A Semisupervised Method With Swarm Intelligence Optimization for Intelligent Fault Diagnosis

With the recent development of deep learning, machine-learning-based methods have gained promising achievements for fault diagnosis. However, most of these methods are supervised, and a large amount of labeled data is required for training in practical applications. To deal with the problem of limited labeled samples in fault diagnosis tasks, a semisupervised deep-learning-based method, named particle swarm joint classifier with an autoencoder (PJCA), is proposed in this article. In this method, a classifier and an autoencoder are developed using labeled data and unlabeled data, respectively, and two loss functions are combined to train these two models simultaneously. In addition, an optimization strategy based on the greedy algorithm and particle swarm optimization (PSO) is designed and applied to optimize the combined weights of the loss function. The proposed method is verified experimentally on two popular rotating machinery datasets: the Case Western Reserve University bearing dataset and the Paderborn University bearing dataset. The experimental results have demonstrated that the proposed method could achieve a classification accuracy of over 95% on these two datasets with no more than 20 labeled samples per class, and the proposed optimization strategy could improve the classification accuracy significantly when reducing the number of parameters.