학술논문

Fault Analysis of An Induction Motors Using AI and ML: An Efficient Way
Document Type
Conference
Source
2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) Advance Computing and Innovative Technologies in Engineering (ICACITE), 2023 3rd International Conference on. :1424-1430 May, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Vibrations
Induction motors
Electric breakdown
Surveillance
Metaheuristics
Rotors
Rolling bearings
Induction Machine
Machine Learning
Artificial Intelligence
Fault Diagnosis
Language
Abstract
The industry is becoming more and more focused on boosting the energy efficiency and lowering the operating expenses of induction motors. Frequent surveillance of the machine's status using monitoring systems that can discover breakdowns at an early stage may greatly minimize these expenses and enhance the motor's efficiency. Expensive shutdowns, unexpected downtime, breakdowns, and workplace accidents may be avoided by early defect identification. Motor losses and service life may be minimized by preventing and treating early signs of failure. Numerous review articles provide in-depth looks at certain defect detection methods for induction motors. All the methods discussed here can, however, only spot problems in their more advanced forms. We were unable to find any review that compares different methods for finding problems in their early phases of development. In this study, we examine existing methods and tools for early defect detection. The armature, rotor, and rolling bearings of a motor are the primary targets of this examination of current approaches. Methodologies are described, and suggestions for further study in this field are offered, for both steady-state and transient motor operation conditions.