Operation Status Research Articles

Methods for assessing the self-healing properties of asphalt concrete

The article presents the results of a study of the ability of asphalt concrete to independently restore the state of the structure or improve the operational state of the material. The quality indicators that reflect the degree of efficiency of the developed self-healing technology are: the degree of restoration of the operational state of the structure; timeliness of initiation of the self-healing process; the speed of the restoration process, as well as the durability of the operational state after self-healing. The article formulates requirements for new methods for testing the self-healing ability of materials with encapsulated modifiers. It is shown that the self-healing efficiency is significantly higher for asphalt concretes with encapsulated AR polymer than for SMA, which used encapsulated oil. With the optimal content of encapsulated oil, the loss of strength of asphalt concrete samples during repeated compression is 1.4 times less, and for encapsulated AR polymer it is 1.6–2.1 times less. For SMA with encapsulated oil, the failure rate is 1.05, and with encapsulated AR polymer 1.7. The coefficient values reflect that the achievement of the critical value of the strength limit for asphalt concrete with encapsulated AR polymer occurs later by 61.9% than for asphalt concrete with encapsulated oil. The speed of the self-healing process of asphalt concrete using encapsulated oil is 10% faster than asphalt concrete without capsules, and with the use of encapsulated AR polymer – by 23%.

The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

Abstract Modern radial compressors are designed to operate with high performance and a wide range of applications while minimizing their environmental impact. It is necessary to study the machine near the stability limit and gain a comprehensive understanding of the fluid dynamic mechanisms that trigger the instability in the system to extend the operating range at high efficiency. Machine learning aids in developing pattern identification models for detecting compressor instability. In a prior study, a two-stage radial compressor for refrigerant gas underwent extensive simulation, capturing unsteady RANS conditions and generating a substantial dataset of pressure signals from multiple probes. Selected signals, coupled with detailed computational fluid dynamics (CFD) post-processing, revealed fluid dynamic structures near surge conditions. This study utilizes all pressure signals to assess the stage's operational state (stable, transient, or unstable/surge). An additional goal is to develop an algorithm predicting flow patterns in different stage sections with minimal pressure signals at the rotor inlet. This is a preliminary step toward the development of a smart monitoring and diagnostic model for the compressor installed in a plant. The developed model consists of three submodels, trained with CFD results obtained from unsteady Reynolds averaged Navier–Stokes (URANS) simulations. The three submodels are a regression submodule, a classification submodule, and a forecasting algorithm. The regression submodule predicts diffuser static pressure fields, the classification submodule categorizes whether the compressor is in a critical condition or not, and the forecasting algorithm predicts the inducer pressure signals in the future impeller rotations according to the actual operation history. These sub-modules work together to forecast whether the compressor, according to the operation strategy, is going into instability conditions.

A Data-Driven Model for Rapid CII Prediction

The shipping industry plays a crucial role in global trade, but it also contributes significantly to environmental pollution, particularly in regard to carbon emissions. The Carbon Intensity Indicator (CII) was introduced with the objective of reducing emissions in the shipping sector. The lack of familiarity with the carbon performance is a common issue among vessel operator. To address this aspect, the development of methods that can accurately predict the CII for ships is of paramount importance. This paper presents a novel and simplified approach to predicting the CII for ships, which makes use of data-driven modelling techniques. The proposed method considers a restricted set of parameters, including operational data (draft and speed) and environmental conditions, such as wind speed and direction, to provide an accurate prediction of the CII factor. This approach extends the state of research by applying Deep Neural Networks (DNNs) to provide an accurate CII prediction with a deviation of less than 6% over a considered time frame consisting of different operating states (cruising and maneuvering mode). The result is achieved by using a limited amount of training data, which enables ship owners to obtain a rapid estimation of their yearly rating prior to receiving the annual CII evaluation.

Evaluating the Dynamic Comprehensive Resilience of Urban Road Network: A Case Study of Rainstorm in Xi’an, China

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi’an’s evening rush hour. The results reveal that the southwest part of Xi’an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services’ efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management.

