Besides, real-time computation and storage handling may be required, prioritizing the minimization of modeling efforts. The system should also have a trade-off between robustness and performance. The system should model the normal behaviour of the process and be able to identify novel abnormal operations. In addition, the system should distinguish different failure types. Moreover, the system should adapt to the changing process operating conditions and environmental conditions to ensure it continues working correctly. Finally, the model should provide explanations about the root cause of the anomaly so that operators can evaluate it and act based on their expertise. [PERSON] et al. state in [9] that most common deep learning-based semi-supervised anomaly detection works for PdM are based on autoencoders [10], variational autoencoders [11], generative adversarial networks [12], or recurrent neural networks [13; 14]. In addition, semi-supervised diagnosis for PdM is usually addressed by self-organizing map (SOM) [15] and \(t\)-distributed stochastic neighbor embedding (\(t\)-SNE) [16] models. Furthermore, transfer learning techniques on deep learning models enable reusability and adaptability to changes in environmental and operational conditions (EOC). Several works including transfer learning techniques are [PERSON] et al. [17] with application on motor vibration anomaly detection, [PERSON] et al. [18] implementing univariate anomaly detection inspired in U-net, and [PERSON] et al. [19] who combine Bayesian search and convolutional neural networks for anomaly detection. There exist many of state-of-the-art data-driven techniques that address PdM diagnosis based on classification, using samples of the target failure class for learning. However, diagnosis on semi-supervised models is much harder than on supervised classification models given that target failure data are not available for model training. In a semi-supervised scenario, anomaly detection requires modeling normality of correct data on a training dataset to detect anomalous samples with respect to learned normality, whereas diagnosis should group anomalous data instances by similarity to discover failure types, and then give insight on possible causes for each failure type to facilitate maintenance. Three related works that address PdM diagnosis in a semi-supervised way are reviewed in this paragraph. [PERSON] et al. [11] use a variational autoencoder to detect anomalies on a bearings dataset of an experimental setup and diagnose the anomalies using a discriminator architecture. The second work is by [PERSON] et al. [20], who use different state-of-the-art and classical data-driven algorithms including deep learning to detect anomalies on an interacting quadruple tank system, and they use several diagnosis techniques according to the used model. Another related work is by [PERSON] et al. [21], who use different XAI techniques for unsupervised fault detection and diagnosis in rotating machinery, applying machine learning models that are not deep learning models. Research interest on transfer learning in the field of PdM is increasing given that it enables adapting trained models to data changes efficiently. [PERSON] et al. in [22] use transfer learning to adapt a pre-trained 2D deep learning model to acceleration data of a rotating machine for PdM, reducing training resources. [PERSON] et al. apply transfer learning to transfer knowledge between models learned with different data subsets of turbofan simulation dataset [23]. In addition, [PERSON] et al. in [24] use transfer learning for incremental or sequential learning applied to industrial image recognition. They argue that transfer learning is suitable to solve the challenges of industrial real-life scenarios. The objective of this work is to obtain a semi-supervised anomaly detection model trained with correct condition machine data that identifies novel anomalies, where the most accurate model among different data-driven models including deep learning is selected.