###### Abstract  
Over the recent years, Industry 4.0 (4.0) technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and the presence of Industrial Big Data (IBD) have helped achieve intelligent Fault Detection (FD) in manufacturing. Notably, data-driven approaches in FD apply Deep Learning (DL) techniques to help generate insights required for monitoring complex manufacturing processes. However, due to the ratio of instances where actual faults occur, FD datasets tend to be imbalanced, leading to training challenges that result in inefficient DL-based FD models. In this paper, we propose Dual Logits Weights Perturbation (DLWP) loss, a method featuring weight vectors for improved dataset generalization in FD systems. The weight vectors act as hyperparameters adjusted on a case-by-case basis to regulate focus accorded to individual minority classes during training. In particular, our proposed method is suitable for imbalanced datasets from safety-related FD tasks as it generates DL models that minimize false negatives. Subsequently, we integrate human experts into the workflow as a strategy to help safeguard the system. A subset of the results, model predictions with uncertainties exceeding a preset threshold, are considered a preliminary output subject to cross-checking by human experts. We demonstrate that DLWP achieves improved Recall, AUC, F1 scores. fault detection; imbalanced datasets; deep neural networks 2021201219783 10.3390/app11219783 10. Neural Networks (DNNs) [14]. Nevertheless, further research issues persist due to the following: (1) most existing models do not explicitly deal with the problem of real-world imbalanced datasets, (2) models tackling imbalanced datasets depend on sophisticated data augmentation techniques, robust feature extraction (i.e., these models are application-specific), while others employ either one-class or one-vs-all classification strategies, which do not scale well as the number of target classes increases, (3) for safety-related and high-cost applications, it is necessary to limit the occurrence of false-negatives or type II errors while also providing informative measures of uncertainty regarding model predictions. Recently, DL applications have achieved state-of-the-art (SOTA) performance in a wide variety of tasks [15; 16; 17; 18; 19; 20]. The underlying assumption has been that datasets used in training these application models have a target class distribution that is reasonably well balanced across all the classes. However, real-world datasets are usually imbalanced with a long-tailed data distribution that feature under-represented classes [21; 22; 23]. Such skewed target class distributions exist in a variety of manufacturing application areas [8; 24; 25; 26; 27], including in many other real-world applications such as autonomous driving, fraud detection, network intrusion, and rare disease detection where the datasets are characteristically imbalanced [15; 28; 29; 30]. Therefore, a classifier encounters an imbalanced classification problem when trained on an imbalanced dataset where samples from the minority class are scarce compared to the majority class. Classifiers trained on imbalanced datasets generate models that can achieve a perceived high overall model accuracy, even while misclassifying all of the samples from the minority class since they only make up a relatively small portion of the entire dataset [15; 31]. In this paper, we seek to develop a DL-based FD system by generating effective models backed by coordinated reinforcements from human experts. Further, we acknowledge a significant trade-off between safety and operational efficiency for DL-based FD systems and seek to address this in our proposed solution. In particular, our approach involves designing a DL method that generates robust models from imbalanced datasets together with a data-informed call to action that enlists human experts for feedback on a subset of the model predictions with high uncertainty exceeding a preset threshold.