<![CDATA[Operational reliability in the modern manufacturing sector is highly dependent on the performance of induction motors as the main drive [1]. This dependency poses a significant risk if maintenance is still reactive, as sudden failures can trigger significant financial losses. As a solution, the MachinaSense Predictive Engine Guardian system is designed using ESP32-based Internet of Things (IoT) technology to continuously monitor machine conditions [2]. This system integrates four critical sensor parameters to detect anomalies early. [3]. Meanwhile, the vibration sensor plays a role in detecting mechanical disturbances such as bearing wear through vibration frequency analysis [5]. To prevent insulation damage due to heat, a temperature sensor is used to monitor the condition of the stator [4], while the rotational speed is monitored through an RPM monitoring system to analyze the motor slip value according to the fundamental theory of electrical machines [6]. All sensor data is acquired using I2C and UART communication protocols and then sent to a cloud platform via a WiFi network [4]. This multi-sensor integration has been proven to be able to identify operational deviations before they exceed critical thresholds, thus providing a much more accurate diagnosis than single-sensor methods. The application of this technology not only minimizes downtime, but also becomes a concrete step in the transformation towards Industry 4.0 which prioritizes data-based predictive maintenance [7].]]>
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