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

A novel operational reliability and state prediction method based on degradation hidden Markov model with random threshold

AbstractBearings are widely used as a common mechanical component and significantly impacts the reliability and safety of various engineering equipment. In the field of large‐scale equipment such as wind turbines, statistical data for reliability and state prediction of bearings is usually limited, leading to the inapplicability of traditional statistical methods. Besides, individual differences commonly exist among bearings, and the appropriate failure threshold for a specific bearing is difficult to be determined due to the individual uncertainty, which may induce prediction errors. To address these issues, a novel operational reliability and state prediction method based on random threshold degradation hidden Markov model was proposed in the study. The traditional hidden Markov model was improved by considering the effect of performance degradation on state transition probability matrix. Moreover, random failure thresholds of performance degradation were employed to describe individual differences between bearings. The operational reliability and state of bearings was obtained by comprehensive analysis of prediction results under different failure thresholds. Verification of the proposed operational reliability and state prediction method were conducted using PHM2012 bearing operation data. The results underscored the effectiveness of the proposed method in achieving a comparatively high accuracy in operational reliability prediction without the prerequisite of an exact failure threshold, when contrasted with several established reliability and life prediction techniques.

Operation status monitoring for 500 kV DC circuit breaker with internal failures and relative backup fault isolation schemes

As the primary protection and control device in direct current (DC) power systems, DC circuit breakers (DCBs) have complex structures and numerous components, making it prone to diversified internal failures. This paper explores an operation status monitoring method for DCBs, and relative backup fault isolation schemes and system recovery schemes are proposed to handle cases of DCBs with different internal failures. Contributions of this paper are: (1) An operation status monitoring method is proposed to reflect the failure degrees of DCB, categorizing the statuses of DCB into status-1 (normal), status-2 (minor failure) and status-3 (major failure). (2) According to the failure degrees of DCB, relative backup fault isolation and system recovery schemes are proposed to ensure the fault isolation scope is as small as possible and the system could recovery as fast as possible. Simulations are conducted in PSCAD/EMTDC for validation.

Estimating Operating States of Numerical Control Milling Machines Using Wi-Fi Sensing and Channel State Information

This study investigates whether wi-fi sensing, through channel state information (CSI), can estimate the operating state and the material machined by a numerical control (NC) milling machine without contact sensors. The research aims to develop a predictive system that detects machine tool failures by analyzing fluctuations in wi-fi radio waves and assesses the state of the machine based on changes in wi-fi radio waves. If a machine tool vibrates differently from usual, the system predicts a fault based on historical vibration data. To achieve this, a novel approach is required to predict the operating state of a machine tool using wi-fi sensing. Thus, this study conducted a preliminary evaluation to determine the possibility of estimating the operating state of a machine tool using wi-fi sensing. The experiment involved controlling the NC machine tool to alter its operational state during material processing and measuring the processing state using CSI obtained from a wi-fi module. Results indicated that CSI can estimate the operating and machining states of the milling machine.

Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment

Mining safety heavily depends on ventilation, which constitutes a significant portion of the energy costs in operations. Optimizing mine ventilation systems (MVSO) is crucial for minimizing this energy expenditure. However, current algorithms encounter challenges when applied to large-scale mines, primarily due to the complexity of variables and limited attention to optimizing main fans. This study introduces a theoretical knowledge enhanced genetic algorithm for MVSO, incorporating main fan adjustments. The algorithm models changes in the main fan’s operational status and integrates ventilation network equivalent simplification (VNES) and the minimum spanning tree (MST) to reduce the number of variables in the mine ventilation network. Additionally, leveraging mine ventilation sensitivity theory (MVST) enhances the quality of the initial algorithmic population. A simple case and two engineering cases collectively validated that the algorithm consistently provides effective and reliable optimization solutions for mine ventilation systems across varying scales. Specifically, the algorithm reduced energy consumption from 326.94 to 186.99 kW, 433.14 to 239.48 kW, and 520.53 to 324.90 kW across three different scales of mine ventilation systems. Comparative analysis with four other algorithms shows that, although this algorithm has a longer runtime due to the need to identify the minimum spanning tree during iterations, its ability to reduce problem dimensionality and improve population quality results in more stable and superior convergence performance, especially for large-scale mine ventilation systems.Graphical abstract

