State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2012;12(8):10109-35. doi: 10.3390/s120810109. Epub 2012 Jul 26.
Performance degradation assessment based on condition monitoring plays an important role in ensuring reliable operation of equipment, reducing production downtime and saving maintenance costs, yet performance degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation. This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time. FSVDD constructs the fuzzy-monitoring coefficient ε⁻ which is sensitive to the initial defect and stably increases as faults develop. Moreover, the parameter ε⁻ describes the accelerating relationships between the damage development and running time. However, the index ε⁻ with an oscillating trend disagrees with the irreversible damage development. The running time is introduced to form a monotonic index, namely damage severity index (DSI). DSI inherits all advantages of ε⁻ and overcomes its disadvantage. A run-to-failure test is carried out to validate the performance of the proposed method. The results show that DSI reflects the growth of the damages with running time perfectly.
基于状态监测的性能退化评估对于确保设备可靠运行、减少生产停机时间和节约维护成本至关重要，但性能退化具有很强的模糊性，且动态信息是随机和模糊的，因此如何评估模糊轴承性能退化是一个挑战。本研究提出了一种使用模糊支持向量数据描述（FSVDD）和运行时间的滚动轴承单调退化评估指标。FSVDD 构建了对初始缺陷敏感且随故障发展稳定增加的模糊监测系数 ε⁻。此外，参数 ε⁻ 描述了损伤发展与运行时间之间的加速关系。然而，呈波动趋势的指标 ε⁻ 不符合不可逆的损伤发展。引入运行时间形成一个单调指标，即损伤严重度指数（DSI）。DSI 继承了 ε⁻ 的所有优点并克服了其缺点。进行了一次失效试验以验证所提出方法的性能。结果表明，DSI 随运行时间的变化完美地反映了损伤的增长。
