Rough set based multi-class fault diagnosis of induction motor using Hilbert Transform

2012 ◽  
Author(s):  
Pratyay Konar ◽  
Shekhar Bhawal ◽  
Moumita Saha ◽  
Jaya Sil ◽  
Paramita Chattopadhyay
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Rough Set ◽  
Hilbert Transform

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.

Study on Remote Control and Fault Diagnosis for Ultrahigh Speed Grinding

2007 ◽  
Vol 359-360 ◽  
pp. 518-522
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu ◽  
Xing Yu Jiang ◽  
Jian Yu Yang
Keyword(s):  
Remote Control ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Rough Set ◽  
Theoretical Research ◽  
Reduction Rule ◽  
Diagnosis System ◽  
Linear Speed ◽  
Fault Diagnosis System ◽  
Diagnosis Model

Remote control and fault diagnosis of ultrahigh speeding grinding is studied, which is based on the theory of rough set. Knowledge acquisition and reduction rule of fault diagnosis, realization method of remote control for ultrahigh speed grinding are studied, diagnosis model is established. Based on the theoretical research and ultrahigh speed grinder with a linear speed of 250 m/s, the remote control and fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running show that the environment is improved, the mental pressure of workers is relieved and the efficiency is improved. At the same time, it proves that the ability to diagnosis and the accuracy of diagnosis for the ultrahigh speed grinding are improved and the time for diagnosis is shortened by applying rough set.

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