Application of Modern Machine Diagnostic Systems to Improve Safety in the Underground Mining Process

Abstract Currently used machine diagnostic systems are based on very modern solutions based on the acquisition and recording of their operating parameters in real time. Increasingly available and high-tech sensor systems mean that the number of recorded parameters is increasing and their quality is improving. These data are mainly used to assess the technical condition of machines and the processes they perform. In mining, these data can also be used to assess and, at a later stage, improve the safety of the underground mining process. Referring to this issue, the paper presents examples of the use of diagnostic systems for powered roof supports and longwall shearers to assess the safety status of the underground hard coal mining process. In the case of the wall support, the focus was on measuring the pressures in the stands of its individual sections. Temporary changes in the values of these pressures constitute a valuable source of information regarding the interaction of the support with the rock mass. In particular, this concerns the identification of the effects of the informational impact of the rock mass on the longwall excavation protected by the support. The research results presented in the paper, especially in the case of very dangerous dynamic impacts, indicate the possibility of both diagnosing the operating condition of the section and identifying symptoms of exposure to such events. This undoubtedly significantly expands the possibilities of using the measured pressures. Diagnostic signals from a longwall shearer are also widely used. The current intensities drawn by its motors while cutting the rock mass, as well as the advance speed and its position in the wall make it possible to analyze these parameters and their changes before, during and after the occurrence of various types of events. These data enable the assessment of the effects of the rock mass on its operational efficiency and safety status. It also enables the identification of symptoms that precede the occurrence of such events. The presented examples indicate the need for a broader and more holistic approach to the use of diagnostic parameters of mining machines. In particular, this concerns the study of the cooperation between the support and the rock mass and its influence on the efficiency and safety of the rock mass mining process. The subject matter addressed relates to very important and current issues, and the developed methodology and obtained results should be applied in practice as soon as possible.

Evaluation of Air Traffic Network Resilience: A UK Case Study

With growing air travel demand, weather disruptions cost millions in flight delays and cancellations. Current resilience analysis research has been focused on airports and airlines, rather than the en-route waypoints, and has failed to consider the impact of disruption scenarios. This paper analyses the resilience of the United Kingdom (UK) air traffic network to weather events that disrupt the network’s high-traffic areas. A Demand and Capacity Balancing (DCB) model is used to simulate adverse weather and re-optimise the cancellation, delay, and rerouting of flights. The model’s feasibility and effectiveness were evaluated under 20 concentrated and randomly occurring extreme disruption scenarios, lasting 2 h and 4 h. The results show that the network is vulnerable to extended weather events that target the network’s most central waypoints. However, the network demonstrates resilience to weather disruptions lasting up to two hours, maintaining operational status without any flight cancellations. As the scale of disruption increases, the network’s resilience decreases. Notably, there exists a threshold beyond which further escalation in disruption scale does not significantly impair the network’s performance.

Prediction of Lithium-Ion Battery Health Using GRU-BPP

Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering to analyze the relationships between operating conditions—such as temperature and load profiles—and battery performance degradation. This paper uses a publicly accessible dataset derived by aging three prismatic LIB cells under a realistic forklift operation profile. First, we identify the features that are relevant to driving variance, then we employ the winning algorithm of K-means clustering for the classification of operational states. Granger causality later investigates the inter-group relationships. Our GRU-BPP model achieves an RMSE value of 0.167 and an MAE of 0.129 for the reference performance testing (RPT) dataset and an RMSE of 0.032 with an MAE of 0.025 for the aging dataset, thus outperformed benchmark methods such as GRU, LME, and XGBoost. These results further enhance the predictiveness and robustness of this approach and yield a holistic solution to the conventional challenges in battery management and their remaining useful life (RUL) predictions.

The technique of transforming symptom's symbol into emptiness: A mind-body therapy in the Chinese context.

