A CYBER-PHYSICAL SYSTEM BASED REAL-TIME FAULT DIAGNOSIS OF INDUCTION MOTORS

Induction motors are one of the major electrical prime movers in industrial sectors. Since these motors are operated continuously, they are subjected to wear and tear which lead to faults at a later stage in its life. These faults which arise can be classified into 5 major categories i.e., broken rotor bars, stator winding faults, air-gap eccentricity, bearing faults, and torque fluctuations. A failure in induction motors leads to machine downtime, increased maintenance costs, and puts the lives of the plant personnel at risk, thus leading to undesirable consequences. Hence, uninterrupted operation of the machine is the need of the hour for which real-time condition-based monitoring of induction motors needs to be implemented. Industries are making an attempt to tap into the technology that involves around cyber-physical systems (CPS) and access real-time information regarding the motor health condition. The present article explores the CPS structure for real-time fault identification so that appropriate action can be taken by plant personnel. The CPS technology is a modular framework, which consists of a current sensor that transmits data to a remote minicomputer (e.g., Intel NUC kit) or a microcontroller (e.g., Raspberry Pi) by processing it through a data acquisition (DAQ) system across a wireless network. Since the range of defect frequencies for fault diagnosis in these induction motors is 5 kHz, Nyquist sampling frequency (𝐹𝑠) for data acquisition should at least be 10 kHz. It is to be noted that a microcontroller can be of low cost; however, maintaining 𝐹𝑠 more than 500 Hz tends to cause random jitters at the core of the operating system (OS). As a result, the signal-to-noise ratio (SNR) is compromised in microcontrollers leading to incorrect post-processing of the current time-stamp data for motor fault diagnosis. Hence, in the present article, a minicomputer is used for data acquisition of current time data at 𝐹𝑠 of 10 kHz and infer the motor health status by investigating the current spectrum. The information of motor health condition is stored in comma-separated values (CSV) file, which is further transferred over Google Cloud Storage (GCS) via hypertext transfer protocol (HTTP) with transport-layer security (TLS) encryption. HTTP converts the CSV data file into binary format and maintains the record of meta-data of the files. Meta-data essentially keeps track of when the file was created in the remote minicomputer. Additionally, in order to ensure a high data transfer rate at a given instant of time, the HTTP file transfer protocol divides the actual data into small chunks that are subjected to parallel composite uploads. When the data is collected in the computer at the receiver’s end i.e., the plant personnel in the present case, the data is recreated back to the original CSV file. As a result, the concerned plant personnel has complete information about the specific motor which has started failing and prevents any major breakdown of the machine. Thus, the fault detection of the motors at the incipient stage through CPS technology helps in developing an effective process that aids in the smooth functioning of the machines.