1
|
Cheng L, Lu H, Yan S, Liu C, Qiao J, Qi J, Cheng W, Zhang Y, Zhang Y. An Aging Small-Signal Model for Degradation Prediction of Microwave Heterojunction Bipolar Transistor S-Parameters Based on Prior Knowledge Neural Network. Micromachines (Basel) 2023; 14:2023. [PMID: 38004880 PMCID: PMC10673388 DOI: 10.3390/mi14112023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 10/25/2023] [Accepted: 10/28/2023] [Indexed: 11/26/2023]
Abstract
In this paper, an aging small-signal model for degradation prediction of microwave heterojunction bipolar transistor (HBT) S-parameters based on prior knowledge neural networks (PKNNs) is explored. A dual-extreme learning machine (D-ELM) structure with an adaptive genetic algorithm (AGA) optimization process is used to simulate the fresh S-parameters of InP HBT devices and the degradation of S-parameters after accelerated aging, respectively. In addition to the reliability parametric inputs of the original aging problem, the S-parameter degradation trend obtained from the aging small-signal equivalent circuit is used as additional information to inject into the D-ELM structure. Good agreement was achieved between measured and predicted results of the degradation of S-parameters within a frequency range of 0.1 to 40 GHz.
Collapse
Affiliation(s)
- Lin Cheng
- Key Laboratory for Wide Band Gap Semiconductor Materials and Devices of Education Ministry, School of Microelectronics, Xidian University, Xi’an 710071, China; (L.C.); (H.L.); (S.Y.); (J.Q.); (J.Q.); (Y.Z.); (Y.Z.)
| | - Hongliang Lu
- Key Laboratory for Wide Band Gap Semiconductor Materials and Devices of Education Ministry, School of Microelectronics, Xidian University, Xi’an 710071, China; (L.C.); (H.L.); (S.Y.); (J.Q.); (J.Q.); (Y.Z.); (Y.Z.)
| | - Silu Yan
- Key Laboratory for Wide Band Gap Semiconductor Materials and Devices of Education Ministry, School of Microelectronics, Xidian University, Xi’an 710071, China; (L.C.); (H.L.); (S.Y.); (J.Q.); (J.Q.); (Y.Z.); (Y.Z.)
| | - Chen Liu
- Key Laboratory for Wide Band Gap Semiconductor Materials and Devices of Education Ministry, School of Microelectronics, Xidian University, Xi’an 710071, China; (L.C.); (H.L.); (S.Y.); (J.Q.); (J.Q.); (Y.Z.); (Y.Z.)
| | - Jiantao Qiao
- Key Laboratory for Wide Band Gap Semiconductor Materials and Devices of Education Ministry, School of Microelectronics, Xidian University, Xi’an 710071, China; (L.C.); (H.L.); (S.Y.); (J.Q.); (J.Q.); (Y.Z.); (Y.Z.)
| | - Junjun Qi
- Key Laboratory for Wide Band Gap Semiconductor Materials and Devices of Education Ministry, School of Microelectronics, Xidian University, Xi’an 710071, China; (L.C.); (H.L.); (S.Y.); (J.Q.); (J.Q.); (Y.Z.); (Y.Z.)
| | - Wei Cheng
- Science and Technology on Monolithic Integrated Circuits and Modules Laboratory, Nanjing Electronic Devices Institute, Nanjing 210016, China
| | - Yimen Zhang
- Key Laboratory for Wide Band Gap Semiconductor Materials and Devices of Education Ministry, School of Microelectronics, Xidian University, Xi’an 710071, China; (L.C.); (H.L.); (S.Y.); (J.Q.); (J.Q.); (Y.Z.); (Y.Z.)
| | - Yuming Zhang
- Key Laboratory for Wide Band Gap Semiconductor Materials and Devices of Education Ministry, School of Microelectronics, Xidian University, Xi’an 710071, China; (L.C.); (H.L.); (S.Y.); (J.Q.); (J.Q.); (Y.Z.); (Y.Z.)
