## 2 Review  
The three main categories of FD include model-based, knowledge-based and, data-driven approaches [9; 10; 11]. In this work, we focus on data-driven methods that are more suitable in the context of I4.0 as they rely upon industrial big data (IBD), containing valuable historical information on the operation processes. The data-driven approach used in this paper is a supervised method [32] that depends on labeled data, providing prior information about the fault subspace or region in data space. General approaches designed for tackling imbalanced datasets can be split into two types: re-sampling and reweighting. Data re-sampling techniques such as undersampling and oversampling have been used as a solution to tackle the imbalanced dataset problem [31; 33; 34]. Undersampling-based methods work by reducing the number of samples from the majority class to achieve a more balanced target class distribution. These methods are synonymous with data purging, a practice that always leads to the loss of information from the original dataset [35]. In cases where the original dataset is not large enough, undersampling further reduces the size of the dataset, hence limiting the capacity to train a robust classifier [33]. Oversampling-based methods, on the other hand, solve the imbalance problem by generating random replica samples from the minority class to balance the distribution of the target classes. These additional copies end up increasing the likelihood of over-fitting by the classifier. Another downside in cases where the original dataset is already large is that oversampling creates even more data that has to be evaluated by the classifier, leading to an increase in the workload [31; 34]. [PERSON] and [PERSON] [36] propose a Gaussian Process Regression (GPR) and Generative Adversarial Network (GAN) framework through which complete data are generated, first by replacing missing values using GPR and finally applying GAN to generate refinements including new data similar to the real data. [PERSON] et al. [25] use data augmentation techniques to oversample the minority and combine this with a convolutional neural network (CNN) to automatically extract effective features of various defect classes. [PERSON] et al. [37] develop a fault detection and classification (FDC) model out of stacked denoising autoencoders to perform robust feature extraction from data that contains noise induced by mechanical and electrical disturbances. In [38], the authors proposed an FDC-CNN model that makes use of special receptive fields as feature extractors, achieving high performance on FDC tasks while improving training speed. We also note that FD models depending on robust feature extraction can be application-specific, requiring explicit knowledge of relations between process variables. Nonetheless, models dependent on feature extraction can easily be extended using our classifier-level method to factor in imbalanced datasets. [PERSON] et al. [27] employ the use of one-class classification, while [PERSON] et al. [24] propose a hybrid ANN-Naive Bayes classifier for a two-class imbalanced dataset. Both these strategies face challenges scaling up as the number of target classes increase. Further, [PERSON] et al. [39] propose a transfer learning-based approach, a method to counter imbalanced datasets by pre-training a Neural Network (NN) using a larger dataset as the source domain and eventually transferring the knowledge to train an NN using the imbalanced dataset in the target domain. Our method falls in the data reweighting category, thereby preserving dataset integrity. Cost-sensitive reweighting is a widely used strategy where adaptive weights are applied to different classes based on a chosen criteria. Refs. [40; 41] propose unique formulations for computing a class-balancing weight hyperparameter. They use the class weighting approach on the loss function to balance the losses from the minority and majority samples. [PERSON] et al.