Normal Operation Research Articles

A pantograph-catenary arcing detection model for high-speed railway based on semantic segmentation and generative adversarial network

ABSTRACT Pantograph-catenary arcing refers to an abnormal phenomenon occurring in pantograph-catenary system due to poor contact or other factors, which significantly impacts the normal operation of high-speed railway. Therefore, the detection of arcing occurrences holds significant importance for the intelligent maintenance of pantograph-catenary systems. However, the scarcity of arcing data in pantograph-catenary datasets limits the efficacy of supervised learning methods for arcing detection. To address this issue, we propose a novel pantograph-catenary arcing detection model that integrates semantic segmentation with generative adversarial networks. The model first modifies the loss function of the U 2 -Net network to tailor it specifically for pantograph-catenary semantic segmentation. To generate finer normal pantograph-catenary images, attention mechanism is incorporated into the SPADE-based pantograph-catenary scene generation model. Finally, an improved differencing method is employed to compute the arcing image by subtracting the generated normal pantograph-catenary image from the actual pantograph-catenary image. The experimental results validate the effectiveness of the method for pantograph-catenary arcing detection in the absence of prior arcing knowledge, with a recall rate of 75.3% and an F1-Score of 69.63%. Compared to other advanced pantograph-catenary arcing methods, this method exhibits superior performance.

Study on Deformation of New Tunnels Overcrossing Existing Tunnels Underneath Operating Railways

With the increasing utilization of urban underground space, new tunnels frequently intersect with existing tunnels and operational railways. However, sometimes the excavation and unloading of new tunnels can cause deformation of adjacent existing tunnels and railways, significantly affecting their normal operation. We used finite element software to predict the influence of new tunnel construction on overcrossing existing tunnels and down-traversing operational railways by a dynamic tunneling model based on a connection channel project of the east and west squares of a railway station. This article is not only control the distance between the two tunnels, but the new tunnel and the existing tunnel, as well as the new tunnel and the operation of the railway, the positional relationship between the three, the deformation laws of existing tunnels and operational railways during the construction of new tunnels with different buried depths are analyzed. The results show that the deformation curves of existing tunnels and operational railways present a normal distribution. The maximum deformation position is at the intersection with the new tunnel upon completion of the new tunnel excavation construction. Moreover, an increase in the buried depth of the new tunnel increases the deformation of the operational railway and the existing tunnel. The influence of the depth change of the new tunnel on the settlement of the operational railway is greater than that of the existing tunnel.

Thermodynamic modeling and analysis of a Carnot battery system integrating calcium looping thermochemical energy storage with coal-fired power plant

Long-term energy storage and carbon capture technologies are pivotal in managing renewable energy surpluses and achieving carbon neutrality. This paper proposes a Carnot battery system integrating calcium-looping thermochemical energy storage with a coal-fired power plant. The system utilizes excess electricity from the grid for energy input, facilitating long-term energy storage, achieving carbon capture, and reducing coal consumption in the power plant. An optimization framework is developed, incorporating component thermodynamic models and optimization algorithms, to maximize the coal savings in plants and energy efficiencies of the Carnot battery system. During the energy release process, the carbonator partially replaces the reheat load of the boiler, necessitating retrofitting of the boiler’s heating surface to ensure normal operation. The base system demonstrates a CO2 capture capacity of 3.23 MJ/kg and a reduction in coal consumption by 7.07 %. The round-trip efficiency and comprehensive efficiency of the base system are 36.93 % and 37.91 %, respectively. The relatively low energy efficiency is primarily due to the deactivation of circulating adsorbents and the limited efficiency of the subcritical coal-fired power plant. By incorporating an additional recarbonation step to mitigate adsorbent deactivation, the system’s comprehensive efficiency is improved to 42.20 %. Further improvements in system efficiency can be achieved by using modified adsorbents with high cycling stability instead of natural limestone and by coupling the system with more efficient ultra-supercritical units instead of the investigated subcritical units. This study offers valuable insights into the development of long-term energy storage solutions and multifunctional Carnot battery technology.

Study on vibration mechanism and dynamic characteristics for TBM main bearing defects

As the key component of TBM main driving system, the main bearing plays an indispensable role both in its service life and the normal operation of the TBM. While prior investigations predominantly focused on static analyses of the main bearing, only a scant few delved into its dynamic characteristics and the vibration mechanism. In order to explore the dynamic characteristics of TBM main bearing, a comprehensive dynamics model of the TBM main bearing considering local defects is established, the vibration mechanism of the main bearing with local defects is revealed, and the influence of uneven load is investigated. Furthermore, the effects of roller wear and bearing clearance on the vibration response of the main bearing are analyzed, which systematically describes the real motion of the main bearing. Finally, a main bearing scaling test rig is constructed, and the proposed model is validated through experiments and simulations, demonstrating its good accuracy. In addition, the results show that the incremental imposition of uneven load correlated with a rising trend in the root mean square (RMS) value of vibration response. Moreover, maintaining an optimal axial clearance is pivotal in ensuring the reliable operation of the main bearing. The research work in this paper provides theoretical scaffolding for the defect diagnosis of TBM main bearing, whilst also serves as an analytical cornerstone for future explorations into the dynamics of various types of thrust bearings.

