FPGA friendly fault detection technique for drive fed induction motor

Panigrahy, Parth Sarathi; Mitra, Subhrajit; Konar, Pratyay; Chattopadhyay, Paramita

doi:10.1109/ciec.2016.7513787

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…Validation of data-driven algorithm on a device that emulates real-world machinery is truly necessary. So, the experimental investigations were carried out on Machinery Fault Simulator (MFS), a laboratory prototype from Spectra Quest to emulate different IM faults (Konar and Chattopadhyay, 2011;Konar et al, 2015;Panigrahy et al, 2016). One healthy motor and three faulty motors of the identical specification were kept on the testbed individually and stator current was recorded at a sampling frequency of 1.28 kHz for every operational condition.…”

Section: Proposed Scheme and Data Acquisitionmentioning

confidence: 99%

“…In turn, a huge dataset accounting for large acquisition time is required. So, in the present work, timedomain features are extracted from different sub-bands of discrete wavelet transform (DWT)-inverse DWT (IDWT) algorithm (Garcia-Perez et al, 2011;Romero-Troncoso et al, 2011;Panigrahy et al, 2016). DWT is a dyadic process of orthogonal decomposition of the signal information by using an appropriate low-and high-pass filter.…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Decent fault classification of VFD fed induction motor using random forest algorithm

Panigrahy¹,

Santra²,

Chattopadhyay

2020

AIEDAM

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A data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.

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Section: Proposed Scheme and Data Acquisitionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%