## 4 Results and Discussion
For this reason, it was evident that  
changes in the characteristic frequency of the vibration over time did not adequately capture the vibrational signatures corresponding to the inherent nature of lubricant degradation. Figure 8 shows the results of using the structural response algorithm to analyze the vibration emitted during the clamping and unclamping of the toggle clamping system. This approach used the shock response spectrum to estimate the maximum acceleration imparted into a series of SDOF systems of varying natural frequencies. A singular energy value was then calculated and used as a health indicator (\(HI_{SRS}\)) to monitor the real-time lubrication condition of the toggle clamping system. Like the previously explored vibration analysis techniques, the \(HI_{SRS}\) of the clamping period (Figure 8a) showed no consistent trend indicative of changes in the lubrication condition of the toggle clamping system. On the contrary, the SRS health indicator of the vibration generated during the unclamping period (Figure 8b) proved to be a feasible solution for condition monitoring. The results obtained by using this approach stands in agreement with the widely published bathtub curve [35]. As described by the bathtub curve, the lifecycle of a lubricant has three stages, namely, a decreasing failure rate, a low failure rate, and an increasing failure rate. Figure 8b is in agreement with this behavior as the \(HI_{SRS}\) assumed a relatively low value at the start of experimentation, due to a freshly re-lubricated toggle. As the cycle-number increased, the \(HI_{SRS}\) remained relatively stable indicating a state of \"low failure rate\" as shown by the green arrows. After a number of cycles elapsed, the system transitioned to a state of \"increasing failure rate\" as shown by the red arrow. Upon the introduction of new lubricant to the system, at cycles 150, 330, and 530, the \(HI_{SRS}\) rapidly returned to its baseline. This consistent reaction indicated that a state of \"decreasing failure rate\" was being achieved as shown by the yellow arrows. This bathtub-like pattern proved to be consistent throughout the experiment. Given this finding, the optimal lubrication regime was to re-lubricate  
Figure 6: Time domain analysis for monitoring of the toggle’s lubricating condition: (**a**) Plot of RMS health indicator vs. cycle number for the clamping period; (**b**) plot of RMS health indicator vs. cycle number for the unclamping period. Figure 7: Frequency domain analysis for condition monitoring of the toggle’s lubricating condition: (**a**) plot of FC health indicator vs. cycle number for the clamping period; (**b**) plot of FC health indicator vs. cycle number for the unclamping period. the toggle when the system transitions from a \"low failure rate\" to a \"high failure rate\". In doing so, the life of the lubricant life is maximized while vibrational damage to the toggle is maintained at a low state. The above results confirm that vibration analysis is a novel approach for monitoring the lubrication condition of the toggle clamping system. It was observed that vibrational signatures pertaining to lubrication condition were more profound in the unclamping period than in the clamping period. This observation was supported by the fact that the toggle needed to overcome static friction each time the system was unclamped, while during clamping, it was only subjected to kinetic friction. Since static friction is much larger than kinetic friction, higher energy vibrational signatures relating to a deteriorating lubricating condition was emitted during the unclamping period. The optimal condition monitoring approach did not come in the form of individually monitoring changes in the wave amplitude (RMS health indicator) nor the changes in characteristic frequencies (FC health indicator), but rather by monitoring the changes in the energy excitation potential which were dependent on a non-linear combination of amplitude and frequency (SRS health indicator).