School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China.
National United Engineering Laboratory for Advanced Bearing Tribology, Henan University of Science and Technology, Luoyang 471023, China.
Sensors (Basel). 2023 Jun 4;23(11):5325. doi: 10.3390/s23115325.
Real-time condition monitoring and fault diagnosis of spindle bearings are critical to the normal operation of the matching machine tool. In this work, considering the interference of random factors, the uncertainty of the vibration performance maintaining reliability (VPMR) is introduced for machine tool spindle bearings (MTSB). The maximum entropy method and Poisson counting principle are combined to solve the variation probability, so as to accurately characterize the degradation process of the optimal vibration performance state (OVPS) for MTSB. The dynamic mean uncertainty calculated using the least-squares method by polynomial fitting, fused into the grey bootstrap maximum entropy method, is utilized to evaluate the random fluctuation state of OVPS. Then, the VPMR is calculated, which is used to dynamically evaluate the failure degree of accuracy for MTSB. The results show that the maximum relative errors between the estimated true value and the actual value of the VPMR are 6.55% and 9.91%, and appropriate remedial measures should be taken before 6773 min and 5134 min for the MTSB in Case 1 and Case 2, respectively, so as to avoid serious safety accidents that are caused by the failure of OVPS.
实时状态监测和故障诊断对于数控机床的正常运行至关重要。在这项工作中，考虑到随机因素的干扰，针对机床主轴轴承（MTSB）引入了振动性能维持可靠性（VPMR）的不确定性。结合最大熵方法和泊松计数原理来求解变化概率，从而准确地描述 MTSB 最优振动性能状态（OVPS）的退化过程。利用多项式拟合的最小二乘法计算动态均值不确定性，并将其融合到灰色 bootstrap 最大熵方法中，以评估 OVPS 的随机波动状态。然后，计算 VPMR，用于动态评估 MTSB 的精度失效程度。结果表明，VPMR 的估计真实值与实际值之间的最大相对误差分别为 6.55%和 9.91%，在案例 1 和案例 2 中，MTSB 分别应在 6773 分钟和 5134 分钟之前采取适当的补救措施，以避免因 OVPS 失效而导致的严重安全事故。
