航空学报 > 2022, Vol. 43 Issue (5): 225304-225304   doi: 10.7527/S1000-6893.2021.25304

基于振动监测的部件状态维修与备件订购联合决策

何勇, 王红, 李晶, 齐彦昆   

  1. 兰州交通大学 机电工程学院, 兰州 730070
  • 收稿日期:2021-01-21 修回日期:2021-02-21 发布日期:2022-06-01
  • 通讯作者: 王红 E-mail:wh@mail.lzjtu.cn
  • 基金资助:
    国家自然科学基金(72061022);甘肃省自然科学基金(20JR5RA401);甘肃省高等学校创新基金项目(2021B-114);甘肃省教育优秀研究生创新之星项目(2021CXZX-544)

Joint decision making for condition-based maintenance and spare parts ordering of components based on vibration monitoring

HE Yong, WANG Hong, LI Jing, QI Yankun   

  1. School of Mechatronic Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
  • Received:2021-01-21 Revised:2021-02-21 Published:2022-06-01
  • Supported by:
    National Natural Science Foundation of China (72061022); Natural Science Foundation of Gansu Province (20JR5RA401); Higher Education Institutions Innovation Found Project of Gansu Province (2021B-114); Education Department Excellent Postgraduate Innovation Project of Gansu Province (2021CXZX-544)

摘要: 基于部件振动信号的退化特征易受噪声影响而无法准确反映其运行状态,提出一种同时考虑退化特征和故障特征的部件状态维修模型。首先,基于自适应奇异谱分解建立递进式故障特征识别方法;其次,采用比例风险模型描述部件可靠度演化过程,建立以累计故障特征识别次数为备件订购阈值、以维修成本率为优化目标的联合维修模型;最后,以滚动轴承加速寿命试验数据为例对本文方法进行验证。结果表明,自适应奇异谱分解(SSD)方法对不同信号的诊断准确率更高,有效降低了维修过程中的人工诊断次数。以累计故障特征识别次数为备件订购阈值的联合决策方法可以弥补退化特征阈值在部件退化过程中不具有唯一性的不足,进而获得最优维修成本率。

关键词: 状态维修, 备件订购, 故障特征, 振动检测, 轴承, 可靠性分析

Abstract: The degradation feature of components based on vibration signals is easily affected by noise and therefore cannot accurately reflect their operation state. Considering both degradation features and fault features, we propose a condition-based maintenance model. A progressive fault feature identification method based on adaptive singular spectrum decomposition is first created, followed by the description of component reliability evolution by the proportional hazard model and the establishment of a joint maintenance model where the cumulative number of fault features is identified as the spare parts ordering threshold and the maintenance cost rate is used as the optimization objective. Finally, the accelerated life test data of a rolling bearing is used as an example to verify the effectiveness of the proposed method. The results show high diagnostic accuracy of adaptive singular spectrum decomposition for different signals, effectively reducing the number of manual diagnoses. The joint decision making method with the cumulative number of fault feature identification as the spare parts ordering threshold can compensate for the lack of uniqueness of the degradation feature thresholds and further obtain the optimal maintenance cost rate.

Key words: condition-based maintenance, spare parts ordering, fault feature, vibration monitoring, bearing, reliability analysis

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