The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.

中文翻译：

---

异步电动机滚动轴承状态监测与故障诊断的人工智能方法综述

故障检测和诊断（FDD）以及状态监视（CM）和旋转机械（RM）对于早期诊断至关重要，以防止工业环境中的基础设施受到严重破坏。重要的是，需要对有价值的工业设备进行连续监控，以提高安全性，可靠性和可用性，并降低现代工业系统和应用程序的维护成本。但是，感应电动机（IM）便宜，可靠且坚固耐用，因此已在多个工业过程中广泛使用。滚动轴承被认为是IM的主要组成部分。毫无疑问，此基本组件的任何故障都可能导致IM以及整个工业系统的严重故障。从而，目前许多基于不同技术的方法被用作IM滚动轴承的故障预测和诊断。而且，这些技术包括信号/图像处理，智能诊断，数据融合，数据挖掘以及用于时间和频率以及时频域的专家系统。人工智能（AI）技术已在数字技术的每个领域证明了其重要性。工业机器，自动化和流程是AI适应的净前沿。有相当成熟的文献已经使用信号和数据处理技术来解决这个问题。但是，这项工作的主要贡献是基于人工智能（AI）方法对IM的CM和FDD进行了广泛的综述，特别是对于滚动轴承而言。这项研究突出了每种方法的优点和性能局限性。最后，还将重点介绍挑战和未来趋势。