Predict Performance Degradation Research Articles

Life prediction model and performance degradation of lithium-ion battery under different cut-off voltages

Life prediction model and performance degradation of lithium-ion battery under different cut-off voltages

A proton exchange membrane fuel cells degradation prediction method based on multi-scale temporal information merging network

Performance degradation prediction is considered as an effective method to improve the service life of proton exchange membrane fuel cells (PEMFCs). A hybrid method Multi-scale Temporal Information Merging Network (MTIMN) is then proposed for PEMFC degradation prediction, which is designed to merge information from both macro and micro perspectives in PEMFC historical working data. Firstly, a data preprocessing method based on P-M scores and min-max wavelet denoising is designed to eliminate redundant variables and noise considering the structure of PEMFC and data statistics. Secondly, aiming at the voltage recovery phenomenon during the aging process, a Multi-scale Exponential Decomposition Encoding Module (MEDEM) is presented to extract and encode different time scales of sequences from the PEMFC degradation data. Thirdly, a two-layer Historical Information Integration Module (HIIM) is established, which consists of the trend extraction module and the local extraction module, to extract deep dynamic nonlinear characteristics. Finally, the Merging Prediction Module (MPM) merges information from trend and local modules for the final output. The experimental results show that Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are 0.0107 and 0.0079, respectively. Therefore, MTIMN can effectively extract deep dynamic nonlinear characteristics in PEMFC degradation data and improve prediction accuracy greatly.

A deep learning method based on CNN-BiGRU and attention mechanism for proton exchange membrane fuel cell performance degradation prediction

The performance of proton exchange membrane fuel cells (PEMFCs) will gradually deteriorate during long-term operation. Accurate performance degradation prediction is crucial for extending the lifespan and improve the durability of fuel cells. This paper proposes a deep learning method (CNN-BiGRU-AM) that incorporates convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU) and attention mechanism (AM) for fuel cell degradation prediction. In the proposed method, CNN extracts complex features from the input data through convolutional operations, BiGRU models temporal information by considering both forward and reverse directions of the input sequence, and attention mechanism highlights key information in the input data through weight allocation. The proposed method is validated using long-term experimental data from fuel cells under steady-state and quasi-dynamic conditions. The results indicate that the absolute error of the proposed method is less than 1.2 mV for 97.94% of the data samples under steady-state conditions and less than 1.2 mV for 94.82% of the data samples under quasi-dynamic conditions. The prediction accuracy and stability of the proposed method are significantly improved compared to other deep learning prediction methods.

Preface

It is with great pleasure and a sense of accomplishment that we present the proceedings of the 2024 4th International Conference on Mechatronics, Automation and Intelligent Control (MAIC 2024). This conference builds upon the rich legacy of its predecessors, each of which has contributed significantly to advancing knowledge and fostering collaboration in mechatronics, automation, and intelligent control. The MAIC series has consistently attracted top researchers, scholars, and practitioners from around the globe, making it a prestigious and influential event in the mechanical engineering and automation domains. The 2024 edition is no exception, as it has gathered an impressive array of experts who have come together to share their latest findings, discuss emerging trends, and explore innovative solutions. MAIC 2024 is not only a focused display of the latest research results in mechatronics, automation, and intelligent control, but also an important platform to promote the development and application of relevant disciplines and domains globally. It attracts about 100 delegates from all over the world, including some international scholars, which reflects the internationalization and extensiveness of the fields of mechatronics, automation, and intelligent control. The event is packed with a diverse and engaging agenda, comprising keynote speeches, oral presentations, poster sessions, and academic discussions. During the conference, participants had in-depth discussions and exchanges on the latest progress and trends in mechanical engineering and automation domains. In the keynote speech session, a number of leading authorities working in related fields shared their research results and insightful perspectives, providing participants with valuable academic insights and set the tone for the conference. Professor Xuedao Shu (Ningbo University, China) explained the rolling forming technology and its advantages, and analyzed the technical characteristics of different rolling forming parts in detail; Professor Xingjian Wang (Beihang University, China) presented a fault diagnosis and performance degradation prediction of electro-hydraulic proportional servo valve; Professor Haiquan Zhao (Southwest Jiaotong University, China) proposed a novel adaptive active noise control system using an online genetic algorithm by recasting ANC as an optimization problem. During the oral presentations and poster sessions, participants gained more opportunities to demonstrate their research results and interacted directly with each other, which promoted the collision of academic ideas and deepened the cooperation and exchange. This collection of accepted papers covers a wide range of aspects such as machinery and machine design, advanced forming manufacturing and equipment, fault detection and diagnosis in mechatronics systems, automatic control technology, sensor and detection technology, etc., which fully demonstrates the latest research trends and practical achievements in the fields of mechanical engineering and automation. It not only captures the essence of the conference discussions and presentations, but also serves as a valuable resource for researchers, practitioners, and policymakers alike. The successful organization of MAIC 2024 not only promotes academic exchanges and technical progress in related fields, but also strengthens international cooperation and ties. And the international influence of the conference has been significantly enhanced, which has injected new vitality and impetus for the future development of mechanical engineering and automation areas. Finally, we extend our heartfelt gratitude to all the authors, reviewers, sponsors, organizers, and participants who made MAIC 2024 a truly remarkable event. Meanwhile, we also expect that the MAIC series of conferences will continue to play an important role in promoting international academic exchanges and technical cooperation, and contribute to the prosperity and development of mechatronics, automation, and intelligent control. Committee of MAIC 2024 List of Committee Member is available in this Pdf.

