Pub Date : 2022-03-01DOI: 10.1080/21642583.2022.2042424
Yu Zhang, Peng Wang, Hongwan Yang, Qi Cui
Because the traditional power generation method has caused certain damage to the environment, the microgrid system composed of renewable energy has been widely developed and applied. This paper studies distributed power sources including photovoltaics, wind turbines, energy storage systems, gas turbines, and fuel cells. Under the conditions of microgrid islands and grid-connected operation, the fuel cost, operation and maintenance cost, and the electricity transaction cost between the microgrid and the distribution network, establish the optimal objective function for the operating cost of the microgrid. At the same time, due to the standard moth-flame optimization algorithm having low optimization accuracy and are easy to fall into local optimal solution, an improved moth-flame optimization algorithm based on Sine mapping and Gaussian mutation is proposed. This algorithm is used to obtain the output of each distributed power source and total operating cost in a dispatch period. Finally, an example is used to verify the effectiveness and economy of the proposed model and the improved algorithm.
{"title":"Optimal dispatching of microgrid based on improved moth-flame optimization algorithm based on sine mapping and Gaussian mutation","authors":"Yu Zhang, Peng Wang, Hongwan Yang, Qi Cui","doi":"10.1080/21642583.2022.2042424","DOIUrl":"https://doi.org/10.1080/21642583.2022.2042424","url":null,"abstract":"Because the traditional power generation method has caused certain damage to the environment, the microgrid system composed of renewable energy has been widely developed and applied. This paper studies distributed power sources including photovoltaics, wind turbines, energy storage systems, gas turbines, and fuel cells. Under the conditions of microgrid islands and grid-connected operation, the fuel cost, operation and maintenance cost, and the electricity transaction cost between the microgrid and the distribution network, establish the optimal objective function for the operating cost of the microgrid. At the same time, due to the standard moth-flame optimization algorithm having low optimization accuracy and are easy to fall into local optimal solution, an improved moth-flame optimization algorithm based on Sine mapping and Gaussian mutation is proposed. This algorithm is used to obtain the output of each distributed power source and total operating cost in a dispatch period. Finally, an example is used to verify the effectiveness and economy of the proposed model and the improved algorithm.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"2021 11","pages":"115 - 125"},"PeriodicalIF":4.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41310808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-28DOI: 10.1080/21642583.2022.2039321
Hong Jianwang, R. Ramírez-Mendoza
This paper shows our new contributions on data driven model predictive control, such as persistent excitation, optimal state feedback controller, output predictor and stability. After reviewing the definition of persistent excitation and its important property, the idea of data driven is introduced in model predictive control to construct our considered data driven model predictive control, whose state information and output variable are generated by measured data online. Variation tool is applied to obtain the optimal controller or predictive controller through our own derivation. Furthermore, for the cost function in data driven model predictive control, its preliminary stability is analysed by using the linear matrix inequality and one single optimal state feedback controller is given. To bridge the gap between our derived results and other control strategies, output predictor is constructed from the point of data driven idea, i.e. using some collected input–output data from one experiment to establish the output predictor at any later time instant. Finally, one simulation example is given to prove the efficiency of our derived results.
