Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.

工业4.0是新的工业革命。通过将每台机器和活动通过网络传感器连接到Internet，将生成大量数据。机器学习（ML）和深度学习（DL）是人工智能（AI）的两个子集，用于评估生成的数据并产生有关制造企业的有价值的信息，同时并行引入工业AI（IAI）。通过强调工业4.0的功能，要求和挑战，本文重点介绍了工业4.0的原理。此外，提出了一种新的AIA架构。此外，还详细介绍了工业4.0中使用的最重要的ML和DL算法。讨论并评估了每种算法的功能，应用和效率。然后，我们专注于最重要的工业4.0领域之一，即智能电网，其中根据智能电网应用的效率和有效性介绍和分析了ML和DL模型。最后，重点介绍并讨论了新工业时代背景下数据分析领域的趋势和挑战，例如可伸缩性，网络安全和大数据。