caa a22 4500 60615891X CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00521-014-1698-6 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1698-6 Condition diagnosis of multiple bearings using adaptive operator probabilities in genetic algorithms and back propagation neural networks [Elektronische Daten] [Lili Wulandhari, Antoni Wibowo, Mohammad Desa] Condition diagnosis of bearings is one of the most common plant maintenance activities in manufacturing industries. It is essential to detect bearing faults early to avoid unexpected breakdown of plant due to undetected faulty bearings. Many meta-heuristics techniques for condition diagnosis of single bearing systems have been developed. The techniques, however, are not effectively applicable for multiple bearing systems. In this paper, a new hybrid technique of genetic algorithms (GAs) with adaptive operator probabilities (AGAs) and back propagation neural networks (BPNNs), called AGAs-BPNNs, is proposed specifically for condition diagnosis of multiple bearing systems. In this technique, AGAs are integrated with BPNNs to attain better initial weights for the BPNNs and hence reduce their learning time. We tested the proposed technique on a two bearing systems, and used ten extracted features from the system's vibration signals data as input and sixteen bearing condition classes as target output. The experimental results show that the AGAs-BPNNs technique obtains much higher classification accuracy in shorter CPU time and number of iterations compared with the standard BPNNs, and the hybrid of standard GAs and BPNNs. The Natural Computing Applications Forum, 2014 Genetic algorithms nationallicence Back propagation neural networks nationallicence Condition diagnosis nationallicence Adaptive operator probabilities nationallicence Multiple bearings nationallicence Wulandhari Lili Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Baharu, Malaysia aut Wibowo Antoni Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Baharu, Malaysia aut Desa Mohammad Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Baharu, Malaysia aut Neural Computing and Applications Springer London 26/1(2015-01-01), 57-65 0941-0643 26:1<57 2015 26 521 https://doi.org/10.1007/s00521-014-1698-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1698-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wulandhari Lili Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Baharu, Malaysia aut NATIONALLICENCE 700 1- Wibowo Antoni Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Baharu, Malaysia aut NATIONALLICENCE 700 1- Desa Mohammad Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Baharu, Malaysia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/1(2015-01-01), 57-65 0941-0643 26:1<57 2015 26 521