Review of rolling bearings performance degradation trend prediction

Rolling bearings are widely used in rotating machinery in modern industry, and ensuring their stability during operation is one of the prerequisites for the overall safety of the equipment. Predicting performance degradation can play a key role in preventing accidents and extending equipment life. With the development of big data and deep learning, more trend prediction methods are emerging in the field of performance degradation prediction of rolling bearings. Therefore, this paper reviews the evaluation indicators and performance degradation prediction models for rolling bearing performance degradation prediction. The advantages and disadvantages of physical degradation indicators, and virtual degradation indicators are analyzed. It is presented to utilize the powerful feature self-extraction ability and nonlinear function characterization ability of deep learning methods to construct bearing evaluation indicators. It also analyzes the research progress of traditional performance degradation prediction models and deep learning prediction models. In this review, future developments in rolling bearing performance degradation prediction are summarized in this paper as deep learning-based, digital twin correlation, high dimensionality, and adaptive, which guide researchers and practitioners to effectively identify suitable performance degradation prediction models.

Prediction method for re-damage behavior of the repaired CFRP bolted joint

Composite materials are widely used in various aircraft due to their high specific strength, stiffness, corrosion resistance, and robust design flexibility. However, when subjected to complex loading conditions, composite structures are susceptible to damage, necessitating prompt repair upon aircraft return. Analyzing the re-damage process of repaired structures is crucial for assessing aircraft structural reliability, lifespan, and determining whether a repeat flight is warranted. In this study, a prediction method was proposed for re-damage behavior through meso-modeling and energy analysis of the repaired composite bolted joint. First, this article established a meso-mechanical model for composite laminates, analyzed force transfer characteristics at the fiber − matrix interface, and derived equations for stress field. Additionally, a strain energy distribution model was developed for composite bolted repaired joint near fibers and repaired area, proposing a damage prediction method based on the strain energy criterion. This method could anticipate the location, type, sequence, and magnitude of damage. Finally, simulation calculations yielded mechanical characteristics of the repaired area under typical load conditions, while loading tests verified the feasibility of the prediction method. This study offered an effective means of predicting performance degradation in composite structures and provided a vital direction for optimal spacecraft structure repairing methods and parameters.

A data-driven method with sample entropy and CEEMDAN for short-term performance degradation prediction of dynamic hydrogen fuel cells

Short-term degradation prediction plays a crucial role in enhancing the reliability, safety, and cost-effectiveness of hydrogen fuel cells. A data-driven method for short-term degradation prediction is proposed based on Sample Entropy (SE), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and a varying-depth Gated Recurrent Unit (GRU). CEEMDAN decomposes the voltage sequence into Intrinsic Mode Functions (IMFs), which are then reconstructed into three meaningful combined Intrinsic Mode Functions (co-IMFs) using SE. The varying-depth GRU is employed to predict these co-IMFs. Results show that under dynamic conditions, the proposed method achieves an RMSE of 0.0012 for 1-h ahead predictions and 0.0151 for 35-h ahead predictions. Under static conditions, the RMSE for 1-h ahead is 0.0012, and for 35-h ahead prediction, it is 0.0097. Comparative analysis with hybrid models using the same dataset reveals that this novel approach demonstrates superior accuracy and efficiency.