Opportunistic Change During a Punctuation: How and When the Front Lines Can Drive Bursts of Incremental Change

Environmental jolts can trigger more conducive conditions for driving change in organizations. However, punctuated equilibrium theories of organizational change concentrate on top managers’ implementation of de novo radical changes after jolts. Existing research has not examined frontline-driven, incremental change efforts during these periods of disrupted stasis, despite the value of frontline change ideas. We develop a process model to explain how and when those on an organization’s front lines can leverage a jolt to opportunistically implement long-desired change ideas in ways that promote their retention. We conducted a two-year qualitative field study at a hospital during the Covid-19 pandemic, examining the trajectories of 33 premeditated change ideas raised by frontline staff. By comparing ideas that persisted to become part of normal operations with those that failed to be selected or retained, we identified practices and conditions that promoted the selection and retention of frontline change ideas. Our study suggests that frontline change advocates can seed the long-term retention of “shovel-ready” ideas—as opposed to de novo ideas—after a jolt by rapidly and opportunistically deploying a novel set of practices before the brief window of opportunity created by lessened constraints and increased managerial receptivity closes. Prior theories of change largely assume frontline-driven change to be slow and continuous, proceeding in a one-off fashion; we explain how and when frontline change can instead occur in rapid, opportunistic bursts. This study advances theories of punctuated equilibrium and bottom-up change in organizations by unearthing an alternative way that change can be intentionally accomplished in organizations. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.15120 .

Initial Experience With Ultra-High-Definition 3D Exoscope in Thyroid and Parathyroid Surgery.

Operation with a 3D exoscope has recently been introduced in clinical practice. The exoscope consists of two cameras placed in front of the operative field. Images are shown on a large 3D screen with high resolution. The system can be used to enhance precise dissection and provides new possibilities for improved ergonomics, fluorescence, and other optical-guided modalities. Initial experience with the ultra-high-definition (4K) 3D exoscope in thyroid and parathyroid operations. The exoscope (OrbEyeTM) was mounted on a holding system (Olympus). We used the exoscope in parathyroidectomy (N = 6) and thyroidectomy (N = 6). Immediate advantages and disadvantages were discussed and recorded. The learning curve for use of the exoscope may be shorter for surgeons with training in endoscopic or robotic procedures. There may be improved ergonomics compared with normal open-neck operations. Further, the optical guided operations can be used with fluorescence and have potential for different on-lay techniques in the future. The 4K 3D image quality is state-of-art and is highly appreciated during fine surgical dissection and eliminates the need for loupes. In several ways, using the ORBEYE™ in thyroid and parathyroid surgery provides the surgical team with a new and enhanced experience. This includes improved possibility for teaching, surgical ergonomics, and a 4K 3D camera with a powerful magnification system. However, it is not clear if utilization of these features would improve surgical outcomes. Furthermore, the ORBEYE™ lacks incorporation of parathyroid autofluorescence, and the current costs for the system do not facilitate general access to exoscope assisted operations.

Revisiting Pulse-Based OCV Incremental Capacity Analysis for Diagnostics of Li-Ion Batteries

This paper presents the concept of applying incremental capacity analysis (ICA) on the OCV curve in the SoC space. The OCV curve can be obtained from any sequence of discharge or charge current or power pulse with a necessary rest period to allow the cell to reach a pseudo-OCV after each pulse. With a high resolution (>100 pulses) in the full SoC window, an OCV-vs.-SoC curve can be obtained with sufficient accuracy to perform an ICA on the obtained OCV curve. ICA as a diagnostic technique has commonly been applied on Li-ion cells with constant charge and discharge at slow currents. However, a slow controlled constant current charge or discharge is normally not feasible and cannot be easily applied to a battery in an application. Here, we revisit pulse-based ICA to supplement the conventional constant-current-based technique. Based on actual ageing data, we show that ICA performed on a selection of high-resolution OCV curves is comparable or better than conventional ICA with constant current. The main advantage of OCV-ICA is that it can be applied to most cells and systems without a significant interruption of normal cell operation. OCV-ICA can provide valuable insights into ageing mechanisms as well as, e.g., detailed information on changes in internal resistance.

