Abstract

The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation based performance demonstration and finally conclusive summary of the developed tool/technique along with future scope.

中文翻译：

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通过人工智能和机器学习的发展实现数字化转型

摘要

数字转换（DT）是获取用于将业务，应用程序，服务转换并将手动过程升级为自动化的数字工具，技术，方法，机制等。DT通过自动化，创新和创造力提高了系统的效率。DT在工程领域的另一个概念是通过自动化代替手动和/或常规过程，以有效方式处理大数据问题，并在不了解系统参数的情况下利用静态/动态系统信息。DT代表了组织中开发人员和/或用户的机遇与挑战，例如针对各种应用（例如，数字孪生，网络安全，状态监视和故障检测与诊断（FDD），预测和预测，智能数据分析，医疗保健监视，特征提取和选择，智能制造和生产，未来城市，先进建筑，弹性基础设施，更大的可持续性等）。此外，由于先进的人工智能，机器学习和数据分析技术的巨大影响，DT的利润在全球范围内得到了提高。因此，将DT集成到所有领域为用户和开发人员都带来了价值。在这篇社论中，我们介绍了不同工程领域的DT的52种不同应用，包括其详细信息，最新技术，方法，拟议的方法开发，