High accuracy and adaptability of PEMFC degradation interval prediction with Informer-GPR under dynamic conditions

Proton exchange membrane fuel cells (PEMFCs) are pivotal components within green energy systems; However, commercialization and large-scale application of these techniques are constrained by the performance degradation prediction problem. Existing prediction methods mainly focus on the performance degradation under static and quasi-dynamic conditions, yet point estimation uncertainty quantification and interval estimation under dynamic conditions would contribute to safe and efficient operation. In this research, a fusion method named Informer-GPR is proposed, which accurately quantifies the uncertainty of performance degradation prediction and demonstrates good adaptability under various dynamic conditions and prediction step sizes. This method utilizes Informer as a point estimation method to circumvent the deficiency of global information extraction in recurrent neural networks and employs sparse self-attention mechanisms to allocate weights, enhancing the extraction of crucial information in the aging process. Furthermore, gaussian process regression (GPR) is employed to quantify the uncertainty of point estimation process, providing safer confidence interval estimation. Experimental findings demonstrate that the Informer-GPR method reduces the RMSE of point estimation by 20.8 %–64.4 % on dynamic cycle condition datasets, offering precise confidence interval estimations. Moreover, its robust performance across diverse dynamic conditions and multiple prediction steps underscores its versatility, thereby enhancing prediction efficacy in dynamic scenarios.

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Performance degradation prediction of rolling bearing based on temporal graph convolutional neural network

Performance degradation prediction of rolling bearing based on temporal graph convolutional neural network

Prediction of IGBT Gate Oxide Layer's Performance Degradation Based on MultiScaleFormer Network.

Insulated gate bipolar transistors (IGBTs) are widely used in power electronic devices, and their health prediction problems have attracted much attention in the field of power electronic equipment health management. The performance degradation of IGBT gate oxide is one of the most important failure modes. In order to analyze this failure mechanism and the ease of implementation of a monitoring circuit, the gate leakage current of IGBTs was selected as the fault precursor parameter for the degradation of their gate oxide performance, and feature selection and fusion were carried out by using time domain characteristic analysis, grayscale correlation, Mahalanobis distance, Kalman filter, and other methods. Thus, a health indicator was obtained to characterize the degradation of IGBT performance, which was used to indicate the degree of aging of the IGBT gate oxide layer. In this paper, we propose an improved degradation prediction model called MultiScaleFormer, inspired by advanced design ideas of the iTransformer network architecture, combined with the health parameters of IGBTs to construct a degradation prediction model for the IGBT gate oxide layer. MultiScaleFormer showed the highest fitting accuracy compared with the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), CNN-LSTM, and Transformer models in our experiment. The mean absolute error (MAE) of the MultiScaleFormer prediction was as low as 0.0087. Extraction of the health indicator and the construction and verification of the degradation prediction model were carried out on the dataset released by the NASA-Ames Laboratory. These results demonstrate the feasibility of the gate leakage current as a fault precursor parameter for IGBT gate oxide failure, and the feasibility and accuracy of the MultiScaleFormer prediction model for IGBT performance degradation.

Bridge performance degradation model based on the multi-variate bayesian dynamic linear method

The degradation of bridge structural performance arises from the combined influence of various factors. Performance assessment and reliable prediction of bridge performance degradation through effective utilizing of detection information updates is a challenging problem. In this paper, the concept of performance indicators is redefined, employing to delineate bridge performance degradation. A bridge performance degradation model (the error ≤8%) is formulated, considering the multiple-variable Bayesian dynamic linear method (MBDLM) and revealing the coupling mechanisms among factors influencing bridge performance degradation. On this basis, the prediction performance of the model is quantitatively evaluated by three metrics: mean squared error, predictive mean squared error and mean absolute percentage error. A methodology is presented for the assessment, prediction, and maintenance reinforcement of in-service bridge structural performance degradation. This approach holds promise for future applications in safety assessments and the decision-making process for preventive maintenance of operational bridges.

