Zobrazeno 1 - 10
of 556
pro vyhledávání: '"Steven X. Ding"'
Publikováno v:
IET Control Theory & Applications, Vol 18, Iss 17, Pp 2347-2357 (2024)
Abstract The main objective of this paper is to develop a distributed fault detection (FD) approach for large‐scale interconnected systems using sensor networks. Specifically, the one‐step prediction based on the measured data is implemented in a
Externí odkaz:
https://doaj.org/article/388caa1e2e624364ac26ed7a4190da13
Publikováno v:
IET Control Theory & Applications, Vol 18, Iss 2, Pp 201-212 (2024)
Abstract This paper studies data‐driven distributed fault diagnosis for large‐scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to improve the fault detection
Externí odkaz:
https://doaj.org/article/a9fdf69d0cb14177b9fdaab489ddc29b
Publikováno v:
IEEE Access, Vol 10, Pp 74244-74258 (2022)
This paper proposes a novel deep learning architecture for estimating the remaining useful lifetime (RUL) of industrial components, which solely relies on the recently developed transformer architectures. The RUL estimation resorts to analysing degra
Externí odkaz:
https://doaj.org/article/99e598de00f24ef08ef93ba232b137ea
Publikováno v:
IEEE Access, Vol 9, Pp 149520-149528 (2021)
This paper presents an innovative and practical solution for fault-tolerant control of industrial systems based on hybrid redundant measurements. The paper proposes a fault-tolerant control scheme against single and multiple sensor faults that can oc
Externí odkaz:
https://doaj.org/article/80fe21f99da34a09be9e2099cf4ea89d
Publikováno v:
Intelligent Systems with Applications, Vol 10, Iss , Pp 200049- (2021)
We propose a novel sequence-to-sequence prediction approach for the estimation of the remaining useful lifetime (RUL) of technical components. The approach is based on deep recurrent neural network structures, namely bidirectional Long Short Term Mem
Externí odkaz:
https://doaj.org/article/a47fd5590b6d4633babaac6dc5d22e48
Publikováno v:
IEEE Access, Vol 7, Pp 11105-11113 (2019)
This paper is devoted to investigating the observer-based fault detection (FD) filters for nonlinear distributed processes described by hyperbolic partial differential equations (PDEs). To this end, the PDE systems are first approximated by the Takag
Externí odkaz:
https://doaj.org/article/d15154b831d74a2687d0c49a281402c2
Publikováno v:
IEEE Access, Vol 6, Pp 16207-16215 (2018)
Both the model-based and data-driven techniques for fault detection have their merits and drawbacks. The fault detection systems are usually laid out separately with the health monitoring systems in practice. In this paper, the well-established obser
Externí odkaz:
https://doaj.org/article/f1dda25e93fe411c859ab087967d79f6
Publikováno v:
Sensors, Vol 21, Iss 16, p 5488 (2021)
This paper presents a novel approach for anomaly detection in industrial processes. The system solely relies on unlabeled data and employs a 1D-convolutional neural network-based deep autoencoder architecture. As a core novelty, we split the autoenco
Externí odkaz:
https://doaj.org/article/60cecf0efafc4ace9ccc79ed188b6384
Publikováno v:
IEEE Access, Vol 5, Pp 20590-20616 (2017)
Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basi
Externí odkaz:
https://doaj.org/article/447db06d2239443bb27b10f7048a3277
Publikováno v:
Energies, Vol 13, Iss 4, p 807 (2020)
It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM metho
Externí odkaz:
https://doaj.org/article/d3073fcb78934798a9a1d5d2cd49ba06