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If the belt failure has not been detected early, a sudden shutdown may happen, producing safety and economic consequences. However, most maintenance personnel use their senses of sight, hearing, smell, and touch to identify the cause of the problem while diagnosing a belt drive condition. Hence, this research involves developing an intelligent contamination status detection system based on vibration signal analysis for a pulley-belt rotating system. Time-domain signal analysis was employed to extract some suggestive features such as the root mean square, kurtosis, and skewness from the vibration data. An artificial neural network (ANN) model was built to detect the simulated different operating conditions. The vibration data was gathered with the help of two MEMS accelerometers (ADXL335) interfaced with an NI USB-6009 data acquisition device. A signal capture, analysis, and feature extraction system was developed using Matlab Simulink. The simulated operating conditions include clean, wet, and powder-contaminated belts. The results showed that the designed system could identify the pulley-belt operation conditions with 100% overall accuracy.<\/jats:p>","DOI":"10.3233\/jifs-222438","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T12:37:27Z","timestamp":1691152647000},"page":"6629-6643","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Prediction of the belt drive contamination status based on vibration analysis and artificial neural network"],"prefix":"10.1177","volume":"45","author":[{"given":"Ihsan A.","family":"Baqer","sequence":"first","affiliation":[{"name":"University of Technology-Iraq","place":["Iraq"]}]},{"given":"Alaa Abdulhady","family":"Jaber","sequence":"additional","affiliation":[{"name":"University of Technology-Iraq","place":["Iraq"]}]},{"given":"Wafa A.","family":"Soud","sequence":"additional","affiliation":[{"name":"University of Technology-Iraq","place":["Iraq"]}]}],"member":"179","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2016.2530159"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42835-021-00711-x"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.18517\/ijaseit.9.2.7426"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"SchiewaldtK. 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