scholarly journals Investigation and development of a real-time on-site condition monitoring system for induction motors

2007 ◽  
Author(s):  
S. Bakhri ◽  
N. Ertugrul ◽  
W.L. Soong ◽  
S. Al-Sarawi
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Induction Motors ◽  
Site Condition ◽  
Condition Monitoring System

Online Real-Time Monitoring System Through Using Adaptive Angular-Velocity VKF Order Tracking

2012 ◽  
Author(s):  
Ting-Chi Yeh ◽  
Min-Chun Pan
Keyword(s):  
Angular Velocity ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Theoretical Derivation ◽  
Periodic Loading ◽  
Order Tracking ◽  
Computation Scheme ◽  
Condition Monitoring System ◽  
Operation Status

When rotary machines are running, acousto-mechanical signals acquired from the machines are able to reveal their operation status and machine conditions. Mechanical systems under periodic loading due to rotary operation usually respond in measurements with a superposition of sinusoids whose frequencies are integer (or fractional integer) multiples of the reference shaft speed. In this study we built an online real-time machine condition monitoring system based on the adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm, which was implemented through a DSP chip module and a user interface coded by the LabVIEW®. This paper briefly introduces the theoretical derivation and numerical implementation of computation scheme. Experimental works justify the effectiveness of applying the developed online real-time condition monitoring system. They are the detection of startup on the fluid-induced instability, whirl, performed by using a journal-bearing rotor test rig.

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

On-Board Condition Monitoring System for Freight Trains

2001 ◽  
Author(s):  
John Donelson ◽  
Wayne M. Zavis ◽  
S. K. (John) Punwani ◽  
Monique Ferguson Stewart ◽  
Mark C. Edwards
Keyword(s):  
Research And Development ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Continuous Monitoring ◽  
Operation Efficiency ◽  
International Corporation ◽  
Mechanical Components ◽  
Condition Monitoring System ◽  
Railroad Safety

Abstract Science Applications International Corporation (SAIC) and Wilcoxon Research have developed a real-time on-board condition monitoring system for freight trains. The Office of Research and Development of the Federal Railroad Administration funded the development of the system. The system monitors bearings, wheels, trucks and brakes on freight trains in order to detect equipment defects and derailments. The objectives of the system are to improve railroad safety and operation efficiency through continuous monitoring of mechanical components on freight trains.

A Real-Time Condition Monitoring System for a Machine Tool Spindle

2010 ◽  
Vol 450 ◽  
pp. 259-262 ◽  
Author(s):  
Chao Ching Ho ◽  
Tzu Hsin Kuo ◽  
Tsung Ting Tsai
Keyword(s):  
Machine Tool ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Vibration Monitoring ◽  
Sensor System ◽  
External Information ◽  
Processing Quality ◽  
Machine Tool Spindle ◽  
Condition Monitoring System

Designing a robust condition monitoring system for a machine tool spindle is an important task because the spindle has a significant effect on the processing quality. In this study, a solar-powered wireless sensor system is installed inside the spindle and is used to monitor the machine tool processing state in real time, thereby improving the processing quality. Accelerometer sensors are employed to estimate tool wear; these sensors monitor the vibration of the spindle. The vibration monitoring data of the high-speed spindle is wirelessly transmitted to an external information device in real time. As an alternative to sensors that employ wired power transmission, a solar energy transmission system has been developed to provide the required electric power to the sensor system. The experimental results show that the proposed system successfully measures the vibration frequency of the rotational machine tool spindle.

Sign in / Sign up

Export Citation Format

Share Document