Design And Implementation Of A Data Acquisition System For Multi Fault Diagnosis Analysis In Induction Motors

Abstract

This Master's thesis focuses on the design of a data acquisition system for detecting motor faults early and implementing smart maintenance strategies. Understanding and diagnosing faults in induction motors is critical for maintaining the reliability and efficiency of industrial operations. The thesis aims to design a data acquisition system for the early detection of motor faults. By developing a novel data acquisition system and comparing its performance with existing systems, this research aims to provide valuable insights and practical solutions to enhance motor fault diagnosis, ultimately improving operational reliability and reducing downtime in industrial settings. The study begins by introducing the main research topic, discussing its broader context and significance, and emphasizing the need for a reliable data acquisition system for multi-fault diagnosis in induction motors, particularly squirrel cage motors widely used across various industries. A comprehensive study of key motor elements is provided, including their importance and operating principles. Existing methods for motor fault diagnosis and online condition monitoring are covered. The thesis introduces data acquisition systems and reviews various algorithms for their design, including sensors, signal processing techniques, and data analysis methods. The research delves deeper into data acquisition systems, reviewing various algorithms for their design, the selection and placement of sensors, signal processing techniques, and data analysis methods. It investigates the development of an algorithm, flow chart, and block diagram for a data acquisition system to study motor fault data from different load motors in the mineral processing plant. The study details the development and design of the data acquisition system, analyzes and compares the results obtained from different motor loads, and discusses essential aspects based on these results. Furthermore, this research discusses the development and evaluation of a newly designed data acquisition system, the "Motor Analyzer LEVEL V1.0." It compares it with existing systems in the industry, specifically the Baker EXP3000 Explorer Dynamic Motor Analyzer (99-EXP3000-CE). The Fast Fourier Transform (FFT) converts real-time data from the time domain into the frequency domain, facilitating detailed analysis. The "Motor Analyzer LEVEL V1.0" was developed based on this research and was tested against the Baker EXP3000 Explorer. Voltage measurements in the time domain from both systems showed very similar results. However, the horizontal axis scales differed due to variations in sampling rates—1000 Hz for the Motor Analyzer LEVEL V1.0 and 10000 Hz for the Baker EXP3000 Explorer. The frequency domain analysis also demonstrated substantial similarity between the two systems, with a validation accuracy of nearly 98% for both current and voltage measurements in both time and frequency domains. 6 This research marks the first development of the Motor Analyzer LEVEL V1.0 in Mongolia is contributing significantly to advancements in local industrial technology. The thesis summarizes these findings, presents conclusions, and offers further recommendations for future research to enhance industrial operations' reliability and efficiency.