{"type":"ScholarlyArticle","title":"Fault Diagnosis in REDNet Model Space","authors":[{"name":"Xiren Zhou","slug":"xiren-zhou","given":"Xiren","family":"Zhou","affiliation":""},{"name":"Ziyu Tang","slug":"ziyu-tang","given":"Ziyu","family":"Tang","affiliation":""},{"name":"Shikang Liu","slug":"shikang-liu","given":"Shikang","family":"Liu","affiliation":""},{"name":"Ao Chen","slug":"ao-chen","given":"Ao","family":"Chen","affiliation":""},{"name":"Xiangyu Wang","slug":"xiangyu-wang","given":"Xiangyu","family":"Wang","affiliation":""},{"name":"Huanhuan Chen","slug":"huanhuan-chen","given":"Huanhuan","family":"Chen","affiliation":""}],"abstract":"Fault Diagnosis (FD) in time-varying data presents considerations such as limited training data, intra- and inter-dimensional correlations, and constraints of training time. In response, this paper introduces FD in the Reservoir-Embedded-Directional Network (REDNet) model space. Model-oriented methods utilize well-fitted networks or functions, denoted as \"models\" that capture data's changing information, as more stable and parsimonious representations of the data. Our approach employs REDNet for data fitting, wherein multiple reservoirs are organized along intrinsic correlation directions to establish intra- and inter-dimensional dependencies, thereby capturing multi-directional dynamics in high-dimensional data.\r\nRepresenting each data instance with an independently fitted REDNet model maps these instances into a class-separable REDNet model space, where FD could be performed on the models rather than the original data. Concentrating on the data-intrinsic dynamics, our method achieves rapid training speeds, and maintains robust performance even with minimal training data. Experiments on several datasets demonstrate its effectiveness.","doi":"10.24963/ijcai.2025/802","url":"https://ijcai.org/proceedings/2025/802","pdf":"https://ijcai.org/proceedings/2025/0802.pdf","markdown":"https://ijcai.org/proceedings/2025/802.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/802","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":802,"pages":{"first":7209,"last":7217},"keywords":["Machine Learning: ML: Classification","Data Mining: DM: Anomaly/outlier detection","Machine Learning: ML: Representation learning","Machine Learning: ML: Recurrent networks"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}