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Machine Learning: An Expert Thinking System

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Handbook of Smart Materials, Technologies, and Devices

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Abstract

Industry 4.0 makes it believable to collect and investigate data across machines, aiding more efficient and flexible processes to manufacture the parts with high quality at a low cost. The technologies which enable this are digital twin, big data analytics, autonomous robots, Internet of things, cybersecurity, cloud computing, augmented reality, and additive manufacturing. Thus, interconnected intelligent machines allow autonomous manufacturing using decentralized decision-making systems that cooperate with each other, making the manufacturing process more efficient. Machine maintenance can be categorized into three types, namely, predictive maintenance (supervised), run to failure (semi-supervised), and preventive maintenance (unsupervised). Self-diagnostic machines are an integral part of smart factories. Predictive maintenance is a proactive maintenance strategy that predicts failure. These predictions are based on data gathered through condition monitoring sensors using IoT, analyzed using big data, and predicted using machine learning algorithms. This can lead to major cost savings and increased availability of the systems, thus optimizing performance.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    T. Mohanraj, Jayanthi Yerchuru, R. S. Nithin Aravind & R. Yameni

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  1. T. Mohanraj
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  2. Jayanthi Yerchuru
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  3. R. S. Nithin Aravind
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  4. R. Yameni
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Correspondence to T. Mohanraj .

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Editors and Affiliations

  1. Department of Chemistry and Environmental Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ, USA

    Chaudhery Mustansar Hussain

  2. University of Padova, Padova, Italy

    Paolo Di Sia

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Mohanraj, T., Yerchuru, J., Aravind, R.S.N., Yameni, R. (2021). Machine Learning: An Expert Thinking System. In: Hussain, C.M., Di Sia, P. (eds) Handbook of Smart Materials, Technologies, and Devices. Springer, Cham. https://doi.org/10.1007/978-3-030-58675-1_29-1

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  • DOI: https://doi.org/10.1007/978-3-030-58675-1_29-1

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  • Print ISBN: 978-3-030-58675-1

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