Industrial Machine Troubleshooting Diagnosis Annunciator Using IoT

Abstract

There is always a need to provide safety to industrial electrical machines. Some of the machines are very expensive and critical. Their downtime causes substantial economic losses to industries. Thus, health should constantly be monitored for such costly and vital loads. This paper proposes an Industrial Machine Troubleshooting Diagnosis Annunciator (IMTDA) system utilizing the Internet of Things (IoT) for real-time monitoring and fault diagnosis of industrial machines. The IMTDA leverages the ThinkSpeak application to establish a connection between the machine and a cloud platform. Various sensors monitor critical parameters like voltage, current, overload, overcurrent, and short circuits. The collected data is transmitted to ThinkSpeak, enabling visualization of fault trends over a day through graphical representations. Additionally, the system provides real-time data accessibility on mobile devices via the ThinkSpeak application, facilitating prompt identification and rectification of machine malfunctions. This approach empowers proactive maintenance strategies, minimizing downtime and enhancing industrial process efficiency. The data available in the IoT platform tells about the type of fault causes of the fault and the frequency of the faults that occurred on the machines. Further, this data or information can be utilized by the maintenance units in the industry, and hence, they can take necessary remedial actions over different faults. The results available over the IoT platform are given in the paper. Coordination of hardware and IoT (ThinkSpeak) platform is discussed in detail.