A model with high-precision on proton exchange membrane fuel cell performance degradation prediction based on temporal convolutional network-long short-term memory

Prediction with high-precision of the performance degradation is crucial for the development of proton exchange membrane fuel cells (PEMFCs) with long lifetime. In this work, a new hybrid neural network model on fuel cell performance degradation prediction (TCN-LSTM) is developed by incorporating the advantages of temporal convolutional network (TCN) and long short-term memory (LSTM). The innovation of TCN-LSTM is that LSTM captures the long-term correlation of data from the high-level features of the original data extracted by TCN. On the available PEMFC datasets (IEEE 2014 PHM Data Challenge), the TCN-LSTM reduces the root mean square error (RMSE) by up to 94% compared with other state-of-the-art algorithms. In addition, the TCN-LSTM is also assessed on 2673 h self-tested fuel cell voltage degradation dataset recorded under dynamic cyclic load conditions with complicated decay mechanism. The RMSE is only 0.00205 even under the 50% training length, which further verifies the excellent prediction performance of TCN-LSTM. It can be concluded that the TCN-LSTM will be a promising fuel cell performance degradation prediction model.

A multi-scale fuel cell degradation prediction method based on isometric convolution block and long short-term memory networks

Performance degradation prediction is considered an effective method to enhance the durability of Proton Exchange Membrane Fuel Cells (PEMFCs). However, accurate prediction under dynamic conditions remains a challenge. The MSD-ICB-LSTM model is proposed to improve the accuracy of the performance degradation prediction, which incorporates the information from both long-term and short-term modes within the PEMFC history operating data. Firstly, redundant variables are removed from the data, and the noise is eliminated using locally weighted scatterplot smoothing (LOWESS) to raise the quality of the input samples. Secondly, the Multi-Scale Decomposition (MSD) method is used to decompose the pre-processed data into long-term and short-term modes. Thirdly, a combined module containing encoding, internal decomposition and a double-layer Isometric Convolution Block (ICB) is used to deeply explore the nonlinear degradation characteristics in the short-term mode, while a Long-Short-Term Memory Network (LSTM) is utilized to estimate the long-term mode. The final result of prediction is obtained by combining the predictions of two modes. The experimental results show that the prediction accuracy of the proposed MSD-ICB-LSTM model is 99.74% in R2，and the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are 0.0034 and 0.77%, respectively. Thus, the method can efficiently explore the multi-scale features and enhance the accuracy of PEMFC performance degradation prediction.

Traffic Flow Prediction by an Ensemble Framework with Data Denoising and Functional Principal Components Analysis

Accurate traffic flow data are important for congestion identification and real-time traffic control. Raw traffic flow data may be disturbed by different noises during the acquisition process, which leads to the degradation of model prediction performance. To address this problems, this paper proposes a data denoising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with wavelet threshold denoising (WTD) to suppress potential outliers in the data, and then introduces functional principal component analysis to accomplish the traffic forecasting task. We use the proposed framework for real traffic flow prediction and validate the prediction performance of the proposed framework using root mean square error and mean absolute error. In the prediction performance comparison, five denoising methods are considered: empirical mode decomposition (EMD), ensemble empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, ICEEMDAN, and WTD. The prediction results show that the model combined with the denoising method outperforms the model without the denoising method. ICEEMDAN combined with WTD’s denoising method improves prediction performance more than other methods. Furthermore, the WTD method with dmey type achieves higher accuracy than other types.

Performance degradation model and life prediction method of spool pair caused by erosion wear

The erosion of solid particles in the fluid causes the wear of the metering edge of the spool pair, resulting in a decrease in system performance. This paper aims to develop a general method to evaluate performance degradation and life prediction of spool pairs induced by erosion. Considering the diversity of influencing factors and the complexity of operating conditions, traditional numerical calculation methods will consume huge computational costs. It is difficult to qualitatively and quantitatively analyze the evolution of metering edge by numerical calculation. The erosion prediction model (EPM) constructed by machine learning methods can be used to predict the wear profile discretized in time and space. Combined with the mathematical model of the valve-controlled cylinder, a performance degradation model (PDM) that can simulate real-time working conditions is established. Through the data exchange and iterative update between EPM and PDM, a complete digital platform for the calculation of slide valve durability is constructed. After comparing with the experimental results, it can be concluded that the proposed method can realize the performance degradation evaluation and life prediction of the spool pair caused by erosion wear under continuous working conditions.

Dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction

As a critical component of Intelligent Transportation Systems (ITS), traffic flow prediction is indispensable for vehicle routing and transportation management. However, traffic prediction is an intractable task due to the complex spatial–temporal dependencies. First, traffic data has multiple spatial dependencies that arise from different contexts and vary with time. Second, multi-scale temporal features naturally exist in the traffic data and have dynamic significance for predicting future traffic states. Insufficiently capturing such spatial and temporal dependencies will lead to the prediction performance degradation. To this end, we propose a dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction. Unlike existing methods, we propose a novel attention layer assigning weights to each feature of nodes to augment the dynamic spatial dependence modeling and a dynamic multi-branch temporal network learning temporal features from the time domain and frequency domain complementary to enhance the dynamic multi-scale temporal features learning capability. Extensive experiments on four well-known traffic datasets demonstrate that the proposed model outperforms the best baselines with a ratio of 5.27%, 2.44%, 3.27%, and 7.34% in terms of mean absolute error and also has superior long-term prediction performance than state-of-the-art baselines.

Degradation and polarization curve prediction of proton exchange membrane fuel cells: An interpretable model perspective

Balancing the performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) is a significant challenge. Current data-driven technologies mainly focus on improving the accuracy of performance degradation prediction. However, these methods are often considered as black-box models, where the internal workings of the model are in an unknown state, making it challenging to explain the relationship of each feature with performance/durability. To address this challenge, a novel interpretable model for PEMFC performance degradation and polarization curve is proposed, incorporating feature selection interpretation and black-box model interpretation. The key innovation of this paper lies in transforming the black-box model into a dual interpretable transparent model both before and during the learning process. Additionally, the prediction and interpretation of PEMFC polarization curves are performed. The Boruta algorithm is employed to interpret the significance of each feature's impact on the model, filtering out key feature variables. Based on the Boruta-selected feature variables, the SHapley Additive exPlanations (SHAP) algorithm is used to calculate the marginal contribution of each numerical value to the predicted target. This provides an in-depth explanation of the internal workings of the model, analyzing how each predictive factor influences the model results. Through Boruta feature analysis, it is discovered that the cathode characteristics of PEMFC have a greater impact on performance degradation than anode characteristics. The SHAP algorithm interpretative analysis identifies the optimal flow rates corresponding to the best performance for the cathode hydrogen flow rate and anode oxygen flow rate as 0.090 to 0.13 NLPM and 0.39 to 0.50 NLPM, respectively. This indicates that the algorithmic framework has a certain physical meaning in explaining PEMFC performance degradation.

A CNN-BiLSTM-Attention approach for EHA degradation prediction based on time-series generative adversarial network

As a representative integrated system for power-by-wire (PBW) systems, Electro-hydrostatic actuator (EHA) has series of advantages such as high power density, compactness, and high efficiency, which is one of the important development directions of future hydraulic system field. However, due to its high integration and high reliability requirements, it is challenging to conduct degradation studies in short period of time with limited data samples. For this type of high integrated mechatronics system, Prognostics and Health Management (PHM) is one of the key works to ensure its safety and reliability, especially the performance degradation prediction presented in this paper. To deal with the small size of EHA data, a time-based data enhancement method for expanding the performance data set is proposed based on Time Generative Adversarial Network (TimeGAN). Considering the complex of working state and system performance, the relationship between the EHA operation data and its health indicator is then analyzed using the CNN-BiLSTM-Attention model, so as to generate the health indicator combine with TimeGAN synthesis data. Finally, CNN-BiLSTM-Attention model with multi-input channels is developed, and EHA data as well as TimeGAN synthesized EHA data are incorporated into the model. The results show that this method can greatly improve the prediction accuracy of EHA performance, and provide a novel method for performance degradation prediction of integrated mechatronic system.

Covid-19 induced data shift causes degradation of predictive model performance in perioperative medicine

Covid-19 induced data shift causes degradation of predictive model performance in perioperative medicine