{"title":"Synthesis analysis for data driven model predictive control*","authors":"Hong Jianwang, R. Ramírez-Mendoza","doi":"10.1080/21642583.2022.2039321","DOIUrl":"https://doi.org/10.1080/21642583.2022.2039321","url":null,"abstract":"This paper shows our new contributions on data driven model predictive control, such as persistent excitation, optimal state feedback controller, output predictor and stability. After reviewing the definition of persistent excitation and its important property, the idea of data driven is introduced in model predictive control to construct our considered data driven model predictive control, whose state information and output variable are generated by measured data online. Variation tool is applied to obtain the optimal controller or predictive controller through our own derivation. Furthermore, for the cost function in data driven model predictive control, its preliminary stability is analysed by using the linear matrix inequality and one single optimal state feedback controller is given. To bridge the gap between our derived results and other control strategies, output predictor is constructed from the point of data driven idea, i.e. using some collected input–output data from one experiment to establish the output predictor at any later time instant. Finally, one simulation example is given to prove the efficiency of our derived results.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"79 - 89"},"PeriodicalIF":4.1,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42542256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-28DOI: 10.1080/21642583.2022.2040060
Hamiden Abd El-Wahed Khalifa, Pavan Kumar, S. Alodhaibi
This research article aims to study a multi-objective linear fractional programming (FMOLFP) problem having fuzzy random coefficients as well as fuzzy pseudorandom decision variables. Initially, the FMOLFP model is converted to a single objective fuzzy linear programming (FLP) model. Secondly, we show that a fuzzy random optimal solution of an FLP problem is resolved into a class of random optimal solution of relative pseudorandom linear programming (LP) model. As a result, some of theorems show that a fuzzy random optimal solution of a fuzzy pseudorandom LP problem is combined with a series of random optimal solutions of relative pseudorandom LP problems. As an application, the developed approach is implemented to an inventory management problem by taking the parameters as trapezoidal fuzzy numbers, ultimately resulting in a new initiative for modelling real-world problems for optimization. In the last, some numerical examples are introduced to clarify the obtained results and their applicability.
{"title":"Application of fuzzy random-based multi-objective linear fractional programming to inventory management problem","authors":"Hamiden Abd El-Wahed Khalifa, Pavan Kumar, S. Alodhaibi","doi":"10.1080/21642583.2022.2040060","DOIUrl":"https://doi.org/10.1080/21642583.2022.2040060","url":null,"abstract":"This research article aims to study a multi-objective linear fractional programming (FMOLFP) problem having fuzzy random coefficients as well as fuzzy pseudorandom decision variables. Initially, the FMOLFP model is converted to a single objective fuzzy linear programming (FLP) model. Secondly, we show that a fuzzy random optimal solution of an FLP problem is resolved into a class of random optimal solution of relative pseudorandom linear programming (LP) model. As a result, some of theorems show that a fuzzy random optimal solution of a fuzzy pseudorandom LP problem is combined with a series of random optimal solutions of relative pseudorandom LP problems. As an application, the developed approach is implemented to an inventory management problem by taking the parameters as trapezoidal fuzzy numbers, ultimately resulting in a new initiative for modelling real-world problems for optimization. In the last, some numerical examples are introduced to clarify the obtained results and their applicability.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"90 - 103"},"PeriodicalIF":4.1,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49134871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-28DOI: 10.1080/21642583.2022.2040061
Cheng Wang, Feng Gao, Xukai Tan, Wei Xu
The tunnel shaking table model test has many influencing factors, and the test parameters are difficult to meet the strict similarity ratio. There are often large errors in predicting prototypes directly using the similarity ratio derived from the classical similarity theory. In order to improve the prediction accuracy of the tunnel shaking table model test, this article proposes a modified method of the traditional similarity theory. Based on the traditional dimensional analysis method, this method uses a non-direct similarity technique to rebuild the dimensional matrix for the main test parameters, derive a new similarity criterion, and then obtain a new similarity ratio. Different from the traditional similarity ratio which is a certain value, the new similarity ratio varies with dynamic parameters, which is more consistent with the actual situation. The tunnel shaking table model test and numerical simulation are carried out to verify the method. Experiments show that the modified method is superior to the traditional similarity theory in numerical prediction accuracy.
