Abstract
Owing to their excellent exploitation properties, induction motors are of key importance to industrial systems. Therefore, early fault detection in induction motors has recently received increasing attention, encompassing a number of modern technologies such as Internet of Things and Cloud Computing. In this paper, an example of fault detection system will be presented. The system detects a broken rotor bar of induction motors, employing conventional vibration analysis techniques and the Radial Basis Function (RBF) neural network enhanced by the Microsoft Azure cloud platform.
Highlights
Induction motors play an important role in the contemporary industry
The system detects a broken rotor bar of induction motors, employing conventional vibration analysis techniques and the Radial Basis Function (RBF) neural network enhanced by the Microsoft Azure cloud platform
This paper describes an example of decentralized system for fault detection in induction motors based on the Microsoft Azure cloud platform
Summary
Induction motors play an important role in the contemporary industry. They are widely used due to a large number of favorable features such as low price, reliability, rugged construction and low maintenance costs. Sudden faults cause economic losses and affect work safety in industrial plants. Early fault detection in induction motors has been discussed numerous researches over the years (HernandezVargas et al, 2014). The Internet of Things (IoT) offers an opportunity to develop an innovative and efficient system for fault detection in induction motors. The Internet of Things represents a network of physical devices, sensors, actuators, software and connectivity which enables these objects to connect and exchange data. The application of the IoT to the manufacturing industry is called the Industrial Internet of Things (IIoT)
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