| |
Collapse
|
2
|
Wu W, Wang C, Bian W, Hua B, Gomez JY, Orme CJ, Tang W, Stewart FF, Ding D. Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cell with Interfacial Electrical Sensors Using Data-Driven Machine Learning. Adv Sci (Weinh) 2023; 10:e2304074. [PMID: 37632697 PMCID: PMC10602546 DOI: 10.1002/advs.202304074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Indexed: 08/28/2023]
Abstract
Protonic ceramic electrochemical cells (PCECs) offer promising paths for energy storage and conversion. Despite considerable achievements made, PCECs still face challenges such as physiochemical compatibility between componenets and suboptimal solid-solid contact at the interfaces between the electrolytes and electrodes. In this study, a novel approach is proposed that combines in situ electrochemical characterization of interfacial electrical sensor embedded PCECs and machine learning to quantify the contributions of different cell components to total degradation, as well as to predict the remaining useful life. The experimental results suggest that the overpotential induced by the oxygen electrode is 48% less than that of oxygen electrode/electrolyte interfacial contact for up to 1171 h. The data-driven machine learning simulation predicts the RUL of up to 2132 h. The root cause of degradation is overpotential increase induced by oxygen electrode, which accounts for 82.9% of total cell degradation. The success of the failure diagnostic model is demonstrated by its consistency with degradation modes that do not manifest in electrolysis fade during early real operations. This synergistic approach provides valuable insights into practical failure diagnosis of PCECs and has the potential to revolutionize their development by enabling improved performance prediction and material selection for enhanced durability and efficiency.
Collapse
Affiliation(s)
- Wei Wu
- Energy & Environmental Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| | - Congjian Wang
- Nuclear Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| | - Wenjuan Bian
- Energy & Environmental Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| | - Bin Hua
- Energy & Environmental Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| | - Joshua Y. Gomez
- Energy & Environmental Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| | - Christopher J. Orme
- Energy & Environmental Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| | - Wei Tang
- Energy & Environmental Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| | - Frederick F. Stewart
- Energy & Environmental Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| | - Dong Ding
- Energy & Environmental Science and TechnologyIdaho National LaboratoryIdaho FallsID83415USA
| |
Collapse
|
3
|
Liu Y, Liu Y, Yang Y. Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks. Entropy (Basel) 2023; 25:1316. [PMID: 37761615 PMCID: PMC10527696 DOI: 10.3390/e25091316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/30/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023]
Abstract
Contact fatigue is one of the most common failure forms of typical basic components such as bearings and gears. Accurate prediction of contact fatigue performance degradation trends in components is conducive to the scientific formulation of maintenance strategies and health management of equipment, which is of great significance for industrial production. In this paper, to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional networks (TCNs) was proposed. Firstly, a multi-domain and high-dimensional feature set of vibration signals was constructed, and performance degradation indexes with good sensitivity and strong trends were initially screened using comprehensive evaluation indexes. Secondly, the kernel principal component analysis (KPCA) method was used to eliminate redundant information among multi-domain features and construct health indexes (HIs) based on a convolutional autoencoder (CAE) network. Then, the performance degradation trend prediction model based on TCN was constructed, and the degradation trend prediction for the monitored object was realized using direct multi-step prediction. On this basis, the effectiveness of the proposed method was verified using a bearing common-use data set, and it was successfully applied to performance degradation trend prediction for rolling contact fatigue specimens. The results show that using KPCA can reduce the feature set from 14 dimensions to 4 dimensions and retain 98.33% of the information in the original preferred feature set. The method of constructing the HI based on CAE is effective, and change processes versus time of the constructed HI can truly reflect the degradation process of rolling contact fatigue specimen performance; this method has obvious advantages over the two commonly used methods for constructing HIs including auto-encoding (AE) networks and gaussian mixture models (GMMs). The model based on TCN can accurately predict the performance degradation of rolling contact fatigue specimens. Compared with prediction models based on long short-term memory (LSTM) networks and gating recurrent units (GRUs), the model based on TCN has better performance and higher prediction accuracy. The RMS error and average absolute error for a prediction step of 3 are 0.0146 and 0.0105, respectively. Overall, the proposed method has universal significance and can be applied to predict the performance degradation trend of other mechanical equipment/parts.
Collapse
Affiliation(s)
| | | | - Yan Yang
- College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China; (Y.L.); (Y.L.)
| |
Collapse
|
4
|
Hu Y, Zhang L, Jiang Y, Peng K, Jin Z. A Hybrid Method for Performance Degradation Probability Prediction of Proton Exchange Membrane Fuel Cell. Membranes (Basel) 2023; 13:426. [PMID: 37103853 PMCID: PMC10142057 DOI: 10.3390/membranes13040426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/08/2023] [Accepted: 04/10/2023] [Indexed: 06/19/2023]
Abstract
The proton exchange membrane fuel cell (PEMFC) is a promising power source, but the short lifespan and high maintenance cost restrict its development and widespread application. Performance degradation prediction is an effective technique to extend the lifespan and reduce the maintenance cost of PEMFC. This paper proposed a novel hybrid method for the performance degradation prediction of PEMFC. Firstly, considering the randomness of PEMFC degradation, a Wiener process model is established to describe the degradation of the aging factor. Secondly, the unscented Kalman filter algorithm is used to estimate the degradation state of the aging factor from monitoring voltage. Then, in order to predict the degradation state of PEMFC, the transformer structure is used to capture the data characteristics and fluctuations of the aging factor. To quantify the uncertainty of the predicted results, we also add the Monte Carlo dropout technology to the transformer to obtain the confidence interval of the predicted result. Finally, the effectiveness and superiority of the proposed method are verified on the experimental datasets.
