Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault char...Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault characteristic in the incipient stage can result in misdiagnosis, it is a challenge for fault-tolerant control to ensure system safety and reliability. Therefore, to address this issue, a fault diagnosis and fault-tolerant control with a knowledge transfer strategy(KT-FDFTC) is proposed in this paper. First, a knowledge reasoning diagnosis strategy using multi-source transfer learning is designed to distinguish the similar characteristic of incipient faults. Then, the multi-source knowledge can assist in the diagnosis strategy to strengthen the fault information for fault-tolerant control. Second, a knowledge adaptive compensation mechanism, which makes knowledge and data coupled into the output trajectory regarded as an objective function, is employed to dynamically compute the control law. Then, KT-FDFTC can ensure the stable operation to adapt to various fault conditions. Third, the Lyapunov function is established to demonstrate the stability of KT-FDFTC. Then, the theoretical basis can offer the successful application of KTFDFTC. Finally, the proposed method is validated through a real WWTP and a simulation platform. The experimental results confirm that KT-FDFTC can provide good diagnosis performance and fault tolerance ability.展开更多
In real-world scenarios,few-shot unsupervised domain adaptation(FUDA)faces the dual challenges of limited source supervision and poor target generalization due to the extremely scarce annotated source samples.Existing...In real-world scenarios,few-shot unsupervised domain adaptation(FUDA)faces the dual challenges of limited source supervision and poor target generalization due to the extremely scarce annotated source samples.Existing methods often overlook the restricted learning capacity caused by sparse source labels or fail to effectively utilize the structural information within the target domain to enhance discriminative performance.To address these issues,we propose a novel method,Collaborative Pseudo-label Transfer(CPLT),which jointly improves cross-domain adaptation under few-shot UDA settings.CPLT comprises two key components:a Pseudo-label Guided Source Augmentation(PGSA)mechanism that iteratively selects high-confidence target samples to augment the source domain and strengthen initial representation learning,and a Target-aware Discriminative Modeling(TADM)that leverages pseudo-labeled target data to construct auxiliary classifiers for enhanced inter-class discrimination and reduced misclassification under domain shift.Experiments on three widely used FUDA benchmarks validate the superior performance of CPLT,achieving average accuracy gains of+3.5%on Office-31,+1.4%on Office-Home,and+1.0%on DomainNet over competitive existing methods.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.62125301,62021003,62303024,U24A20275,62522302,62473011,92467205)the National Key Research and Development Project (Grant Nos.2022YFB3305800-5,2024YFE0212400)+2 种基金the Youth Beijing Scholars Program (Grant No.037)the Beijing Nova Program (Grant Nos.20240484694,20250484938)the Beijing Natural Science Foundation (Grant No.L253010)。
文摘Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault characteristic in the incipient stage can result in misdiagnosis, it is a challenge for fault-tolerant control to ensure system safety and reliability. Therefore, to address this issue, a fault diagnosis and fault-tolerant control with a knowledge transfer strategy(KT-FDFTC) is proposed in this paper. First, a knowledge reasoning diagnosis strategy using multi-source transfer learning is designed to distinguish the similar characteristic of incipient faults. Then, the multi-source knowledge can assist in the diagnosis strategy to strengthen the fault information for fault-tolerant control. Second, a knowledge adaptive compensation mechanism, which makes knowledge and data coupled into the output trajectory regarded as an objective function, is employed to dynamically compute the control law. Then, KT-FDFTC can ensure the stable operation to adapt to various fault conditions. Third, the Lyapunov function is established to demonstrate the stability of KT-FDFTC. Then, the theoretical basis can offer the successful application of KTFDFTC. Finally, the proposed method is validated through a real WWTP and a simulation platform. The experimental results confirm that KT-FDFTC can provide good diagnosis performance and fault tolerance ability.
基金supported by the Science and Technology Project of Qinghai Province(No.2023-QY-208).
文摘In real-world scenarios,few-shot unsupervised domain adaptation(FUDA)faces the dual challenges of limited source supervision and poor target generalization due to the extremely scarce annotated source samples.Existing methods often overlook the restricted learning capacity caused by sparse source labels or fail to effectively utilize the structural information within the target domain to enhance discriminative performance.To address these issues,we propose a novel method,Collaborative Pseudo-label Transfer(CPLT),which jointly improves cross-domain adaptation under few-shot UDA settings.CPLT comprises two key components:a Pseudo-label Guided Source Augmentation(PGSA)mechanism that iteratively selects high-confidence target samples to augment the source domain and strengthen initial representation learning,and a Target-aware Discriminative Modeling(TADM)that leverages pseudo-labeled target data to construct auxiliary classifiers for enhanced inter-class discrimination and reduced misclassification under domain shift.Experiments on three widely used FUDA benchmarks validate the superior performance of CPLT,achieving average accuracy gains of+3.5%on Office-31,+1.4%on Office-Home,and+1.0%on DomainNet over competitive existing methods.