The technique of transforming symptom's symbol into emptiness (TSSE) is a new mind-body treatment method proposed by Tianjun Liu in 2008. It integrates Qigong and concrete object-image thinking rooted in traditional Chinese culture into modern psychotherapy and proposes that mental and physical problems can be alleviated or eliminated in the process of movement. Accordingly, the therapist needs to guide the client with various symptoms to psychological nothingness where the client cannot see or feel these symptoms, and the purpose of healing can be achieved through the experience of emptiness. TSSE is divided into static and dynamic operations and consists of 10 steps. The static operation includes trio relaxation exercises (the body, breath, and mind), identifying the target symptom, visualizing the target symptom as an object-image, visualizing a symbolic carrier, and filling out record sheet A. The dynamic operation includes trio relaxation exercises again, moving the symbolic object into the carrier, moving the carrier with the symbolic object into psychological nothingness, moving back and assessment, and filling out record sheet B. The effectiveness of TSSE can be evaluated by the therapist's judgment based on the client's performance and by the difference between the symptom impact scores recorded in sheets A and B. TSSE has been proven to be an effective psychosomatic treatment solution by some empirical studies conducted in China. Future research can combine other technologies, such as fMRI and fNIRS, to further explore the potential effective mechanisms of TSSE.

Emergency Care Efficiency vs. Quality: Uncovering Hidden Consequences of Fast-Track Routing Decisions

Problem definition: This work aims to examine the role of emergency department (ED) operational status related to congestion in fast-track (FT) routing decisions and the subsequent effects on patient outcomes. Methodology/results: In this paper, we utilize a two-year data set from two hospital EDs in Alberta, Canada, and adopt an instrumental variable approach to examine the effects of FT routing decisions on patient outcomes. Based on the empirical findings, we utilize a data-calibrated simulation to compare the performance of different routing policies. First, our study reveals that FT routing decisions are not purely clinically driven, and ED operational status is also associated with FT routing decisions. Second, being routed to FT can improve ED efficiency by reducing the average length of stay and left without being seen rates. However, this efficiency improvement comes at the cost of potential quality decline. In particular, being routed to the FT leads to an 8.2% increase in the 48-hour revisit rate for the high-complexity group and a 2.3% increase for the medium-complexity group. Third, we delve into the mechanisms behind observed patient outcomes and find that physicians in the FT area may prioritize expediting patient flow by simplifying patient diagnosis and treatment procedures. Consequently, the quality of care may be compromised for high- and medium-complexity patients. Finally, our simulation findings highlight the importance of selecting the “right” patients to be routed to the FT unit. To this end, the complexity-based classification method and dynamic routing policies emerge as promising avenues. Managerial implications: Our findings call for immediate attention from healthcare practitioners to carefully balance the trade-off between emergency care efficiency and quality, emphasizing the necessity of selecting the right patients for routing. Funding: This study is partially supported by the Hong Kong Research Grants Council [Grants GRF 11508921 and CRF C7162-20G]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0440 .

Gelfand Triplets, Ladder Operators and Coherent States

Inspired by a similar construction on Hermite functions, we construct two series of Gelfand triplets, each one spanned by Laguerre–Gauss functions with a fixed positive value of one parameter, considered as the fundamental one. We prove the continuity of different types of ladder operators on these triplets. Laguerre–Gauss functions with negative values of the fundamental parameter are proven to be continuous functionals on one of these triplets. Different sorts of coherent states are considered and proven to be in some spaces of test functions corresponding to Gelfand triplets.

Intelligent Helicopter Turbine Engine Fault Diagnosis Using Multi-Head Attention

A turbine engine provides power to the helicopter, enabling the helicopter to travel and hover in the air. Since the rotorcraft operates at high altitudes, ensuring safety and maintaining a healthy operational status are crucial at all times. Therefore, a prognostics and health management (PHM) system for the turbine engine must be implemented to predict any anomalies or faults to prevent catastrophic accidents. This research proposes a novel fault diagnosis method for helicopter turbine engines based on operational data acquired from actual aircraft. First, the proposed method predicts engine torque using other operational data while accounting for uncertainty. A Bayesian regression approach is employed to predict the engine torque. The torque margin, defined as the difference between the actual torque and the estimated torque, is then used to diagnose engine faults. Specifically, a multi-head attention mechanism is incorporated to capture interactions between various engine parameters. Additionally, domain adaptation techniques are applied to enhance the model's generalization performance, ensuring robustness across diverse operating conditions. The proposed method is validated using seven different datasets, each acquired from a helicopter engine. Four datasets were used for training, while the remaining three were allocated for testing and validation. The results indicated that the proposed method accurately predicted torque. Furthermore, the fault diagnosis showed promising results, leading to a 3rd-place finish in the 2024 PHM Society Data Challenge in terms of validation score.

