NDNET: A Unified Framework for Anomaly and Novelty Detection

Dataset of a parameterized U-bend flow for deep learning applications

“…The Figure is modified from Decke et al. [3] . The boundary points can vary within the dashed boxes, while the curve parameters indicate the variation between the origin boundary point and its opposite boundary point.…”

“…Research in multimodal features, transfer learning, active learning and learning from image and/or graph data using supervised, unsupervised and or semi-supervised learning methods are feasible. The dataset was originally used as a case study to demonstrate the versatile applicability of a developed framework for anomaly and novelty detection [3] . To enable other researchers the use this dataset, and for subsequent investigations, this dataset is provided with public accessibility licensed by CC-BY-NC-4.0.…”

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

“…In the field of PSE, the term process monitoring has been often used interchangeably with the term fault detection and diagnosis [20]. However, in other domains, such as signal processing and medical data analysis, the same concept is known by different names, such as anomaly detection , novelty detection [710][711][712][713][714][715][716][717][718][719][720][721][722][723][724] and changepoint detection [725][726][727][728][729][730][731][732][733][734][735][736][737][738][739][740][741]. Although these three terms refer to variations in the same fundamental problem (at least from a computational and mathematical perspective), there is a noticeable lack of information exchange between the communities focused on these studies and the process system engineering community.…”