Real-time Operational Data Research Articles

MSDG: Multi-Scale Dynamic Graph Neural Network for Industrial Time Series Anomaly Detection

A large number of sensors are typically installed in industrial plants to collect real-time operational data. These sensors monitor data with time series correlation and spatial correlation over time. In previous studies, GNN has built many successful models to deal with time series data, but most of these models have fixed perspectives and struggle to capture the dynamic correlations in time and space simultaneously. Therefore, this paper constructs a multi-scale dynamic graph neural network (MSDG) for anomaly detection in industrial sensor data. First, a multi-scale sliding window mechanism is proposed to input different scale sensor data into the corresponding network. Then, a dynamic graph neural network is constructed to capture the spatial–temporal dependencies of multivariate sensor data. Finally, the model comprehensively considers the extracted features for sequence reconstruction and utilizes the reconstruction errors for anomaly detection. Experiments have been conducted on three real public datasets, and the results show that the proposed method outperforms the mainstream methods.

Optimizing Maintenance of Energy Supply Systems in City Logistics with Heuristics and Reinforcement Learning

In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We model this problem as a multi-period team orienteering problem. To address this multi-period challenge, we propose a dual approach: a novel reinforcement learning (RL) framework and a biased-randomized heuristic algorithm. The RL-based method dynamically learns from real-time operational data and evolving asset conditions, adapting to changes in asset health and failure probabilities to optimize decision making. In addition, we develop and apply a biased-randomized heuristic algorithm designed to provide effective solutions within practical computational limits. Our approach is validated through a series of computational experiments comparing the RL model and the heuristic algorithm. The results demonstrate that, when properly trained, the RL-based model is able to offer equivalent or even superior performance compared to the heuristic algorithm.

Long-distance transmission conductor condition sensing based on distributed fiber optic sensing technology

Abstract The existing long-distance transmission line perception mainly focuses on the measurement and analysis of electrical parameters. When the line is subject to wind vibration, icing or galloping, the changes of electrical parameters are not obvious and difficult to capture, resulting in poor performance of long-distance transmission line fault state perception. In this regard, the long-distance transmission line condition sensing based on distributed optical fiber sensing technology is studied. This method designs corresponding sensing methods for four working conditions: using the back Brillouin scattering sensor and phase sensitive Rayleigh scattering sensor in the optical fiber sensing technology to form a multi parameter distributed optical fiber sensing device, which is used to sense the surface temperature, vibration, strain and other data of the long-distance transmission line; By analyzing the linear relationship between fiber Brillouin frequency shift and Brillouin power and fiber strain and temperature, the sensing results of icing thickness of long-distance transmission lines are obtained; By calculating the amplitude and phase information of the detection signal, the sensing results of long-distance transmission line vibration are obtained; By calculating the time difference of polarization mutation signal at both ends of long-distance transmission line, combined with the propagation speed of optical signal and the length of long-distance transmission line, the perception result of lightning fault location of long-distance transmission line can be obtained. The experimental results show that this method can more accurately collect the real-time operation data of long-distance transmission lines, and can effectively perceive the galloping, wind vibration and lightning fault location of long-distance transmission lines.

Insights on the Optimization of Short- and Long-Term Maintenance Decisions for Floating Offshore Wind Using Nested Genetic Algorithms

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices.

State Evaluation of Electrical Equipment in Substations Based on Data Mining

This paper explores the combination of a data mining-based state evaluation method for electrical equipment in substations, analyzing the effectiveness and accuracy. First, a Gaussian mixture model is applied to fit all raw data of electrical equipment. The Expectation Maximization algorithm summarizes the data distribution characteristics and identifies outliers. The a priori algorithm is then employed for data mining to derive frequent itemsets and association rules between equipment quality and measurement data. For new equipment samples, conditional probabilities of each feature are independently calculated and combined to classify and evaluate equipment quality. The results suggest that equipment reliability in smart substations can be inferred from historical and real-time operational data using improved association rule algorithms and Naive Bayes classifiers. Finally, the proposed method was applied to analyze statistical data from a 110 kV substation of a power supply company. The states prediction accuracy exceeded 95% when compared with actual equipment quality. The effectiveness evaluation metrics demonstrated that this method outperforms single-category algorithms in terms of accuracy and discrimination ability.

