scholarly journals The Correlation between Surface Tracking and Partial Discharge Characteristics on Pressboard Surface Immersed in MIDEL eN

2017 ◽  
Vol 7 (2) ◽  
pp. 631 ◽  
Author(s):  
Nur Amirah Othman ◽  
Hidayat Zainuddin ◽  
Aminuddin Aman ◽  
Sharin Abd Ghani ◽  
Imran Sutan Chairul
Keyword(s):  
Surface Layer ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Partial Discharge ◽  
Experimental Results ◽  
Surface Discharge ◽  
Discharge Characteristics ◽  
Surface Tracking ◽  
Tracking Process ◽  
Condition Monitoring System

This paper presents the investigation of the surface tracking on pressboard surface immersed in MIDEL eN oil.  In this work, the development of surface discharge was analyzed by correlating the visual records of surface tracking on impregnated pressboard and the partial discharge (PD) activities.  The PD activities during the surface tracking process were analyzed in terms of Phase Resolved Partial Discharge (PRPD) patterns.  Throughout the experiment, surface discharge is found as the development of tree-like patterns in the form of white marks occurring on the oil-pressboard interface.  This phenomenon is generally accepted as the drying out process that involves evaporation and decomposition of the oil molecules in the pressboard pores due to the surface discharge activities on the pressboard surface layer.  The development of surface discharge on the pressboard surface can continue from minutes to months or even years until failure.  Thus, condition monitoring system is important to characterize this type of faulty condition.  The experimental results show that there is the decreasing trend of PD magnitude during the development of white mark hallway of a gap distance which is eventually suffered from an unexpected fault.

On-Board Road Condition Monitoring System Using Slip-Based Tire-Road Friction Estimation and Wheel Speed Signal Analysis

2006 ◽  
Author(s):  
Kang Li ◽  
James A. Misener ◽  
Karl Hedrick
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Wheel Speed ◽  
Road Surface ◽  
Experimental Results ◽  
Probe Vehicle ◽  
Road Surfaces ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Condition Monitoring System

This paper presents an on-board road condition monitoring system developed for the safety application in Vehicle Infrastructure Integration (VII) project. The system equipped on the so-called probe vehicle is able to continuously evaluate road surface in terms of slipperiness and coarseness. Road surface is classified into four grades using stock mobile sensors and GPS speed-based measurements. The task of distinguishing slippery extents of road surfaces was treated as a "pattern-recognition" problem based on experimental results such that road surfaces can be classified into three slip levels, normal (μmax ≥0.5), slippery (0.3≥μmax <0.5), and very slippery (μmax <0.3) provided enough excitation. To distinguish rough road surfaces like gravel roads from normal asphalt roads, a separate classifier making use of a filterbank for analyzing wheel speed signal was implemented. Experimental results demonstrate the feasibility of this road condition monitoring system for detecting slippery and rough road surfaces in close to real-time. Once a slippery road condition is detected by the probe vehicle, a warning message with accurate GPS position can be transmitted from the probe vehicle to road side equipment (RSE) and further be relayed to following vehicles as well as traffic management center (TMC) via Dedicated Short Range Communication (DSRC); hence the safety of road users can be improved with the aid of this cooperative or VII active safety 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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