PMID- 35630130
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 2072-666X (Print)
IS  - 2072-666X (Electronic)
IS  - 2072-666X (Linking)
VI  - 13
IP  - 5
DP  - 2022 Apr 23
TI  - FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of 
      Induction Motor Drives.
LID - 10.3390/mi13050663 [doi]
LID - 663
AB  - In modern industrial manufacturing processes, induction motors are broadly 
      utilized as industrial drives. Online condition monitoring and diagnosis of 
      faults that occur inside and/or outside of the Induction Motor Drive (IMD) system 
      make the motor highly reliable, helping to avoid unscheduled downtimes, which 
      cause more revenue loss and disruption of production. This can be achieved only 
      when the irregularities produced because of the faults are sensed at the moment 
      they occur and diagnosed quickly so that suitable actions to protect the 
      equipment can be taken. This requires intelligent control with a high-performance 
      scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic 
      implementation with a Back Propagation Neural network (BPN) is suggested in this 
      article to diagnose the fault more efficiently and almost instantly. It is 
      reported that the classification of the neural network will provide the output 
      within 2 micros although the clone procedure with microcontroller requires 7 ms. This 
      intelligent control with a high-performance technique is applied to the IMD fed 
      by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach 
      was simulated and experimentally validated.
FAU - Rajeswaran, Nagalingam
AU  - Rajeswaran N
AUID- ORCID: 0000-0002-3303-9206
AD  - Electrical and Electronics Engineering, Malla Reddy Engineering College, 
      Secunderabad 500100, India.
FAU - Thangaraj, Rajesh
AU  - Thangaraj R
AD  - Electrical and Electronics Engineering, Malla Reddy Engineering College, 
      Secunderabad 500100, India.
FAU - Mihet-Popa, Lucian
AU  - Mihet-Popa L
AUID- ORCID: 0000-0002-4556-2774
AD  - Faculty of Information Technology, Engineering, and Economics, Oestfold 
      University College, 1757 Halden, Norway.
FAU - Krishna Vajjala, Kesava Vamsi
AU  - Krishna Vajjala KV
AD  - Department of Physics, Malla Reddy Engineering College, Secunderabad 500100, 
      India.
FAU - Ozer, Ozen
AU  - Ozer O
AUID- ORCID: 0000-0001-6476-0664
AD  - Department of Mathematics, Faculty of Science and Arts, Kirklareli University, 
      Kirklareli 39100, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20220423
PL  - Switzerland
TA  - Micromachines (Basel)
JT  - Micromachines
JID - 101640903
PMC - PMC9146959
OTO - NOTNLM
OT  - Back Propagation Neural Network
OT  - Discrete Wavelet Transforms
OT  - FPGA
OT  - Induction Motor Drive
OT  - condition monitoring
OT  - fault diagnosis
COIS- The authors declare no conflict of interest.
EDAT- 2022/05/29 06:00
MHDA- 2022/05/29 06:01
PMCR- 2022/04/23
CRDT- 2022/05/28 01:31
PHST- 2022/03/22 00:00 [received]
PHST- 2022/04/18 00:00 [revised]
PHST- 2022/04/21 00:00 [accepted]
PHST- 2022/05/28 01:31 [entrez]
PHST- 2022/05/29 06:00 [pubmed]
PHST- 2022/05/29 06:01 [medline]
PHST- 2022/04/23 00:00 [pmc-release]
AID - mi13050663 [pii]
AID - micromachines-13-00663 [pii]
AID - 10.3390/mi13050663 [doi]
PST - epublish
SO  - Micromachines (Basel). 2022 Apr 23;13(5):663. doi: 10.3390/mi13050663.