An industrial internet of things application for real-time condition monitoring

In recent years, the production model has become widespread around the world. Smart technology and modern automation tools are increasingly changing the face of production and industry. The use of innovative maintenance techniques contributes to production by reducing disruptions and sudden stops in industry. The achievable production costs by reducing downtime and increasing maintenance performance is the most critical task. It can achieve these goals with the widespread use of low-cost smart devices that can provide a comprehensive online view of equipment operating conditions. For these reasons, an Internet of Things-based method is proposed for monitoring induction motors in this thesis. These devices analyze the vibration signals of the motor to provide conditional and continuous control of the tolerance of the motor after a fault. As a result of artificial intelligence, production and algorithms, it will be possible to remotely monitor the state of the motor using the methods of remote clouds of the Internet of things. The sound or vibration sensors that evaluate the stability of the machine help to reduce breakdowns and faults by issuing warnings and predicting whether the machine is stable, by informing monitoring centers before any fault occurs to the motor. For this purpose, it will be possible to measure vibration signals from the induction motors and make condition analysis remotely. The proposed approach is based on measuring vibration signals with a remote sensing kit and identifying faults based on artificial intelligence. The approach consists of two methods. The first method is based on the detection of shaft imbalances based on machine learning with vibration signals measured with the kit. For this purpose, the performance of different machine learning methods was compared. The second approach is to evaluate the diagnostic performance of three different one-dimensional convolutional neural networks on motor vibration signals. Obtained results show that methods can be used to detect motor faults in real environment and multiple motors can be easily monitored remotely.