{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T05:22:59Z","timestamp":1767676979101,"version":"3.40.5"},"reference-count":49,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,12,15]]},"abstract":"<jats:p>In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, reduce maintenance cost and increase plant availability. Induction motors are the most sought-after prime movers in modern-day industries due to their robustness. Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. This paper presents the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based system to diagnose the vibration and Instantaneous Power (IP)-based responses of rolling element bearings and broken rotor bars in an induction motor. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the size of input features and finding the most optimal feature set. A comparative analysis of the effectiveness of SVM and ANN is carried out using statistical parameters extracted from vibration and IP signals. The highest accuracy of 92.5% and 98.2% was achieved for vibration and IP signatures, respectively, using the proposed SFFS-based feature selection technique and ANN classification method. The results reveal that ANN has better performance than SVM and the proposed strategy can be used for automatic recognition of machine faults. The use of this type of intelligent system helps in avoiding unwanted and unplanned system shutdowns due to the failure of the motor.<\/jats:p>","DOI":"10.1142\/s0218001420590430","type":"journal-article","created":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T10:41:36Z","timestamp":1581936096000},"page":"2059043","source":"Crossref","is-referenced-by-count":28,"title":["Artificial Intelligence-Based Fault Diagnosis for Condition Monitoring of Electric Motors"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4614-0044","authenticated-orcid":false,"given":"Amandeep","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lini","family":"Mathew","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shantanu","family":"Chatterji","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepam","family":"Goyal","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,5,11]]},"reference":[{"key":"S0218001420590430BIB001","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural Networks for Pattern Recognition","author":"Bishop C. 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