Condition Monitoring and Fault Diagnosis of Induction Motor - An Experimental Analysis

2021 ◽  
Author(s):  
Abdelelah Almounajjed ◽  
Ashwin Kumar Sahoo ◽  
Mani Kant Kumar ◽  
Mhamad Waleed Bakro
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Experimental Analysis

scholarly journals Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

2021 ◽  
Vol 11 (1) ◽  
pp. 174-180
Author(s):  
Jyothi R ◽  
◽  
Tejas Holla ◽  
Umesh NS ◽  
K Uma Rao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Industrial Applications ◽  
Voltage Source ◽  
Variable Frequency ◽  
Process Industries ◽  
Ac Drives ◽  
Stator Current ◽  
Variable Frequency Drives

AC drives are employed mainly in process plants for various applications. In most industrial applications, Induction motor drives are preferred as they are robust, reliable, and efficient. Process industries have seen a paradigm shift from manual control to automatic control. Advancements in power electronics technology have led to smooth control of the induction motor using variable frequency drives over an entire speed range. Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. Various electric faults that are incipient in the VFD cause an abrupt change in circuit parameters resulting in insulation damage, reduced efficiency, and leading to catastrophic failure of the entire system. Hence, continuous monitoring of the system parameters such as stator current, speed, and the vibration of the machine is essential to diagnose incipient faults in the system. AI techniques have been effectively used in the fault diagnosis of electrical systems. In the proposed work, simulation results of machine learning-based fault diagnosis techniques are presented. Real-time IoT-based condition monitoring of the Variable Frequency Drive is also implemented for enhanced fault diagnosis of various incipient electrical faults in AC drives. The experimental results obtained are validated with the simulation data.

scholarly journals Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment

2019 ◽  
Vol 6 (2) ◽  
pp. d1-d8 ◽  
Author(s):  
S. Altaf ◽  
M. S. Mehmood ◽  
M. W. Soomro
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Industrial Applications ◽  
Manufacturing Companies ◽  
Signal Processing Technique ◽  
Broken Rotor Bar ◽  
Machine Fault Diagnosis ◽  
Machine Fault

Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.

Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar

2020 ◽  
pp. 1-1
Author(s):  
Zahra Hosseinpoor ◽  
Mohammad Mehdi Arefi ◽  
Roozbeh Razavi-Far ◽  
Niloofar Mozafari ◽  
Saeede Hazbavi
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Virtual Sensors ◽  
Broken Rotor Bar

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199691
Author(s):  
Omar AlShorman ◽  
Fahad Alkahatni ◽  
Mahmoud Masadeh ◽  
Muhammad Irfan ◽  
Adam Glowacz ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Vital Role ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Review Study ◽  
Public Datasets ◽  
Fatigue Detection ◽  
Types Of Faults

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

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