{"title":"A modified method for improving the prediction accuracy of the tunnel shaking table model test based on non-direct similarity technique","authors":"Cheng Wang, Feng Gao, Xukai Tan, Wei Xu","doi":"10.1080/21642583.2022.2040061","DOIUrl":"https://doi.org/10.1080/21642583.2022.2040061","url":null,"abstract":"The tunnel shaking table model test has many influencing factors, and the test parameters are difficult to meet the strict similarity ratio. There are often large errors in predicting prototypes directly using the similarity ratio derived from the classical similarity theory. In order to improve the prediction accuracy of the tunnel shaking table model test, this article proposes a modified method of the traditional similarity theory. Based on the traditional dimensional analysis method, this method uses a non-direct similarity technique to rebuild the dimensional matrix for the main test parameters, derive a new similarity criterion, and then obtain a new similarity ratio. Different from the traditional similarity ratio which is a certain value, the new similarity ratio varies with dynamic parameters, which is more consistent with the actual situation. The tunnel shaking table model test and numerical simulation are carried out to verify the method. Experiments show that the modified method is superior to the traditional similarity theory in numerical prediction accuracy.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"104 - 114"},"PeriodicalIF":4.1,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45400873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-27DOI: 10.1080/21642583.2022.2047125
Lin Xu, Jiaqiang Du, Baoye Song, Maoyong Cao
Trajectory tracking is a critical problem in the field of mobile robotics. In this paper, a control scheme combined with backstepping and fractional-order PID is developed for the trajectory tracking of the differential-drive mobile robot. The kinematic and dynamic models of the mobile robot are described in detail for the trajectory tracking controller design. Then, based on the model of the mobile robot, the design of the trajectory tracking control system is addressed by combining backstepping with fractional-order PID. Moreover, to obtain an optimal control system, an improved beetle swarm optimization algorithm is presented to tune the parameters of the kinematic and dynamic controllers simultaneously. Finally, several simulations are implemented to the trajectory tracking of mobile robots in the cases with and without skidding and sliding, and the results can confirm the effectiveness and superiority of the combined control scheme. Abbreviations: FOPID: fractional-order PID; FOPD: fractional-order PD; DDMR:differential-drive mobile robot; BAS: beetle antennae search; BA: beetle antennae; PSO:particle swarm optimization; BSO: beetle swarm optimization.
{"title":"A combined backstepping and fractional-order PID controller to trajectory tracking of mobile robots","authors":"Lin Xu, Jiaqiang Du, Baoye Song, Maoyong Cao","doi":"10.1080/21642583.2022.2047125","DOIUrl":"https://doi.org/10.1080/21642583.2022.2047125","url":null,"abstract":"Trajectory tracking is a critical problem in the field of mobile robotics. In this paper, a control scheme combined with backstepping and fractional-order PID is developed for the trajectory tracking of the differential-drive mobile robot. The kinematic and dynamic models of the mobile robot are described in detail for the trajectory tracking controller design. Then, based on the model of the mobile robot, the design of the trajectory tracking control system is addressed by combining backstepping with fractional-order PID. Moreover, to obtain an optimal control system, an improved beetle swarm optimization algorithm is presented to tune the parameters of the kinematic and dynamic controllers simultaneously. Finally, several simulations are implemented to the trajectory tracking of mobile robots in the cases with and without skidding and sliding, and the results can confirm the effectiveness and superiority of the combined control scheme. Abbreviations: FOPID: fractional-order PID; FOPD: fractional-order PD; DDMR:differential-drive mobile robot; BAS: beetle antennae search; BA: beetle antennae; PSO:particle swarm optimization; BSO: beetle swarm optimization.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"134 - 141"},"PeriodicalIF":4.1,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43279947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-04DOI: 10.1080/21642583.2022.2031335
Yan-Hong Liu, Fengling Huang, Hua Yang
In this paper, the congestion control for named data networking (NDN) is studied. A novel dynamic content store-based congestion control strategy is proposed on account of the characteristic of in-network cache in NDN. A queuing network model is constructed to judge whether congestion occurs. If the network has the tendency of congestion or the congestion happened, the buffer of the output queue is dynamically expanded by borrowing NDN content store (CS), and the forwarding rates of data packets and corresponding interest packets are reduced so as to prevent or alleviate network congestion. In order to reflect fairness, the CS to be borrowed by the data output queue in the port is calculated in terms of the data output queue length and its weight. The simulation results based on ndnSIM show that the given scheme improves the bottleneck link utilization and maintains a low packet loss rate and average flow completion time.
