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