This work presents the application of domain generalization (DG) strategies in unsuper- vised deep learning methods, employing a Long Short-Term Memory (LSTM) and Autoencoder (AE) architecture for the detection of incipient faults in critical equipment at a mining desali- nation plant, within a predictive maintenance (PdM) framework. The case study is based on real operational data from 12 high-pressure and booster pumps over 2 to 3 years, using sensor- based monitoring of vibration, temperature, speed, and pressure. The initial phase involved ba- sic exploratory analysis through correlation matrices, Principal Component Analysis (PCA), and histograms. Next, an unsupervised deep learning algorithm was implemented using only the LSTM-AE model, with parameter tuning focused on improving signal reconstruction quality. In the following stage, a hybrid model combining LSTM-AE and DG techniques was developed. The domain generalization strategies investigated include Domain-Adversarial Neural Networks (DANN), Domain Alignment (DA), and Learning Disentangled Representations (LDR). These ap- proaches were assessed using performance metrics to compare the LSTM-AE baseline against the enhanced DG-integrated models. The results show a modest improvement when applying domain generalization. This enhancement is attributed to the model’s ability to capture domain-invariant features during training, leading to better generalization and improved anomaly detection perfor- mance compared to the standalone LSTM-AE approach.