一种基于混合人工智能模型的轴承故障诊断方法
Lijie Sun1, Xin Tao2, Yanping Lu3
1School of Art and Design, Taizhou University, Taizhou, Zhejiang, China.
PloS one
|July 31, 2025
概括
这项研究介绍了与深度信念网络和极端学习机器 (DBN-ELM) 集成的改进的哈里斯霍克斯优化 (IHHO),用于准确的滚动轴承故障诊断. 该方法增强了从弱信号中提取特征,提高了工业设备的可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承的性能对于工业设备至关重要.
- 提取初始的弱故障信号对于准确的诊断具有挑战性.
- 现有的优化算法可能会汇聚到局部最佳.
研究的目的:
- 通过混合人工智能模型提出一种高效的轴承故障诊断技术.
- 为了提高轴承故障检测的准确性和概括性.
- 为了解决传统优化算法在特征提取方面的局限性.
主要方法:
- 噪声过的最大第二阶段循环静止盲解卷 (CYCBD).
- 改进了哈里斯·霍克斯优化 (IHHO) 以差异进化突变和非线性逃脱能量.
- 一个混合的IHHO-DBN-ELM模型优化深度信念网络和极端学习机器结构.
- 申请进入西部储备大学轴承故障数据集.
主要成果:
- 从原始时间域振动信号中成功提取故障特征.
- 与传统方法相比,证明了更高的诊断准确性.
- 在轴承故障检测方面取得了卓越的概括能力.
结论:
- 拟议的IHHO-DBN-ELM方法为滚动轴承故障诊断提供了有效的解决方案.
- 混合模型克服了弱信号特征提取传统方法的局限性.
- 这种技术显著提高了工业设备的可靠性和性能.
相关概念视频
01:21Power System Three-Phase Short Circuits
150
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
150
01:18Fault Types
127
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
For line-to-line faults occurring between phases B and C, the...
127