{"title":"A fair dynamic content store-based congestion control strategy for named data networking","authors":"Yan-Hong Liu, Fengling Huang, Hua Yang","doi":"10.1080/21642583.2022.2031335","DOIUrl":"https://doi.org/10.1080/21642583.2022.2031335","url":null,"abstract":"In this paper, the congestion control for named data networking (NDN) is studied. A novel dynamic content store-based congestion control strategy is proposed on account of the characteristic of in-network cache in NDN. A queuing network model is constructed to judge whether congestion occurs. If the network has the tendency of congestion or the congestion happened, the buffer of the output queue is dynamically expanded by borrowing NDN content store (CS), and the forwarding rates of data packets and corresponding interest packets are reduced so as to prevent or alleviate network congestion. In order to reflect fairness, the CS to be borrowed by the data output queue in the port is calculated in terms of the data output queue length and its weight. The simulation results based on ndnSIM show that the given scheme improves the bottleneck link utilization and maintains a low packet loss rate and average flow completion time.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"73 - 78"},"PeriodicalIF":4.1,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46182474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-12DOI: 10.1080/21642583.2021.2024099
Jian Luo, Bingyou Liu, Pan Yang, Xuan Fan
This paper proposes an improved zero-mean normalization sum of squared differences (ZNSSD) algorithm to solve the problem of the inability of traditional structural measurement to extract high-frequency vibration signals. In the proposed technique, the high-speed image sequence of target vibration is captured by a high-speed camera. Then, the ZNSSD template matching algorithm with subpixel accuracy is introduced to process the captured images in the computer. Additionally, a modified search algorithm, the ZNSSD template matching algorithm based on image pyramid (ZNSSD-P), is proposed to significantly reduce the computation time and increase efficiency. Then, a jumping ZNSSD template matching algorithm based on image pyramid (J-ZNSSD-P) is proposed to further improve the efficiency of the ZNSSD-P algorithm. Vibration signals were extracted with Grating Ruler Motion Platform and sound barriers. Results show that the vibration signal extraction method has high precision and efficiency.
{"title":"High-speed vision measurement of vibration based on an improved ZNSSD template matching algorithm","authors":"Jian Luo, Bingyou Liu, Pan Yang, Xuan Fan","doi":"10.1080/21642583.2021.2024099","DOIUrl":"https://doi.org/10.1080/21642583.2021.2024099","url":null,"abstract":"This paper proposes an improved zero-mean normalization sum of squared differences (ZNSSD) algorithm to solve the problem of the inability of traditional structural measurement to extract high-frequency vibration signals. In the proposed technique, the high-speed image sequence of target vibration is captured by a high-speed camera. Then, the ZNSSD template matching algorithm with subpixel accuracy is introduced to process the captured images in the computer. Additionally, a modified search algorithm, the ZNSSD template matching algorithm based on image pyramid (ZNSSD-P), is proposed to significantly reduce the computation time and increase efficiency. Then, a jumping ZNSSD template matching algorithm based on image pyramid (J-ZNSSD-P) is proposed to further improve the efficiency of the ZNSSD-P algorithm. Vibration signals were extracted with Grating Ruler Motion Platform and sound barriers. Results show that the vibration signal extraction method has high precision and efficiency.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"43 - 54"},"PeriodicalIF":4.1,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45918599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-12DOI: 10.1080/21642583.2021.2024916
H. Su, Chenchen Zhou, Yi Cao, Shuang-hua Yang, Zuzhen Ji
ABSTRACT Self-optimizing control (SOC) is a technique for selecting appropriate controlled variables (CVs) and maintaining them constant such that the plant runs at its best. Some tough challenges in this subject, such as how to select CVs when the active constraint set changes remains unsolved since the notion of SOC was presented. Previous work had some drawbacks such as structural complexity and control inaccuracy when dealing with constrained SOC problems due to the elaborate control structures or the limitation of local SOC. In order to overcome the deficiency of previous methods, this paper developed a constrained global SOC (cgSOC) approach to implement self-optimizing controlled variable selection and control structure design. The constrained variables that may change between inactive and active are represented as a nonlinear function of available measurement variables under optimal operations. The unknown function is then intelligently learnt over the whole operating region through neural network training. The difference between the nonlinear function and the actual constrained variables measured in real-time is then used as CVs. When the CVs are controlled at zero in real-time, near-optimal operation can be ensured globally whenever active constraint changes. The efficacy of the proposed approach is demonstrated through an evaporator case study.
