maintenance are identified and studied. This paper identifies five critical processes of data scientists for predictive maintenance and discussed briefly through a literature review. Data science uses various processes, scientific methods, and algorithms to extract knowledge from a large amount of data. It can collect a massive amount of industrial data, which is further used to improve the manufacturing systems' efficiency and reliability. 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If they are not put to regular maintenance then there is more breakdown and disruption in the production line. These days, data science techniques have applications over almost every field and likewise are being applied to Industry 4.0. In this advanced setup, massive data is created and stored every second. Experts with expertise in advanced mathematical and computational skills are in demand to identify root causes of failures and quality deviations of a machine, contributing to minimising a loss in time and money. Moreover, new elements with tailored properties can be discovered with material theories and computational skills. The integration of data science with industry 4.0 will increase efficiency and will be helpful to predict the quality of material minimising the production line cost and time. Different research articles on industry 4.0, data science and predictive maintenance are identified and studied. This paper identifies five critical processes of data scientists for predictive maintenance and discussed briefly through a literature review. Data science uses various processes, scientific methods, and algorithms to extract knowledge from a large amount of data. It can collect a massive amount of industrial data, which is further used to improve the manufacturing systems' efficiency and reliability. 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