CNN-Based Hybrid Optimization for Anomaly Detection of Rudder System

In this study, an automatic test platform suitable for steering gears was established, which can test four sets of rudder systems separately. In addition, we propose an anomaly detection method based on deep learning technology to complete the automated multi-fault classification of the steering gear test. This paper combines the particle swarm optimization algorithm and the grey wolf optimization algorithm to optimize the convolutional neural networks (HPSOGWO-CNN). The proposed HPSOGWO-CNN model is constructed in two stages to realize the efficient and high-accuracy anomaly detection of the rudder system. In the first stage, through 10-fold cross validation, the optimal number of search agents of the HPSOGWO algorithm is obtained, and the performance is compared with GWO and PSO algorithms respectively. The results demonstrate that HPSOGWO algorithm is an excellent technique for automatic selection of hyper-parameters. In the second stage, the designed HPSOGWO algorithm is used to fine-tune the hyper-parameters of CNN, and a highly matched model for anomaly detection of rudder system test parameters was finally obtained. The experimental results show that the accuracy of this method is 99.846%, the precision is 99.748%, the recall is 99.498%, the F-score is 99.618%, and Kappa reaches 0.99565. CNN-based hybrid optimization for anomaly detection of rudder system, is advanced in comparison to KNN, SVM, BP, CNN, PSO-CNN, GWO-CNN, MGWO-CNN, WdGWO-CNN, RW-GWO-CNN models, in terms of accuracy, precision, recall, F-score, and kappa, respectively. Moreover, it is not affected by the imbalance samples, and can achieve accurate classification for small training samples.