aspg-18-750-1792025674 20261015005434 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2021 4 1 Ahmed N. Al-Masri American University in the Emirates, Dubai, UAE Hamam Mokayed LTU University of Technology, Sweden Gear faults have always been a problem encountered in mechanical processing. For gear fault diagnosis, using mathematical-statistical feature extraction methods, deep learning neural networks (DLNN), particle swarm algorithm (PSA), and support vector machines (SVM), etc. According to the feature extraction of deep learning and particle swarm SVM state recognition, the intelligent diagnosis model is established, and the reliability of the model is verified by experiments. The model uses the combination of spectral features extracted by deep learning adaptively and the time domain features extracted by mathematical statistics methods to form a joint feature vector and then uses particle swarm SVM to diagnose the joint feature vector. After research, this paper draws a classification fitness curve combining the fault spectrum features extracted by DLNN and traditional time-domain statistical features. The classification result obtained by using this method is 95.3%. The reliability of the model is verified, and satisfactory diagnosis results are obtained. In addition, the application results also verify the effectiveness of adaptively extracting spectral features based on deep learning. 2021 26 40 750 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.040102 https://americaspg.com/journal/18/article/750 https://americaspg.com/storage/6285.pdf