Fault diagnosis based on sliding mode observer for LPV descriptor systems
Sign Up to like & getrecommendations! Published in 2019 at "Asian Journal of Control"
DOI: 10.1002/asjc.2022
Abstract: This paper considers the problem of fault detection and reconstruction of actuator faults for linear parameter varying descriptor systems. A polytopic sliding mode observer (PSMO) is constructed to achieve simultaneous reconstruction of LPV polytopic descriptor… read more here.
Keywords: mode observer; sliding mode; fault diagnosis; descriptor systems ... See more keywords
Fault diagnosis of output‐related processes with multi‐block MOPLS
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Chemometrics"
DOI: 10.1002/cem.2917
Abstract: For fault diagnosis of output‐related processes, a relatively high false alarm rate (FAR) of output‐irrelevant faults exists because the output‐irrelevant variables are not removed completely by conventional approaches. A relatively large number of computational loads… read more here.
Keywords: block; diagnosis; output; output related ... See more keywords
Fault Diagnosis Based on the Integration of Exponential Discriminant Analysis and Local Linear Embedding
Sign Up to like & getrecommendations! Published in 2018 at "Canadian Journal of Chemical Engineering"
DOI: 10.1002/cjce.22921
Abstract: Industrial process data have the characteristics of high dimensions and nonlinearity, so it is very important to extract the data features for fault diagnosis. Two kinds of improved exponential discriminant analysis methods, local linear exponential… read more here.
Keywords: exponential discriminant; analysis; fault diagnosis; discriminant analysis ... See more keywords
Class metric regularized deep belief network with sparse representation for fault diagnosis
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22831
Abstract: This paper proposes a joint class metric and sparse representation regularized deep belief network (J‐DBN) method for intelligent fault diagnosis of the rotary equipment. In this novel method, the joint class metric and sparse representation… read more here.
Keywords: sparse representation; fault diagnosis; class metric; class ... See more keywords
Self‐supervised domain adaptation for cross‐domain fault diagnosis
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.23026
Abstract: Unsupervised domain adaptation‐based fault diagnosis methods have been extensively studied due to their powerful knowledge transferability under different working conditions. Despite their encouraging performance, most of them cannot sufficiently account for the temporal dimension of… read more here.
Keywords: self supervised; domain; fault diagnosis; domain adaptation ... See more keywords
Robust fault diagnosis and fault‐tolerant control for uncertain multiagent systems
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Robust and Nonlinear Control"
DOI: 10.1002/rnc.5228
Abstract: This article considers the problem of fault diagnosis and fault‐tolerant control for uncertain multiagent systems. First, the multiagent system model with disturbances and uncertainty parameters is shown in the article. Then, a new robust adaptive… read more here.
Keywords: fault tolerant; diagnosis fault; tolerant control; fault diagnosis ... See more keywords
Digital twin‐assisted fault diagnosis system for robot joints with insufficient data
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Field Robotics"
DOI: 10.1002/rob.22127
Abstract: The robot joint is an important component of the construction robot, and its fault diagnosis can ensure the exact execution of building jobs, stable operation, and timely prevention of probable safety mishaps. However, deep learning‐based… read more here.
Keywords: digital twin; insufficient data; fault diagnosis;
Application of hybrid dimensionality reduction for fault diagnosis of three-phase inverter in PMSM drive system
Sign Up to like & getrecommendations! Published in 2019 at "Electrical Engineering"
DOI: 10.1007/s00202-019-00823-8
Abstract: The feature dimension reduction is a useful pre-processing step for fault diagnosis, where the irrelevant and redundant information in the data can be reduced. In this case, not only the computation complexity can be reduced,… read more here.
Keywords: three phase; phase inverter; reduction; fault diagnosis ... See more keywords
Similarity measures of generalized trapezoidal fuzzy numbers for fault diagnosis
Sign Up to like & getrecommendations! Published in 2019 at "Soft Computing"
DOI: 10.1007/s00500-017-2914-y
Abstract: In this paper, we propose a new similarity measure between generalized trapezoidal fuzzy numbers and several synthesized similarity measures to solve fault diagnosis problem by merging our proposed measures with Dempster–Shafer evidence theory. Firstly, combining… read more here.
Keywords: similarity measures; generalized trapezoidal; trapezoidal fuzzy; similarity ... See more keywords
Establishment of a deep learning network based on feature extraction and its application in gearbox fault diagnosis
Sign Up to like & getrecommendations! Published in 2019 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-019-09710-x
Abstract: Gearbox is an important part of mechanical equipment. If a fault cannot be timely detected, it will cause significant economic losses. In order to solve the problem of early fault diagnosis quickly and accurately, this… read more here.
Keywords: fault diagnosis; learning network; network; deep learning ... See more keywords
A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems
Sign Up to like & getrecommendations! Published in 2022 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-021-09993-z
Abstract: Fault diagnosis plays an important role in actual production activities. As large amounts of data can be collected efficiently and economically, data-driven methods based on deep learning have achieved remarkable results of fault diagnosis of… read more here.
Keywords: diagnosis; fault diagnosis; time; cnn lstm ... See more keywords