Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA
Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA
复制标题
基于TFDC-QPSO优化MOMEDA的核电站CRF泵行星轴承故障特征提取
DOI：
10.1088/1361-6501/ac9e6d
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发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
Shixi Yang
中科院分区：
文献类型：
--
作者：
Jiashuo Zhang;Xin Xiong;Jun He;Yuanyuan Huang;Shixi Yang
针对核电站常规岛循环水泵（CRF泵）机组行星齿轮箱轴承故障特征弱、特征提取困难的问题，采用改进的混合算子量子行为粒子群优化（QPSO）算法，对调整后的多点最优最小熵反卷积（MOMEDA）反卷积方法进行优化，提取行星齿轮箱轴承的周期性故障脉冲。在混合算子中引入微分进化算子，提高了粒子群的多样性，增强了算法的全局优化能力。同时，在算法中引入自适应交叉算子，提高了算法的收敛速度。然后，将这两类算子结合起来，可以构造出既具有全局优化能力又具有较高算法执行效率的参数优化算法。考虑捕捉与轴承故障有关的周期性脉冲特征。提出了一种基于时域和频域反褶积信号特征信息的适应度函数。该函数作为目标函数，便于在MOMEDA上进行参数优化。从而增强了我们的特征提取方法的优化能力。将该方法应用于某试验台内、外啮合故障行星轴承的信号提取实验，取得了较好的特征提取效果。经过对比分析，我们发现该方法提取的特征的重要性和算法的执行效率都优于其他方法。某核电站CRF泵机组行星齿轮箱输入轴轴承的现场特征提取结果也验证了所提方法的工程实用性。
To address the issue of weak fault features and difficulty in feature extraction for planetary gearbox bearings of the circulation water pump (CRF pump) unit in the conventional island of a nuclear power plant, a deconvolution method, named the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is optimised by a mixing operator improved quantum behaviour particle swarm optimisation (QPSO) algorithm, to extract the periodic fault impulse of planetary gearbox bearings. In the mixing operator, a differential evolution operator is introduced to improve particle swarms’ diversity and enhance the algorithm’s global optimisation capability. Meanwhile, a proposed adaptive CrossOver operator is incorporated into the algorithm to increase its convergence speed. Then, combining these two types of operators can construct a parameter optimisation algorithm displaying both global optimisation capability and high algorithm execution efficiency. Consider capturing the periodic impulsive features relating to bearing faults. A fitness function based on the characteristic information of the deconvolution signal in the time and frequency domain is proposed. This function serves as the objective function to facilitate the parameter optimisation on MOMEDA. Thereby, it enhances the optimisation capability of our feature extraction method. Experiments were conducted by adopting the proposed method on the signals collected from the inner- and outer-race faulty planetary bearing in a test bed, in which favourable feature extraction results are obtained. After the comparative analysis, we observed that both significances of features extracted by this method and the execution efficiency of the algorithm are superior compared to other methods. The on-site feature extraction results of the input shaft bearing in the planetary gearbox of a CRF pump unit in a nuclear power plant also demonstrated the engineering practicability of the method proposed in this work.
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