2026-04-18T23:06:18Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/92932024-06-20T00:05:23Zcom_10259_8983com_10259_5086com_10259_2604com_10259_7509com_10259_4266com_10259.4_106col_10259_8984col_10259_7510

Gómez Gil, Francisco Javier Martínez-Martínez, Víctor Ruiz González, Rubén Martínez Martínez, Lidia Gómez Gil, Jaime 2024-06-19T12:24:22Z 2024-06-19T12:24:22Z 2024-02 0168-1699 http://hdl.handle.net/10259/9293 10.1016/j.compag.2023.108556 Monitoring the status of rotating components is important in modern machinery. The goal of this study is to evaluate the feasibility of using a k-Nearest Neighbors (kNN) classifier combined with a Harmony Search (HS) algorithm, to detect the operational status of rotating components within agricultural machines. Vibration data, the source data, were acquired from four accelerometers located along the chassis of a harvester. Five operational statuses of three rotating components of the harvester were studied: engine (low/maximum speed), thresher, and chopper (on/off and balanced/unbalanced). The methodology includes vibration signal acquisition, data preprocessing, smoothing, preselection of frequencies, Brute Force (BF) and Harmony Search frequency selection, and classification with kNN. The input frequencies for the classifier were chosen with either BF search or HS. The main results of the study were: i) the preselection of frequencies reduced the training time between 92.2% and 95.6%; ii) the smoothing stage improved accuracy; iii) HS reduced the training time between 82% and 90% in comparison with BF, reaching accuracies of nearly 100% in the five operational statuses with only 2 input frequencies; iv) similar levels of accuracy were obtained when using data from the accelerometers at different locations. The results suggested that it was feasible to predict the operational status of rotating components of agricultural machines using a kNN classifier with the combination of preselection, smoothing, and the HS algorithm. This feasibility was achieved both in terms of accuracy and computational burden, building upon previously proposed methods. application/pdf eng Elsevier Computers and Electronics in Agriculture. 2024, V. 217, 108556 https://doi.org/10.1016/j.compag.2023.108556 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess k-Nearest Neighbors (kNN) Harmony Search (HS) algorithm Vibrations Accelerometer Machinery monitoring Rotating components Frequency selection Ingeniería mecánica Mechanical engineering Vibration-based monitoring of agro-industrial machinery using a k-Nearest Neighbors (kNN) classifier with a Harmony Search (HS) frequency selector algorithm info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Computers and Electronics in Agriculture 217