Model for the Failure Prediction Mechanism of In-Service Pipelines Based on IoT Technology

With the rapid increase in pipeline mileage in China, the accurate prediction of corrosion issues in in-service pipelines has become crucial for ensuring safe pipeline operation. Traditional pipeline leakage monitoring methods are significantly limited by human factors and equipment precision, making it challenging to predict and identify leakage points accurately. Therefore, aligned with the trend of intelligent pipeline development, this study aims to construct a failure pressure prediction mechanism model for corroded pipelines based on IoT technology. This model leverages intelligent sensing and prediction to assess the safety status of corroded pipeline sections. Ultrasonic phased array technology detects specific corrosion points and detailed defect parameters within pipeline sections. The parameters are then utilized in the Simdroid domestic finite element analysis model to simulate the ultimate burst pressure of the pipeline. A single-variable approach is employed to analyze the sensitivity of different parameters to the pipeline’s ultimate burst pressure, with the minimum burst pressure point of multi-point corroded sections selected as the overall segment failure pressure. Finite element simulation data are integrated into a neural network database to predict the pipeline failure pressure. The real-time operational data of the pipeline are monitored using negative-pressure wave sensing. The operational pressure of the corroded points is compared with the algorithm-predicted failure pressure; if the values approach a critical threshold, an alarm is triggered. Moreover, the remote control terminals evaluate the pipeline’s self-rescue time, providing a buffer for pipeline leakage self-rescue. The failure prediction mechanism model for in-service pipelines was applied to the Fujian–Guangdong branch of the West–East Gas Pipeline III to verify its accuracy and feasibility. The research results offer technical support for the maintenance and emergency repair of pipeline leakage scenarios, leveraging intelligent pipeline technology to reduce costs and increase the efficiency of pipeline operations, thereby supporting the sustainable development of China’s oil and gas pipelines with theoretical and technical backing.

Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA–WISP approach

Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers’ models, and the industry’s intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decision-making, IVFF–PIPRECIA and IVFF–WISP techniques. IVFF–PIPRECIA determines criteria importance weights, whereas IVFF–WISP identifies optimal alternatives. A case study in the textile industry validates the framework’s practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model’s credibility. The approach effectively handles decision-making uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.

Data-Driven Management: The Impact of Big Data Analytics on Organizational Performance

Inevitably, in such a fast-moving landscape of big data analytics lies the transformative opportunity for any organization to unlock new avenues of growth, operational efficiency, and strategic decision-making. This paper contributes a comprehensive methodology that will help businesses take advantage of big data analytics to secure a continued competitive advantage. At the core of this methodology is to have a robust data governance framework that will establish the security, integrity, and accessibility of the enterprise data assets by specifying relevant policies, processes, and technologies. This, in turn, within such a framework, allows state-of-the-art AI-driven anomaly detection mechanisms for encryption and access control to implement protection measures around sensitive information while enabling secure yet efficient data utilization. The methodology also continues its approach of putting in place an integrated data ecosystem that brings together different pockets or sources of data, such as real-time operation data, customer interaction, and unstructured information. A critical component of this methodology is putting in place the advanced predictive analytics capabilities that could be run based on tapping into the power of machine learning algorithms; by doing so, the organization will be predicting market trends, customer behavior, and risks highly accurately. This will be a very proactive way of making decisions that would let the business efficiently use the available resources, innovate products and services ahead of time, and create a distinctly competitive advantage. The transformative ability of this methodology for big data analytics opened new channels toward growth and innovation and firmly established the organization as an industry leader with long-lasting competitive advantage. The place of data-driven insight, data culture, and responsible data practice has been the key to success in this organization.

Design and Development of Fractional Order Convolutional Neural Network Based Fractional Order Nonlinear Reactor Power Simulator for CANDU-PHWR

