Fault diagnosis (FD) is considered as a hot research topic for prognostics and health management of machinery by the detection and identification of faults. The diagnosis of faults in rotating machinery is important for improving safety, enhancing reliability and reducing maintenance cost. Therefore, different research works have started to concentrate on the design of FD models with high automation and accurateness. This paper presents an FD model by integrating hierarchical symbolic analysis (HSA) and particle swarm optimization with a convolutional neural network (PSO-CNN) named HPC model. The presented HPC model initially undergoes feature extraction process using HSA. Then, the PSO-CNN model is utilized for learning the complicated non-linear relationship between the features and health conditions in an automated way. The PSO-CNN exhibits a fast convergence rate and involves a direct encoding mechanism as well as velocity operator to allow the optimal usage of PSO with CNN. For validation, a centrifugal pump dataset is employed. The simulation outcome showcased the superiority of the presence HPC model over the compared methods under different measures. From the detailed experimental outcome, it is shown that the presented FD model offers excellent results by attaining a maximum classification accuracy of 98.97 and 99.09 under two dataset namely dataset 1 and dataset 2 respectively.

通过诊断和识别故障，故障诊断（FD）被认为是机器的预测和健康管理的热门研究课题。旋转机械故障的诊断对于提高安全性，增强可靠性和降低维护成本非常重要。因此，不同的研究工作开始集中于具有高度自动化和准确性的FD模型的设计。本文通过将分层符号分析（HSA）和粒子群优化与名为HPC模型的卷积神经网络（PSO-CNN）相集成，提出了FD模型。提出的HPC模型首先使用HSA进行特征提取过程。然后，将PSO-CNN模型用于自动学习特征与健康状况之间的复杂非线性关系。PSO-CNN展现出快速的收敛速度，并且涉及直接编码机制以及速度运算符，以允许CNN最优地使用PSO。为了验证，采用了离心泵数据集。仿真结果显示了存在HPC模型相对于不同方法在比较方法上的优越性。从详细的实验结果可以看出，所提出的FD模型在两个数据集分别为数据集1和数据集2的情况下达到了98.97和99.09的最大分类精度，从而提供了出色的结果。仿真结果显示了存在HPC模型相对于不同方法在比较方法上的优越性。从详细的实验结果可以看出，所提出的FD模型在两个数据集分别为数据集1和数据集2的情况下达到了98.97和99.09的最大分类精度，从而提供了出色的结果。仿真结果显示了存在HPC模型相对于不同方法在比较方法上的优越性。从详细的实验结果可以看出，所提出的FD模型在两个数据集分别为数据集1和数据集2的情况下达到了98.97和99.09的最大分类精度，从而提供了出色的结果。