2021
DOI: 10.3390/app11219783
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Handling Imbalanced Datasets for Robust Deep Neural Network-Based Fault Detection in Manufacturing Systems

Abstract: 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, lead… Show more

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Cited by 6 publications
(3 citation statements)
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“…Automated body condition scoring research knows this problem all too well with the majority of the training samples sitting in the middle classes and a small number of samples in the edge classes, as can be seen in Figure 3. There are numerous techniques available for dealing with imbalanced classes such as oversampling under-represented classes, undersampling over-represented classes, cost-sensitive learning, and even transfer learning [27]. The technique chosen for this study was cost-sensitive learning and the approach is explained in detail in Section 3.5.3.…”
Section: Data Collectionmentioning
confidence: 99%
See 1 more Smart Citation
“…Automated body condition scoring research knows this problem all too well with the majority of the training samples sitting in the middle classes and a small number of samples in the edge classes, as can be seen in Figure 3. There are numerous techniques available for dealing with imbalanced classes such as oversampling under-represented classes, undersampling over-represented classes, cost-sensitive learning, and even transfer learning [27]. The technique chosen for this study was cost-sensitive learning and the approach is explained in detail in Section 3.5.3.…”
Section: Data Collectionmentioning
confidence: 99%
“…As mentioned before, there are numerous techniques available for dealing with imbalanced classes such as oversampling under-represented classes, undersampling overrepresented classes, cost-sensitive learning, and even transfer learning [27]. Cost-sensitive learning was the chosen technique for this study.…”
Section: Trainingmentioning
confidence: 99%
“…The Quality 4.0 branch of I4.0 is present. By utilizing cutting-edge computational tools and new approaches, this field seeks to improve product quality [3,4]. There are many social Sensors 2023, 23, 7011 2 of 23 constructivisms and patterns, but the main obstacle is grasping Grade 4.0 in social studies.…”
Section: Introductionmentioning
confidence: 99%