Discharge fault detection of dry air switchgear based on ZnO-MoS2 gas sensor

Abstract When discharge faults occur in dry air switchgear, the air decomposes to produce diverse gases, with NO2 reaching the highest levels. Detecting the NO2 level can reflect the operation status of the equipment. This paper proposes to combine ZnO cluster with MoS2 to improve the gas-sensitive properties of the monolayer. Based on the Density Functional Theory (DFT), the effect of (ZnO)n size on the behavior of MoS2 is considered. Key parameters such as adsorption energy and band gap of (ZnO)n-MoS2/NO2 system were calculated. The ZnO-MoS2 heterojunction was successfully synthesized by a hydrothermal method. The gas sensor exhibits a remarkable response and a fast response-recovery time to 100 ppm NO2. In addition, it demonstrates excellent selectivity, long-term stability and a low detection limit. This work confirms the potential of the ZnO-MoS2 composite structure as a highly effective gas sensor for NO2 detection, which provides valuable theoretical and experimental insights for fault detection in dry air switchgear.

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

Cloud systems, typically comprised of various components ( e.g. , microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance for cloud vendors. In current practice, crucial metrics, i.e. , key performance indicators (KPIs), are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by analyzing each metric independently. However, this approach overlooks the complex dependencies existing among cloud components. Some graph neural network-based methods take both temporal and relational information into account, however, the correlation violations in the metrics that serve as indicators of underlying performance issues are difficult for them to identify. Furthermore, a large volume of components in a cloud system results in a vast array of noisy metrics. This complexity renders it impractical for engineers to fully comprehend the correlations, making it challenging to identify performance issues accurately. To address these limitations, we propose Identifying Performance Issues based on Relational-Temporal Features (ISOLATE ), a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues. In particular, it adopts a graph neural network with attention to characterizing the relations among metrics and extracts long-term and multi-scale temporal patterns using a GRU and a convolution network, respectively. The learned graph attention weights can be further used to localize the correlation-violated metrics. Moreover, to relieve the impact of noisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags pseudo labels based on a small portion of confirmed negative examples. Extensive evaluation on both public and industrial datasets shows that ISOLATE outperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3. The ablation study also proves the effectiveness of the relational-temporal features and the PU-learning strategy. Furthermore, we share the success stories of leveraging ISOLATE to identify performance issues in Huawei Cloud, which demonstrates its superiority in practice.

User Sentiment Analysis of the Shared Charging Service for China’s G318 Route

Shared charging services have gained popularity for their contribution to green travel. Accurately identifying the core factors that influence user experience (UX) not only enhances service quality and optimizes user satisfaction, but also promotes the dissemination of green travel concepts. However, the influencing factors and their mechanisms vary significantly across regions, particularly along the Chengdu–Lhasa (G318) route, which features large elevation changes, diverse climatic conditions, rugged terrain, and frequent geological disasters, making the influencing factors particularly complex. This study analyzes comment texts from 38 shared charging stations along the G318 route in the e-Charging APP, totaling 15,214 comments. A comprehensive approach is employed, including high-frequency word analysis, term frequency–inverse document frequency (TF-IDF) comparison, co-occurrence semantic network and co-word matrix feature correlation analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis. This multifaceted analysis explores core themes, user viewpoints, and sentiments in the comments, focusing on users’ perspectives on service quality, usage experience, and environmental impact of the charging stations. The findings indicate that charging speed, service attitude, environment, operational status of hardware and software, and pricing are key factors influencing user sentiment. Users have a high demand for the perfection of supporting facilities of shared charging stations, directly affecting user satisfaction and indirectly influencing the brand image and market competitiveness of enterprises.

Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model

The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.