# Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
## 4 Discussion
### Application to Spindle Maintenance Decision Making  
The majority of this article focused on the characterization of the IG for FRF data collection. However, it remains to be demonstrated how CNC machine tool owners and operators can monitor the dynamic characteristics of the spindle unit for enhancement of CBM practices. Methods for spindle health assessment based on FRF data have seen development in recent literature. [PERSON] and [PERSON] proposed a method for spindle health assessment based on FRF measurements and known bearing fault frequency characteristics, which outperformed conventional vibration monitoring methods [14]. [PERSON] et al. presented method to describe spindle health based on changes in stiffness and damping characteristics using numerical models and a series of spindle failure experiments. In this work, the author was able to directly link spindle dynamics to cutting performance [28]. While these works both show an improved capacity for maintenance decision making compared to conventional methods, they remain isolated examples of usage. As discussed in Section 1, a significant challenge with the development of FRF-based tools for effective maintenance decision making is that it is more difficult to collect FRF data compared to more popular direct vibration monitoring methods. As a result, there is less FRF data available to conclusively demonstrate these benefits. To further motivate the usage of FRF monitoring for spindle maintenance decision making, a case study was conducted by the author on production machine tools used at an automotive engine plant. In this case study, a fleet of 10 machine tool spindles of identical design were monitored periodically every six months during scheduled downtime activities so as not to impact production. The same run-out bar tool was used in the spindle during each measurement. All tests were conducted on a static spindle using the conventional impact hammer with the nylon tip as described in Section 2.1.2. A photograph of the data collection tools used in this case study is shown in Figure 31. Over the five-year period of monitoring this fleet of machines, only one exhibited a spindle failure. While the first nine machines displayed consistent FRF characteristics, the tenth machine showed significant changes prior to the failure. Figure 32 shows a comparison of two FRF measurements taken from the failed machine before and after the spindle repair. The most significant changes were observed in the two dominant modes of the system located near 1400 and 600 Hz. The real and imaginary components of the FRF, as shown in Figure 33, were considered to determine the characteristics, as previously described. An analysis of the modal characteristics near 600 and 1400 Hz showed that after the repair the spindle unit displayed larger damping coefficients at these modes. A comparison of the modal characteristics is shown in Table 4. The two most notable changes evident in the modal parameters were the shift in frequency for Mode 1 from 569 to 808 Hz after the repair and the increase in damping for both modes. These indicators suggest that the dynamics were not stable and that this spindle was at an increased risk of failure in the future. As a result, the maintenance staff observed this machine closely and began the procedures for proc  
Figure 31: Photograph of the manual impact hammer, accelerometer, and run-out bar tool used in the case study during data collection. Figure 32: Comparison of the FRF magnitudes collected from the same machine tool spindle unit five months before a failure occurred and immediately after repair. More severe problems with the spindle followed the changes observed in the FRF. In this case, the change in spindle FRF was observed five months prior to the failure of the spindle. At the point of failure, high torque warnings were intermittently observed from the CNC controller, which prompted service of the spindle.