A highly complex nonlinear Reactor Regulating System (RRS) of Canadian Deuterium Uranium Pressurized Heavy Water Reactor (CANDU-PHWR) based Nuclear Power Plant (NPP) simulated in the present research. The internal design of RRS is secured and vendor controlled which is embedded in AC-132 Programmable Logic Controller (PLC). Therefore, the problem of the identification of the RRS controller model is addressed. A data-driven Fractional Order Nonlinear MIMO Hammerstein Model (FO-NC-MIMO-HM) of NPP is identified using an Adaptive Immune Algorithm (AIA) based on a Global Search Strategy (GSS) and Auxiliary Model Recursive Least Square Method (AMRLSM). Parameters of FO-MIMO-HM are identified using Innovative Real-Time Plant Operational Data (IRTPOD). The original PLC-based controller is replaced with a new Fractional Order Convolutional Neural Network (FO-CNN) based Fractional Order Nonlinear Controller (FO-NC). Therefore, a visual Simulator is developed for detailed modeling, control, simulation, and analysis of the proposed design scheme for RRS in Visual Basic (VB) Software. The performance of the proposed design scheme is tested and validated for different modes of RRS against benchmark data obtained from Plant Data Recorder (PDR) and found in close agreement well within the design bounds.

Comprehensive Review of Machine Learning Techniques for Condition-Based Maintenance

While most industrial maintenance strategies are centered on optimizing machine runtime and cost reduction, the condition-based maintenance (CBM) strategy distinguishes itself from others in its use of real-time operational data from machines to help engineers make informed decisions. The introduction of machine learning (ML) into a CBM strategy can increase its effectiveness, enabling more accurate predictions and making the decision-making process more efficient. In this review paper, we seek to provide a comprehensive overview of the role ML plays in modern CBM systems, beginning by outlining the core concepts and historical development of CBM and briefly introducing various ML techniques being employed in industry today. We then review numerous real-world cases where ML-based CBM systems have been implemented and discuss some of the technological, human, and ethical challenges faced by organizations seeking to integrate sophisticated ML models into existing CBM systems. We end by highlighting some of the current limitations of ML-based CBM systems, paving the way for a discussion on emerging trends and future research directions in this area.

Medical Equipment Management System Based on the Concept of Whole Life Cycle

Strengthen the legal, compliant, and rational use of medical equipment and further guide the rationalization of medical behaviors. By utilizing the Internet of Things (IoT) and image analysis technology, collect real-time operation data of the equipment, establish a real-time running database for medical equipment, and cooperate with the 12 key links of the "whole life" of the equipment and the 8+6 management system framework to implement lean management of the efficiency, benefit, and effectiveness of medical equipment usage. It realizes the improvement of the quality and efficiency of medical equipment, cost reduction and cost control, and provides data support for scientific decision-making. This study innovates the management model for the entire life cycle of medical equipment, providing a scientific approach to the management of hospital equipment.

Thermal efficiency and performance analysis of 50 MW concentrated solar power plant

This study evaluates the operational efficiency and performance of the Shagaya 50 MW Concentrated Solar Power (CSP) plant in Kuwait that has been operational since February 2019. Utilizing Parabolic Trough technology, the plant incorporates a large Solar Field (SF) comprising 8 platforms with total of 206 solar-collector loops. Thermal energy captured by the SF is utilized in a Low Temperature Rankine Cycle to generate electricity via water-steam cycle and heat exchangers, with a nominal turbine capacity of 50 MW. Supported by a thermal energy storage (TES) system capable of storing 1200 MWht energy in molten salt. Through a comprehensive assessment spanning 41 months, real-time operational data is analysed to evaluate the CSP-TES system's efficacy within Kuwait's climatic conditions. Various performance parameters are examined, providing insights into CSP technology's practical implications and operational strategies. Driven by the annual difference in downtime and TES availability, comparative analysis of the plant's annual electricity production over the three years reveals notable variations: 74.99 GWh in 2019, 160.62 GWh in 2020, and 140.04 GWh in 2021, corresponding to capacity factors of 19 %, 40.75 %, and 35.53 %, respectively. The findings underscore the strong correlation between the SF and TES system in determining overall plant efficiency. The study also highlights the impact of high wind speeds on plant operation, triggering shutdowns on 9 days during the assessment period. Additionally, soiling losses in the SF are assessed through daily reflectivity measurements, guiding cleaning efforts using semi-automated armed trucks. Seasonal peaks in cleanliness factor coincide with local spring season, underlining the detrimental effect of reflectivity losses on plant output due to higher dust mass densities. A design flaw causing frequent breakdowns of receiver tubes under high winds is identified and addressed.

Intelligent manufacturing: bridging the gap between the Internet of Things and machinery to achieve optimized operations

The access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities.

