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All Journal Indonesian Journal of Electrical Engineering and Computer Science Spektrum Industri
Santanu Das
Indian Institute of Information Technology, Allahabad
Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Machine learning based stator-winding fault severity detection in induction motors Mishra, Partha; Sarkar, Shubhasish; Chowdhury, Sandip Saha; Das, Santanu
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp182-192

Abstract

Approximately 35% of all induction motor defects are caused by stator inter-turn faults. In this paper a novel algorithm has been proposed to analyze the three-phase stator current signals captured from the motor while it is in operation. The suggested method seeks to identify stator inter-turn short circuit faults in early stage and take the appropriate action to prevent the motor's condition from getting worse. Three-phase current signals have been captured under healthy and faulty conditions of the motor. Involving discrete wavelet transform (DWT) based decomposition followed by reconstruction using inverse DWT (IDWT), 50 Hz fundamental component has been removed from the captured raw current signals. Subsequently, from each phase current 15 statistical parameters have been retrieved. The statistical parameters include mean, standard deviation, skewness, kurtosis, peak-to-peak, root mean square (RMS), energy, crest factor, form factor, impulse factor, and margin factor. At the end, a standard machine learning algorithm namely error correcting output codes-support vector machine (ECOC-SVM) has been employed to classify six different severity of stator winding faults. The proposed fault diagnosis method is load and motor-rating independent.
Investigating The Effect of Nickel Buttering on Corrosion Resistance Das, Santanu; Saha, Manas Kumar
Spektrum Industri Vol. 20 No. 2 (2022): Spektrum Industri - October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/si.v20i2.23

Abstract

Cladding is usually employed for improving corrosion and/or abrasion resistance of a component. Buttering layer between clad layer and structural steel is also sometimes provided to promote bonding between the cladding and the substrate. The buttering layer may also give desired corrosion resistance. Nickel is known to be a common austenite stabilizer, and it is often put inside cladding, coating, buttering, etc. to enhance hardness and corrosion protection of steel. In the current investigation, 316 austenitic stainless steel is deposited over nickel plated medium carbon steel substrate with the help of metal active gas welding. Nickel is accumulated on medium carbon steel by electroplating to act as a buttering layer. Welding current and torch travel speed, two important process parameters are varied with constant arc voltage to develop different values of heat input. Accelerated corrosion test in chloride atmosphere is done on ground and polished clad surface. Corrosion rate is observed to be decreased significantly for all the clad samples relative to the base material. However, no clear trend of corrosion rate against increment in heat input is found for reporting, but corrosion rate shows a typical pattern with the variation of heat input.
Co-Authors Chowdhury, Sandip Saha Mishra, Partha Saha, Manas Kumar Sarkar, Shubhasish