A Predictive Maintenance System Based on Industrial Internet of Things for Multimachine Multiclass Using Deep Neural Network

Among the many applications of Industry 4.0, predictive maintenance is one of the most frequently utilized examples.On the other hand, in order to improve failure categorization, the majority of contemporary machine-learning models require a substantial amount of data.In contrast to traditional maintenance, IIoT systems that perform real-time monitoring can be of tremendous service to the company.These systems can notify the necessary members of the factory's maintenance team in advance of a serious breakdown, which offers a significant advantage.It is of the utmost importance to detect any malfunctions in equipment while they are in operation before they become critical.The purpose of this work is to collect a substantial quantity of data from three AC motors, each of which is equipped with four different kinds of sensors.These sensors include a vibration sensor, a current sensor, a contactless temperature sensor, and an ambient temperature sensor.A variety of motor faults, including normal, vibration, stop, heavy load, and overcurrent, have been purposefully applied to the system in order to build the custom dataset.These motor's faults have been categorized and labeled in accordance with their respective classification responsibilities.A deep neural network (DNN) model consisting of seven layers was utilized.A cloud server is used to train the model, and all of the data from the three AC motors are sent to the cloud server after they have been collected.The result demonstrates that it has good accuracy and loss in both the training and testing phases, with a loss of 0.0014 and an accuracy of 100% while the model has been tested for over and under fitting problems.