<![CDATA[Electric induction motors (IM) are considered to be a highly significant and extensively utilized category of machinery within contemporary industrial settings. Typically, powerful motors, which are frequently essential to industrial processes, are equipped with integrated condition-monitoring systems to support proactive maintenance and the identification of faults. Typically, the cost-effectiveness of such capabilities is limited for tiny motors with a power output of less than ten horsepower, given their relatively low replacement costs. Nevertheless, it is worth noting that several little motors are commonly employed by large industrial facilities, mostly to operate cooling fans or lubricating pumps that support the functionality of larger machinery. It is possible to allocate multiple small motors to a single electrical circuit, so creating a situation where a malfunction in one motor could potentially cause damage to other motors connected to the same circuit. Hence, there exists a necessity to implement condition monitoring techniques for collections of small motors. This paper presents a comprehensive overview of a continuous effort aimed at the development of a machine learning-driven solution for the identification of faults in a multitude of small electric motors.]]>
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