A real time study on condition monitoring of distribution transformer using thermal imager

Mariprasath, T.; Kirubakaran, V.

doi:10.1016/j.infrared.2018.02.009

Cited by 53 publications

(14 citation statements)

References 29 publications

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“…The next method of fault detection is thermal analysis. Thermal analysis methods are described in [23,24,25]. Temperature detection can be performed using thermal imaging cameras, infrared thermometers and portable laser thermometers.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Głowacz

2019

Sensors

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Increasing demand for higher safety of motors can be noticed in recent years. Developing of new fault detection techniques is related with higher safety of motors. This paper presents fault detection technique of an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. The EID, CG-A, and CG-B use commutator motors. Measurement of acoustic signals of the EID, CG-A, and CG-B was carried out using a microphone. Five signals of the EID are analysed: healthy, with 15 broken rotor blades (faulty fan), with a bent spring, with a shifted brush (motor off), with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy, with a heavily damaged rear sliding bearing, with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy, with a light damaged rear sliding bearing, motor off. Methods such as: Root Mean Square (RMS), MSAF-17-MULTIEXPANDED-FILTER-14 are used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 method is also developed and described in the paper. Classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis is carried out. The results of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 are very good (total efficiency of recognition of all classes—TED = 96%, TECG-A = 97%, TECG-B = 100%).

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Section: Introductionmentioning

confidence: 99%

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Głowacz

2019

Sensors

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“…Short time of PD will not produce much defects, but through longer constant appearing time it can have dangerous effect beginning with degradation of insulation till total failure of device or system. Physical and electrical characteristics of PD mostly depends on diversity of PD occurrence and on material and characteristics of equipment [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge Detection Technique in High Voltage Power Component: A Review

Rohani

Zaidi

Sakanovic

et al. 2019

IJIE

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In this study, a review on categorization of partial discharge (PD) detection technique is presented. The PD detection is important in order to monitor and diagnosis the insulation health on high voltage (HV) power component. PD at high voltage transformer occurs due to the unscheduled maintenance, aging of equipment, breakdown of insulation, gas bubbles in insulation liquid, manufacturing error and etc. In order to maintain constant and high as possible of the transformer performance, it is essential to control, detect and measure the PD phenomena. There are many methods have been developed based on point of observation; optical, chemical, acoustic and electrical. However, the different methods have their own advantages and disadvantages.

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“…To this end, different monitoring and fault detection methods are proposed for analysis and monitoring in recent years. Measuring temperature [1][2][3][4], oil dissolved gas analysis (DGA) [5][6][7], dielectric response analysis [8,9], vibration analysis [10][11][12], partial discharge analysis (electric and acoustic) [2,13] and measuring transfer function [14][15][16] are among these methods where each one has its own disadvantages in fault detection. Detection of internal faults of the transformer through the oil DGA has attracted attention.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis and clustering of power transformer winding fault types by cross‐correlation and clustering analysis of FRA results

Abbasi

Mahmoudi

Avazzadeh

2018

IET Generation, Transmission & Distribution

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A real time study on condition monitoring of distribution transformer using thermal imager

Cited by 53 publications

References 29 publications

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Classification of Partial Discharge Detection Technique in High Voltage Power Component: A Review

Diagnosis and clustering of power transformer winding fault types by cross‐correlation and clustering analysis of FRA results

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