Performance evaluation and optimization of the cascade refrigeration system based on the digital twin model

Cascade refrigeration systems (CRSs) have become the priority choice in low-temperature applications, causing severe energy waste due to the lack of practical control strategies. Currently, refrigeration simulations are mainly based on the simplified thermodynamic or Artificial Intelligence (AI) model. However, the former is insufficiently accurate, while the latter is of unclear thermodynamic significance. By connecting the physical and digital space, the digital twin technology provides exciting opportunities for combining thermodynamic and AI methods. In this paper, an integrated digital twin model for the CRS is innovatively proposed based on the modified semi-empirical model of the twin-screw refrigeration compressor. As a major update, the identification and update method of characteristic parameters is developed based on real-time operating data to ensure performance consistency between digital and physical twins. By comparing with the simplified model, results show that the digital twin model can improve prediction accuracy by 7.42%–23.62%. Founded on the proposed model, the thermodynamic performance of the CRS is evaluated. To facilitate on-site applications, the definition of flat intermediate temperature is given, which is fitted to a control correlation with several easily observed variables as the inputs. Taking a food factory as a case study, simulation results reveal that the optimum intermediate temperature should be significantly lower than the original, and the coefficient of performance (COP) has an average improvement potential of 9.1%. By deploying the optimum strategy under the on-site condition, a superior energy-saving rate of up to 13.1% is attained according to test reports.

Dynamic Modeling of Heat Exchangers Based on Mechanism and Reinforcement Learning Synergy

The current lack of a high-precision, real-time model applicable to the control optimization process of heat exchange systems, especially the difficulty in determining the overall heat transfer coefficient K of heat exchanger operating parameters in real time, is a prominent issue. This paper mainly unfolds the following work: 1. We propose a dynamic model for the control and optimization of the heat exchanger operation. By constructing a system to collect real-time operating data on the flow rates and temperatures on both sides of the heat exchanger, the parameter identification of the overall heat transfer coefficient K is performed. Subsequently, by combining this with mechanistic equations, a novel heat exchanger model is established based on the fusion of mechanistic principles and reinforcement learning. 2. We validate the new model, where the average relative error between the model’s temperature output values and the actual measured values is below 5%, indicating the high identification accuracy of the model. Moreover, under variations in the temperature and flow rate, the overall heat transfer coefficient K demonstrates the correct patterns of change. 3. To further enhance the model’s identification accuracy, a study on the reward functions in reinforcement learning is conducted. A model with the Logarithmic Mean Temperature Difference (LMTD) as the reward function exhibits a high identification accuracy. However, upon comparison, a model using the Arithmetic Mean Temperature Difference (AMTD) for relative error as the reward function shows an even higher identification accuracy. The model is validated under various operating conditions, such as changes in the flow rate on the hot side, demonstrating good scalability and applicability. This research contributes to providing a high-precision dynamic parameter basis for the precise control of heat exchange systems, offering significant guidance for the control optimization of actual heat exchange system operations.

Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning

Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development for Industry 4.0. The digital twin receives real-time operational data to monitor the asset in the digital domain. It performs real-time processing, data analysis, and machine learning to predict optimized decisions. In the era of next-generation mobile networks, IoT devices can communicate and perform their remote operations in a timely manner. In smart greenhouse technology, the digital twin could be a revolutionary substitute for real-time remote monitoring and process management. However, there has been limited work on digital twin-driven smart greenhouse technology. In this paper, a process management framework is developed that can be interpreted as a machine learning and cloud-based data-driven digital twin for smart greenhouses. The proposed framework consists of three layers: the physical, fog, and cloud layers. The physical greenhouse measurements are monitored using a highly immersive cloud-based, real-time 3D environment. We present an example architecture using commercial cloud and open-source tools to verify the proof of concept. Additionally, different ML techniques are utilized to predict the operational requirements for smart greenhouses.