Collapse
Affiliation(s)
- Yanyan Hu
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China
| | - Li Zhang
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Yunpeng Jiang
- SPIC Digital Technology Co., Ltd., Beijing 100080, China
| | - Kaixiang Peng
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zengwang Jin
- School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Northwestern Polytechnical University, Xi’an 710072, China
| |
Collapse
|
5
|
Zou X, Xu G, Fang P, Li W, Jin Z, Guo S, Hu Y, Li M, Pan J, Sun Z, Yan F. Unsupervised Learning-Guided Accelerated Discovery of Alkaline Anion Exchange Membranes for Fuel Cells. Angew Chem Int Ed Engl 2023; 62:e202300388. [PMID: 36897018 DOI: 10.1002/anie.202300388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/11/2023]
Abstract
Without insight into the correlation between the structure and properties, anion exchange membranes (AEMs) for fuel cells are developed usually using the empirical trial and error method or simulation methods. Here, a virtual module compound enumeration screening (V-MCES) approach, which does not require the establishment of expensive training databases and can search the chemical space containing more than 4.2×105 candidates was proposed. The accuracy of the V-MCES model was considerably improved when the model was combined with supervised learning for the feature selection of molecular descriptors. Techniques from V-MCES, correlating the molecular structures of the AEMs with the predicted chemical stability, generated a ranking list of potential high stability AEMs. Under the guidance of V-MCES, highly stable AEMs were synthesized. With understanding of AEM structure and performance by machine learning, AEM science may enter a new era of unprecedented levels of architectural design.
Collapse
Affiliation(s)
- Xiuyang Zou
- Soochow University, College of Chemistry, RenAi load 199, Suzhou, Jiangsu, China, 215006, Suzhou, CHINA
| | - Guodong Xu
- Soochow University, College of Chemistry, CHINA
| | - Pengda Fang
- Soochow University, College of Chemistry, CHINA
| | - Weizheng Li
- Soochow University, College of Chemistry, CHINA
| | - Zhiyu Jin
- Soochow University, College of Chemistry, CHINA
| | - Siyu Guo
- Soochow University, College of Chemistry, CHINA
| | - Yin Hu
- Soochow University, College of Chemistry, CHINA
| | - Meisheng Li
- Huaiyin Normal University, College of Chemistry, CHINA
| | - Ji Pan
- Soochow University, College of Chemistry, CHINA
| | - Zhe Sun
- Soochow University, College of Chemistry, CHINA
| | - Feng Yan
- Soochow University, Department of Chemistry, 199. Ren'ai Road,Suzhou, 215123, Suzhou, CHINA
| |
Collapse
|
6
|
Aquilina M, Ciantar KG, Galea C, Camilleri KP, Farrugia RA, Abela J. The Best of Both Worlds: A Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks. Sensors (Basel) 2022; 23:419. [PMID: 36617016 PMCID: PMC9823731 DOI: 10.3390/s23010419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/08/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: (A) train standard SR networks on synthetic low-resolution-high-resolution (LR-HR) pairs or (B) predict the degradations of an LR image and then use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information and followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network. We show that a single lightweight metadata insertion block together with a degradation prediction mechanism can allow non-blind SR architectures to rival or outperform state-of-the-art dedicated blind SR networks. We implement various contrastive and iterative degradation prediction schemes and show they are readily compatible with high-performance SR networks such as RCAN and HAN within our framework. Furthermore, we demonstrate our framework's robustness by successfully performing blind SR on images degraded with blurring, noise and compression. This represents the first explicit combined blind prediction and SR of images degraded with such a complex pipeline, acting as a baseline for further advancements.
