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Shi Y, Wang Z, Du X, Gong B, Lu Y, Li L, Ling G. Membrane Fouling Diagnosis of Membrane Components Based on MOJS-ADBN. MEMBRANES 2022; 12:843. [PMID: 36135861 PMCID: PMC9505124 DOI: 10.3390/membranes12090843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/13/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Given the strong nonlinearity and large time-varying characteristics of membrane component fouling in the membrane water treatment process, a membrane component-membrane fouling diagnosis method based on the multi-objective jellyfish search adaptive deep belief network (MOJS-ADBN) is proposed. Firstly, the adaptive learning rate is introduced into the unsupervised pre-training phase of DBN to improve the convergence speed of the network. Secondly, the MOJS method is used to replace the gradient-based layer-by-layer weight fine-tuning method in traditional DBN to improve the ability of network feature extraction. At the same time, the convergence of the MOJS-ADBN learning process is proven by constructing the Lyapunov function. Finally, MOJS-ADBN is used in the membrane packaging diagnosis to verify the performance of the model diagnosis. The experimental results show that MOJS-ADBN has a fast convergence speed and a high diagnostic accuracy, and can provide a theoretical basis for membrane fouling diagnosis in the actual operation of membrane water treatment.
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Affiliation(s)
- Yaoke Shi
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
| | - Zhiwen Wang
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
- Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China
- National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China
| | - Xianjun Du
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
- Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China
- National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China
| | - Bin Gong
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
| | - Yanrong Lu
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
- Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China
- National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China
| | - Long Li
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
- GS-Unis Intelligent Transportation System & Control Technology Co., Ltd., Lanzhou 730050, China
| | - Guobi Ling
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
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Shi Y, Wang Z, Du X, Gong B, Lu Y, Li L. Membrane fouling diagnosis of membrane components based on multi-feature information fusion. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2022.120670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles Using Parameter Estimation with Recursive Least Squares. BATTERIES-BASEL 2019. [DOI: 10.3390/batteries6010001] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With the increase in usage of electric vehicles (EVs), the demand for Lithium-ion (Li-ion) batteries is also on the rise. The battery management system (BMS) plays an important role in ensuring the safe and reliable operation of the battery in EVs. Sensor faults in the BMS can have significant negative effects on the system, hence it is important to diagnose these faults in real-time. Existing sensor fault detection and isolation (FDI) methods have not considered battery degradation. Degradation can affect the long-term performance of the battery and cause false fault detection. This paper presents a model-based sensor FDI scheme for a Li-ion cell undergoing degradation. The proposed scheme uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters in real time. The estimated ECM parameters are put through weighted moving average (WMA) filters, and then cumulative sum control charts (CUSUM) are implemented to detect any significant deviation between unfiltered and filtered data, which would indicate a fault. The current and voltage faults are isolated based on the responsiveness of the parameters when each fault occurs. The proposed FDI scheme is then validated through conducting a series of experiments and simulations.
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Buciakowski M, Pazera M, Witczak M. A Combined H 2 / H ∞ Approach for Robust Joint Actuator and Sensor Fault Estimation: Application to a DC Servo-Motor System. SENSORS 2019; 19:s19112648. [PMID: 31212718 PMCID: PMC6603540 DOI: 10.3390/s19112648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 05/31/2019] [Accepted: 06/06/2019] [Indexed: 11/25/2022]
Abstract
The main objective of this paper is to develop an actuator and sensor fault estimation framework taking into account various uncertainty sources. In particular, these are divided into three groups: sensor measurement noise, process-external exogenous disturbances, as well as unknown fault dynamics. Unlike the approaches presented in the literature, here they are not processed in the same way but treated separately in a suitably tailored fashion. Finally, the approach resolves to minimizing their effect on the fault estimation error in either the H2 or H∞ sense. As a result, a mixed performance–based actuator fault estimation framework is obtained, along with its convergence conditions. The final part of the paper presents performance analysis results obtained for a DC servo-motor. Subsequently, another three-tank-system-based example is presented. In both cases, the proposed approach is compared with an alternative one, which clearly exhibits its superiority.
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Affiliation(s)
- Mariusz Buciakowski
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.
| | - Marcin Pazera
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.
| | - Marcin Witczak
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.
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Che Mid E, Dua V. Parameter estimation using multiparametric programming for implicit Euler’s method based discretization. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2018.11.032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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