Plastic injection molding has become one of the most widely used polymer processing methods due to its ability to viably produce large volumes of complex parts in a short time frame. Most of the plastic injection molding machines currently used in industry possess a toggle clamping mechanism that undergoes a repeated clamping and unclamping cycle during operation. This toggle must therefore be properly lubricated to avoid catastrophic failure and eventual machine downtime. To overcome this limitation, the industry currently relies on the experience of a skilled operator, paired with a fixed empirical value, to determine the timing for re-lubrication. This method often leads to the machine operator either wasting lubricant by over-lubricating the toggle, or damaging the toggle by failing to re-lubricate when needed. Herein, we explore the use of vibration analysis to perform real-time condition monitoring of the lubrication condition of the toggle clamping system. In this study, our novel structural response analysis out performed both traditional time domain and frequency domain analyses in isolating the vibrational signatures indicative of lubricant degradation. Additionally, this study confirms that the vibration generated during the unclamping period of the toggle, proved to contain more valuable information relevant to the instantaneous lubricant quality than provided by its corresponding clamping period.

Accelerometers are sensitive devices that capture vibrational fault signatures from industrial machines. However, noise often contaminates these fault signatures and must be eliminated before analysis. A data-driven (DD) denoising algorithm capable of filtering useful vibrational fault signatures from background noises was derived in this study. The algorithm was first validated by comparing its denoised result with a numerically generated ideal signal with a known exact solution. The DD denoising approach reduced the Mean Squared Error (MSE) from 0.459, when no denoising was performed, to 0.068, indicating an 85.2% decrease in noise. This novel approach outperformed the Discrete Wavelet (DW) denoising approach, which had an MSE of 0.115. The proposed DD denoising algorithm was also applied to preprocess vibration data used for the real-time lubrication condition monitoring of the plastic injection molding machine’s toggle clamping system, thereby reducing false positive relubrication alarms. The false positive rates, when analysis was performed on the raw vibration and the DW denoised vibration, were 10.7% and 7.6%, respectively, whereas the DD denoised vibration yielded the lowest false positive rate at 1%. This low false positive rate of the DD denoised vibration indicates that it is a more reliable condition monitoring system, thereby making this technique suitable for the smart manufacturing industry.