Collapse
Affiliation(s)
- Matthew Aquilina
- Department of Communications & Computer Engineering, Faculty of ICT, University of Malta, MSD2080 Msida, Malta
- Deanery of Molecular, Genetic & Population Health Sciences, University of Edinburgh, Edinburgh EH9 3DW, UK
| | - Keith George Ciantar
- Department of Communications & Computer Engineering, Faculty of ICT, University of Malta, MSD2080 Msida, Malta
- Ascent, 90/3, Alpha Centre, Tarxien Road, LQA1815 Luqa, Malta
| | - Christian Galea
- Department of Communications & Computer Engineering, Faculty of ICT, University of Malta, MSD2080 Msida, Malta
| | - Kenneth P. Camilleri
- Department of Systems & Control Engineering, Faculty of Engineering, University of Malta, MSD2080 Msida, Malta
| | - Reuben A. Farrugia
- Department of Communications & Computer Engineering, Faculty of ICT, University of Malta, MSD2080 Msida, Malta
| | - John Abela
- Department of Computer Information Systems, Faculty of ICT, University of Malta, MSD2080 Msida, Malta
| |
Collapse
|
7
|
Xia Z, Wang Y, Ma L, Zhu Y, Li Y, Tao J, Tian G. A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM. Sensors (Basel) 2022; 23:s23010166. [PMID: 36616764 PMCID: PMC9824588 DOI: 10.3390/s23010166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/30/2022] [Accepted: 12/14/2022] [Indexed: 05/12/2023]
Abstract
Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method.
Collapse
Affiliation(s)
- Zetao Xia
- Ningbo Innovation Center, Zhejiang University, Ningbo 315000, China
| | - Yining Wang
- Ningbo Innovation Center, Zhejiang University, Ningbo 315000, China
| | - Longhua Ma
- School of Information Science and Engineering, NingboTech University, Ningbo 315000, China
| | - Yang Zhu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Yongjie Li
- School of Information Science and Engineering, NingboTech University, Ningbo 315000, China
| | - Jili Tao
- School of Information Science and Engineering, NingboTech University, Ningbo 315000, China
| | - Guanzhong Tian
- Ningbo Innovation Center, Zhejiang University, Ningbo 315000, China
- Correspondence:
| |
Collapse
|
8
|
Hu Y, Wei R, Yang Y, Li X, Huang Z, Liu Y, He C, Lu H. Performance Degradation Prediction Using LSTM with Optimized Parameters. Sensors (Basel) 2022; 22:s22062407. [PMID: 35336579 PMCID: PMC8949053 DOI: 10.3390/s22062407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/01/2022] [Accepted: 03/10/2022] [Indexed: 11/16/2022]
Abstract
Predicting the degradation of mechanical components, such as rolling bearings is critical to the proper monitoring of the condition of mechanical equipment. A new method, based on a long short-term memory network (LSTM) algorithm, has been developed to improve the accuracy of degradation prediction. The model parameters are optimized via improved particle swarm optimization (IPSO). Regarding how this applies to the rolling bearings, firstly, multi-dimension feature parameters are extracted from the bearing’s vibration signals and fused into responsive features by using the kernel joint approximate diagonalization of eigen-matrices (KJADE) method. Then, the between-class and within-class scatter (SS) are calculated to develop performance degradation indicators. Since network model parameters influence the predictive accuracy of the LSTM model, an IPSO algorithm is used to obtain the optimal prediction model via the LSTM model parameters’ optimization. Finally, the LSTM model, with said optimal parameters, was used to predict the degradation trend of the bearing’s performance. The experiment’s results show that the proposed method can effectively identify the trends of degradation and performance. Moreover, the predictive accuracy of this proposed method is greater than that of the extreme learning machine (ELM) and support vector regression (SVR), which are the algorithms conventionally used in degradation modeling.
Collapse
Affiliation(s)
- Yawei Hu
- College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China; (Y.H.); (X.L.); (Z.H.); (C.H.)
| | - Ran Wei
- Anhui NARI Jiyuan Electric Co., Ltd., Hefei 230601, China;
| | - Yang Yang
- China North Vehicle Research Institute, Beijing 100071, China
- Correspondence: (Y.Y.); (Y.L.)
| | - Xuanlin Li
- College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China; (Y.H.); (X.L.); (Z.H.); (C.H.)
| | - Zhifu Huang
- College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China; (Y.H.); (X.L.); (Z.H.); (C.H.)
| | - Yongbin Liu
- College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China; (Y.H.); (X.L.); (Z.H.); (C.H.)
- Correspondence: (Y.Y.); (Y.L.)
| | - Changbo He
- College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China; (Y.H.); (X.L.); (Z.H.); (C.H.)
| | - Huitian Lu
- JJL College of Engineering, South Dakota State University, Brookings, SD 57007, USA;
| |
Collapse
|