Multi-sensor data fusion framework for energy optimization in smart homes

Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic savings, safety, and security in smart homes. However, certain challenges such as real-time operational data for each micro-moment, proper application of data fusion techniques, and end-to-end computing and deployment architecture prevent the establishment of an effective energy-efficient framework to provide personalized energy-saving recommendations. This work presents energy management for smart spaces (EMSS), the proposed energy efficiency framework implemented in an edge-cloud computing platform that fuses data from heterogeneous data sources (environmental sensors, camera, plug data, etc.) at appropriate data fusion levels and process them to generate actionable, explainable, personalized, and persuasive recommendations at the right moment. The user response to the generated recommendations triggers the actuators to perform respective energy-saving actions and provide more personalized future recommendations. Further, SMARTHome - a data generation framework based on configurable scenario files and a set of software codes was proposed to generate synthetic data with respect to different building types and micro-moments. The functionalities of the EMSS (device and user registration), user dashboard, analytics, and energy-saving recommendations were made accessible to the user through web and mobile applications. The validation analysis of the EMSS was performed by (i) comparative analysis of the machine learning and deep learning algorithms used by the decision engine to generate energy-saving recommendations and (ii) benchmarking of EMSS based on the taxonomy of data fusion-based energy efficiency frameworks for smart homes.

Optimization of Real-Time Operational Data Monitoring Through Installing The Device on the Unit

Coal is the largest natural resource in Indonesia and has a vital role in global energy in various industries. PT Putra Perkasa Abadi (PPA), a mining contractor in South Kalimantan, faces problems with manual reporting being late and prone to errors. This project aims to speed up the digitization of the reporting process by providing PPA Team devices in the Heavy Equipment (A2B) and Support units. This is in line with the commitment of the PPA Mining Work Safety, Environment and Quality (K3PLM) Policy. This implementation starts from observation and data collection by selecting SS6 devices and creating designs for each unit. Continue with device installation, analyze problems, and repair installation. Following, carry out trials and socialize the use of the device to operators. With the implementation of the PPA Team application on the SS6 Monitor, Operators at PPA can independently input data on operational activities, including check-in / check-out units. It will reduce paper in operational reporting by providing an SS6 Monitor device. In addition, cost calculations can be adjusted to actual data, and operators can monitor unit Hour Meter (HM) data and operational activities directly on the SS6 Monitor. This reduces the duration of data input time, but also reduces the intensity of crowding on communication radios, and minimizes data input errors. Therefore, optimizing operational monitoring at PPA by installing devices on units can increase efficiency, including HM input for cost calculations, efficient unit HM reporting, fast check-in / check-out processes, digital unit breakdown reporting for more efficient communication, rapid digital validation of P2H, and moving from paper forms to digital tools for more sustainable work practices.

Data-driven urban rail vehicle critical parts maintenance system based on cloud-edge collaboration

In view of the limitations of traditional maintenance methods for rail vehicles, this study proposes a data-driven maintenance system for critical parts of rail vehicles based on a cloud-side collaborative framework. Currently, there are several major issues with the maintenance of critical parts of rail vehicles, including long maintenance cycles, difficult troubleshooting, high maintenance costs, low maintenance efficiency, irregularities in data management, and a low level of informatization. To address these problems, a cloud-edge collaboration approach is adopted. The maintenance information of the vehicles is synchronized and exchanged in real-time with the cloud data center. Sensor and Internet of Things (IoT) technologies are used to collect real-time operational and status data of rail vehicles. The data is then analyzed and modelled in the cloud data center. The system achieves fault prediction and remaining useful life prediction for key components of the rail vehicles by applying machine learning algorithms. The experimental results demonstrate that compared to traditional maintenance methods, the system enables more effective troubleshooting and remaining useful life prediction of critical components of the vehicles. It improves maintenance efficiency and safety while also offering practical application value.

Power Batteries State of Health Estimation of Pure Electric Vehicles for Charging Process

Abstract Under different usage scenarios of various electric vehicles (EVs), it becomes difficult to estimate the battery state of health (SOH) quickly and accurately. This article proposes an SOH estimation method based on EVs’ charging process history data. First, data processing processes for practical application scenarios are established. Then the health indicators (HIs) that directly or indirectly reflect the driver's charging behavior in the charging process are used as the model's input, and the ensemble empirical mode decomposition (EEMD) is introduced to remove the noise brought by capacity regeneration. Subsequently, the maximum information coefficient (MIC)—principal component analysis (PCA) algorithm is employed to extract significant HIs. Eventually, the global optimal nonlinear degradation relationship between HIs and capacity is learned based on Bayesian-optimized Gaussian process regression (BO-GPR). Approximate battery degradation models for practical application scenarios are obtained. This article validates the proposed method from three perspectives: models, vehicles, and regions. The results show that the method has better prediction accuracy and generalization capability and lower computational cost, which provides a solution for future online health state prediction based on a large amount of real-time operational data.