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研究目的:为了缩短机床温升试验时间,提出一种机床热特性快速辨识方法,利用较短时间的温度采样数据即可准确预测出完整的温升曲线,进而获得热平衡时间及稳态温度等热特性参数。创新要点:提出了基于自适应无味卡尔曼滤波的机床选点温升快速辨识方法,其中最短辨识时间判据可以有效解决如何寻找准确辨识热特性参数的最短采样时间问题,而自适应无味卡尔曼滤波则可以实时调整参数,防止外界因素对辨识的干扰。研究方法:由于无味卡尔曼滤波在非线性状态预测和参数辨识上具有优势,所以本文将无味卡尔曼滤波算法应用到机床选点温升辨识上。为了防止辨识过程中的发散退化等问题,将无味卡尔曼滤波发展为自适应无味卡尔曼滤波(图1)。在快速辨识方法上提出了最短辨识时间判据(图2)。文章中又将此算法应用到实际的立式加工中心温升辨识上,证明了该算法的可行性及有效性(图5和6)。最后又将带有自适应调整过程的无味卡尔曼滤波算法和不带调整过程的算法做了对比,显示了自适应调整过程对辨识算法的重要性(图6和11)。重要结论:基于自适应无味卡尔曼滤波的机床选点温升快速辨识方法可以准确快速地辨识出温升曲线,获取热特性参数,将原来394 min的热平衡试验时间缩短,只需28 min即可得到温升变化情况。
Research purposes: In order to shorten the test time of machine temperature rise, a rapid identification method of machine tool thermal characteristics is proposed. By using the temperature sampling data in a short time, the complete temperature rise curve can be accurately predicted, and then the heat balance time and steady temperature Characteristic parameters. Innovative Points: This paper proposes a fast identification method of machine tool temperature rise based on adaptive and tasteless Kalman filter. The shortest identification time criterion can effectively solve the shortest sampling time problem to find the accurate identification of thermal characteristics parameters, while the adaptive tasteless Kalman Filtering real-time parameters can be adjusted to prevent external factors on the identification of interference. Research methods: Because of the advantage of the non-state Kalman filter in nonlinear state prediction and parameter identification, this paper applies the unscented Kalman filter algorithm to temperature rise identification of the machine tool selection point. In order to prevent the problem of divergence degradation in the identification process, the unscented Kalman filter is developed into an adaptive and tasteless Kalman filter (Fig. 1). In the rapid identification method proposed the shortest identification time criterion (Figure 2). In the article, this algorithm is applied to the identification of actual vertical machining temperature rise, which proves the feasibility and effectiveness of the algorithm (Figures 5 and 6). Finally, the unscented Kalman filter with adaptive adjustment process is compared with the algorithm without adjustment process, showing the importance of adaptive adjustment process to the identification algorithm (Figures 6 and 11). Important Conclusions: The rapid identification of temperature rise of machine tool point based on adaptive and tasteless Kalman filter can identify the temperature rise curve accurately and quickly and obtain the thermal characteristic parameters. The heat balance test time of 394 min can be shortened in just 28 min Get temperature changes.