A Novel Intelligent Train Condition Monitoring System Coupling Laser Beam into Image Processing Algorithm

2006 ◽  
Vol 13 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Lee Kang Kuen ◽  
Tony K Y Lee ◽  
S L Ho ◽  
Eddie W K Cheng
Keyword(s):  
Image Processing ◽  
Laser Beam ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Coupling Laser ◽  
Condition Monitoring System ◽  
System Coupling

scholarly journals An Image-Processing Method for Extracting Kinematic Characteristics of Droplets during Pulsed GMAW

2019 ◽  
Vol 9 (24) ◽  
pp. 5481 ◽  
Author(s):  
Peizhuo Zhai ◽  
Songbai Xue ◽  
Tao Chen ◽  
Jianhao Wang ◽  
Yu Tao
Keyword(s):  
Image Processing ◽  
Monitoring System ◽  
High Speed ◽  
Gas Metal Arc Welding ◽  
Processing Algorithm ◽  
Automatic Welding ◽  
Threshold Method ◽  
Image Processing Algorithm ◽  
Kinematic Characteristics ◽  
Metal Arc Welding

Pulsed gas metal arc welding (GMAW) is widely applied in industrial manufacturing. The use of pulsed GMAW was found superior to the traditional direct-current (DC) welding method with respect to spatter, welding performance, and adaptability of all-position welding. These features are closely related to the special pulsed projected metal transfer process. In this paper, a monitoring system based on a high-speed camera and laser backlight is proposed. High-quality images with clear droplets and a translucent arc can be obtained at the same time. Furthermore, a novel image-processing algorithm is proposed in this paper, which was successfully applied to remove the interference of the arc. As a result, the edge and region of droplets were precisely extracted, which is not possible using only the threshold method. Based on the algorithm, centroid coordinates of undetached and detached droplets can be calculated, and more parameters of the kinematic characteristics of droplets can be derived, such as velocity, acceleration, external force, and momentum. The proposed monitoring system and image-processing algorithm give a simple and feasible way to investigate kinematic characteristics, which can provide a new method for possible applications in studying mathematic descriptions of droplet flight trajectory and developing a precise automatic welding 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.

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