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The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.<\/jats:p>","DOI":"10.3390\/s24082653","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T10:10:18Z","timestamp":1713780618000},"page":"2653","source":"Crossref","is-referenced-by-count":1,"title":["FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0868-2918","authenticated-orcid":false,"given":"Roque Alfredo","family":"Osornio-Rios","sequence":"first","affiliation":[{"name":"Cuerpo Acad\u00e9mico (CA) Mecatr\u00f3nica, Facultad de Ingenier\u00eda, Campus San Juan del R\u00edo, Universidad Aut\u00f3noma de Quer\u00e9taro, Av. 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