Sun Lijie, Tao Xin, Lu Yanping
School of Art and Design, Taizhou University, Taizhou, Zhejiang, China.
School of Electronics and Information Engineering, Taizhou University, Taizhou, Zhejiang, China.
PLoS One. 2025 Jul 31;20(7):e0327646. doi: 10.1371/journal.pone.0327646. eCollection 2025.
The working state of rolling bearing severely affects the performance of industrial equipment. Addressing the issue of that the difficulty of incipient weak signals feature extraction influences the rolling bearing diagnosis accuracy, an efficient bearing fault diagnostic technique, a proposition is forwarded for hybrid artificial intelligence models, which integrates Improved Harris Hawks Optimization (IHHO) into the optimization of Deep Belief Networks and Extreme Learning Machines (DBN-ELM). The process employs Maximum Second-order Cyclostationary Blind Deconvolution (CYCBD) to filter out noise from the vibration signals emitted by bearings; secondly, considering the issue with the conventional Harris Hawks Optimization (HHO) algorithm which tends to prematurely converge to local optima, the differential evolution mutation operator is introduced and the escape energy factor is improved from linear to nonlinear in IHHO; then, a double-layer network model based on DBN-ELM is proposed, to avoid the number of hidden layer nodes of DBN from human experience interference, and IHHO is used to optimize DBN structure, which is denoted as IHHO-DBN-ELM method; with the optimal structure is obtained by using a combined IHHO optimized DBN and ELM; in conclusion, the proposed IHHO-DBN-ELM approach is applied to the bearing fault detection using the Western Reserve University's bearing fault dataset. The outcome of the experiments demonstrates that IHHO-DBN-ELM technique successfully extracts fault characteristics from the raw time-domain signals, thereby offering enhanced diagnostic accuracy and superior generalization capabilities.
滚动轴承的工作状态严重影响工业设备的性能。针对早期微弱信号特征提取困难影响滚动轴承诊断精度的问题，提出了一种高效的轴承故障诊断技术，即一种将改进的哈里斯鹰优化算法（IHHO）集成到深度信念网络和极限学习机（DBN-ELM）优化中的混合人工智能模型。该过程采用最大二阶循环平稳盲反卷积（CYCBD）从轴承发出的振动信号中滤除噪声；其次，考虑到传统哈里斯鹰优化（HHO）算法容易过早收敛到局部最优的问题，在IHHO中引入差分进化变异算子并将逃逸能量因子从线性改进为非线性；然后，提出一种基于DBN-ELM的双层网络模型，避免DBN隐藏层节点数量受人为经验干扰，并用IHHO优化DBN结构，记为IHHO-DBN-ELM方法；通过IHHO优化DBN和ELM的组合获得最优结构；最后，将所提出的IHHO-DBN-ELM方法应用于使用美国西储大学轴承故障数据集的轴承故障检测。实验结果表明，IHHO-DBN-ELM技术成功地从原始时域信号中提取了故障特征，从而提高了诊断精度和泛化能力。
