Metalworking fluids (MWFs) are essential in machining processes, although their degradation over time requires continuous monitoring to prevent operational and economic losses. This study addresses missing data in a MWF sensor time series, which hinders the reliability of predictive maintenance. Advanced imputation methods (including pre-trained and fine-tuned MOMENT-1, LSTM-VAE, KNN, and HybridKCL) were evaluated for reconstructing gaps in four critical MWF properties: pH, temperature, concentration, and conductivity. The performance was quantitatively assessed using the MAE and RMSE on artificial masked data. Among the evaluated methods, the fine-tuned MOMENT-1 model generally outperformed the other methods across the variables, exhibiting a favorable balance of low reconstruction error and high visual consistency. However, qualitative inspection remains essential to verify the plausibility of the reconstructed dynamics. These findings contribute to improving the integrity of MWF monitoring data, enabling more reliable predictive analytics, and supporting efficient and sustainable manufacturing.