{"title":"An intelligent approach of controlled variable selection for constrained process self-optimizing control","authors":"H. Su, Chenchen Zhou, Yi Cao, Shuang-hua Yang, Zuzhen Ji","doi":"10.1080/21642583.2021.2024916","DOIUrl":"https://doi.org/10.1080/21642583.2021.2024916","url":null,"abstract":"ABSTRACT Self-optimizing control (SOC) is a technique for selecting appropriate controlled variables (CVs) and maintaining them constant such that the plant runs at its best. Some tough challenges in this subject, such as how to select CVs when the active constraint set changes remains unsolved since the notion of SOC was presented. Previous work had some drawbacks such as structural complexity and control inaccuracy when dealing with constrained SOC problems due to the elaborate control structures or the limitation of local SOC. In order to overcome the deficiency of previous methods, this paper developed a constrained global SOC (cgSOC) approach to implement self-optimizing controlled variable selection and control structure design. The constrained variables that may change between inactive and active are represented as a nonlinear function of available measurement variables under optimal operations. The unknown function is then intelligently learnt over the whole operating region through neural network training. The difference between the nonlinear function and the actual constrained variables measured in real-time is then used as CVs. When the CVs are controlled at zero in real-time, near-optimal operation can be ensured globally whenever active constraint changes. The efficacy of the proposed approach is demonstrated through an evaporator case study.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"65 - 72"},"PeriodicalIF":4.1,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48703847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-12DOI: 10.1080/21642583.2021.2024100
Wenming Cao, Zhiwen Yu, H. Wong
In this paper, we propose a framework to generate diverse clustering solutions and conduct solution retrieval to improve performance. Specifically, we first project unlabelled data from multiple domains into a shared space while preserving the respective semantics. This space allows that representations of samples in a hard domain are recovered by a linear combination of those of others in the easy domains. Meanwhile, a clustering algorithm is adopted to provide pseudo labels for a conditional generative adversarial network to synthesize representations that in turn promote the learning of the above space. Second, we conduct the joint learning of feature projection and partition matrices on batches of representations, where the former ones are considered as clustering solutions and input into another generative adversarial network to generate more solutions. Third, we utilize the fusion of diffusion to effectively retrieve and extract the knowledge in multiple solutions to obtain the final clustering. We perform comparative experiments against other methods on multiple benchmark data sets. Experimental results demonstrate the effectiveness and superiority of our proposed method.
