Handling Imbalanced Datasets for Robust Deep Neural Network-Based Fault Detection in Manufacturing Systems

Over the recent years, Industry 4.0 (I4.0) 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 Dee...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 21; p. 9783
Main Authors Kafunah, Jefkine, Ali, Muhammad Intizar, Breslin, John G.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2021
Subjects
Artificial intelligence
Classification
Datasets
Deep learning
deep neural networks
Distributed control systems
Explicit knowledge
Fault detection
imbalanced datasets
Industrial applications
Internet of Things
Manufacturing
Manufacturing industry
Methods
Neural networks
Workflow
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Summary:Over the recent years, Industry 4.0 (I4.0) 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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11219783