{"title":"GAN-based clustering solution generation and fusion of diffusion","authors":"Wenming Cao, Zhiwen Yu, H. Wong","doi":"10.1080/21642583.2021.2024100","DOIUrl":"https://doi.org/10.1080/21642583.2021.2024100","url":null,"abstract":"In this paper, we propose a framework to generate diverse clustering solutions and conduct solution retrieval to improve performance. Specifically, we first project unlabelled data from multiple domains into a shared space while preserving the respective semantics. This space allows that representations of samples in a hard domain are recovered by a linear combination of those of others in the easy domains. Meanwhile, a clustering algorithm is adopted to provide pseudo labels for a conditional generative adversarial network to synthesize representations that in turn promote the learning of the above space. Second, we conduct the joint learning of feature projection and partition matrices on batches of representations, where the former ones are considered as clustering solutions and input into another generative adversarial network to generate more solutions. Third, we utilize the fusion of diffusion to effectively retrieve and extract the knowledge in multiple solutions to obtain the final clustering. We perform comparative experiments against other methods on multiple benchmark data sets. Experimental results demonstrate the effectiveness and superiority of our proposed method.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"24 - 42"},"PeriodicalIF":4.1,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41693096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-08DOI: 10.1080/21642583.2021.2024915
Simin Li, Shuang-hua Yang, Yi Cao, Zuzhen Ji
Data-driven method has gained its popularity in fault detection. Conventional methods are associated with one-single-layer process monitoring. Information extracted by such a method may not be sufficient to detect some faults for complicated process systems. Inspired by the deep learning conception, a multi-layer fault detection method, namely Deep Principal Component Analysis (DePCA) was proposed previously in the literature. DePCA has the capability to extract deep features for a process resulting in better fault detection performance. However, it assumes that the value of the variable at each moment is unrelated, which is not suitable for complex nonlinear dynamic system. To address the concerns, by adopting dynamic PCA to extract dynamic features, a new deep approach, namely Deep Dynamic Principal Component Analysis (DeDPCA), is proposed. In the new approach, both Dynamic feature and nonlinear feature can be extracted in different layers so that more process faults can be detected. A Tennessee Eastman process case study was then employed for application and validation of the DeDPCA, which indicates the proposed method is suitable for monitoring complex dynamic nonlinear processes.
数据驱动方法在故障检测中得到了广泛的应用。传统方法与单层过程监控相关联。用这种方法提取的信息可能不足以检测复杂过程系统的某些故障。在深度学习概念的启发下，已有文献提出了一种多层故障检测方法，即深度主成分分析(deep Principal Component Analysis, DePCA)。DePCA能够为过程提取深层特征，从而获得更好的故障检测性能。但是，该方法假定各时刻变量的值是不相关的，不适合复杂的非线性动力系统。为了解决这些问题，采用动态主成分分析方法提取动态特征，提出了一种新的深度方法——深度动态主成分分析(deep dynamic Principal Component Analysis, DeDPCA)。该方法可以同时提取不同层次的动态特征和非线性特征，从而检测出更多的过程故障。以田纳西州伊士曼过程为例，对该方法进行了应用和验证，结果表明该方法适用于复杂动态非线性过程的监测。
{"title":"Nonlinear dynamic process monitoring using deep dynamic principal component analysis","authors":"Simin Li, Shuang-hua Yang, Yi Cao, Zuzhen Ji","doi":"10.1080/21642583.2021.2024915","DOIUrl":"https://doi.org/10.1080/21642583.2021.2024915","url":null,"abstract":"Data-driven method has gained its popularity in fault detection. Conventional methods are associated with one-single-layer process monitoring. Information extracted by such a method may not be sufficient to detect some faults for complicated process systems. Inspired by the deep learning conception, a multi-layer fault detection method, namely Deep Principal Component Analysis (DePCA) was proposed previously in the literature. DePCA has the capability to extract deep features for a process resulting in better fault detection performance. However, it assumes that the value of the variable at each moment is unrelated, which is not suitable for complex nonlinear dynamic system. To address the concerns, by adopting dynamic PCA to extract dynamic features, a new deep approach, namely Deep Dynamic Principal Component Analysis (DeDPCA), is proposed. In the new approach, both Dynamic feature and nonlinear feature can be extracted in different layers so that more process faults can be detected. A Tennessee Eastman process case study was then employed for application and validation of the DeDPCA, which indicates the proposed method is suitable for monitoring complex dynamic nonlinear processes.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"55 - 64"},"PeriodicalIF":4.1,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43602054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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