A System Predictive Maintenance Framework for Advanced Reactors Using a Data-Driven Digital Twin

Advanced reactor designers are looking to maximize the system capacity factor to make advanced reactors more economically competitive and meet the projected energy demand. To achieve this goal, we propose a Dynamic Operation and Maintenance Optimization (DyOMO) framework to perform system-level predictive maintenance (PdM) using a dynamic Bayesian network and component-specific PdM using deep neural networks. At the system level, DyOMO detects the presence of anomalous phenomena, determines the most influential degradation mode, and estimates the remaining useful life (RUL) distribution for the system. At the component level, DyOMO summarizes the health state of key system components, determines the presence of an anomaly using a feedforward neural network, and predicts component RUL using a Bayesian neural network. To evaluate the overall performance of DyOMO, normal operations of a Pebble-Bed High-Temperature Gas-cooled Reactor (PB-HTGR) were simulated with realistic component degradation for the steam turbine and steam generator. Across the 20 independent reactor life simulations, it was found that maintenance was always performed before any safety limits were violated and before a component failed. Specifically, the system-level PdM suggested maintenance on the steam generator once the steam pressure approached its safety limit, and the component-specific PdM suggested maintenance on the steam turbine once the turbine blade hardness degraded. The results indicate that through the continuous monitoring of the system and individual components, the DyOMO framework improves safety and increases the availability of the reactor when compared to traditional maintenance philosophies.

Comprehensive evaluation of solder joint integrity in power semiconductors: Exploring multiple failure mechanisms and cumulative impact

Investigating the impact of solder joint degradation in power semiconductors is crucial given their widespread use. Solder joints, being the weakest link, are prone to multiple failure mechanisms, exacerbating degradation under normal operation. This paper presents a novel approach for assessing solder joint degradation, particularly in electric vehicles, where severe thermal and vibration stresses are prevalent. Our method categorizes solder joint conditions into discrete states, from fully perfect to fully damaged, integrating both thermal and vibration-induced failures. By considering cumulative effects, our approach yields more precise reliability estimates. Moreover, it offers a versatile framework applicable across various failure scenarios and readily deployable in the design phase.

Spatial Structure and Vulnerability of Container Shipping Networks: A Case Study in the Beibu Gulf Sea Area

Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea area is a key channel to supporting China’s participation in international economic cooperation in the western region. It is highly susceptible to the influence of the political and economic instability. This study introduces a dual-component framework to analyze the inherent structure and potential vulnerabilities of the container transportation network in the Beibu Gulf Sea areas. The findings show that the core layer of the network exhibited circular solidification characteristics. The entire network heavily relies on some core ports, such as Haiphong Port, Ho Chi Minh Port, and Qinzhou Port, and it highlights the potential increases in vulnerability. The finding shows that deliberate attacks have a greater impact than random attacks on the normal operations of maritime networks. If ports with high intermediary centrality are attacked, the connectivity and transportation efficiency of the Beibu Gulf maritime network will be significantly affected. However, under such circumstances, redistributing cargo transportation through route adjustments can deal with the transmission of cascading failures and maintain the network’s resilience. Based on the existing knowledge and the data collected in a case study, this research stands out as the first to provide a critical examination of the spatial structure and vulnerability of container shipping networks in the Beibu Gulf sea.

EXPRESS: Estimation of the Global Equivalence Ratio of a Swirl Combustor from a Small Number of Absorption Spectra Using Machine Learning.

A new optical diagnostic method that predicts the global fuel-air equivalence ratio of a swirl combustor using absorption spectra from only three optical paths is proposed here. Under normal operation, the global equivalence ratio and total flow rate determine the temperature and concentration fields of the combustor, which subsequently determine the absorption spectra of any combustion species. Therefore, spectra, as the fingerprint for a produced combustion field, were employed to predict the global equivalence ratio, one of the key operational parameters, in this study. Specifically, absorption spectra of water vapor at wavenumbers around 7444.36, 7185.6, and 6805.6 cm-1 measured at three different downstream locations of the combustor were used to predict the global equivalence ratio. As it is difficult to find analytical relationships between the spectra and produced combustion fields, a predictive model was a data-driven acquisition. The absorption spectra as an input were first feature-extracted through stacked convolutional autoencoders (CAEs) and then a dense neural network (DNN) was used for regression prediction between the feature scores and the global equivalence ratio. The model could predict the equivalence ratio with an absolute error of ±0.025 with a probability of 96%, and a gradient-weighted regression activation mapping analysis revealed that the model leverages not only the peak intensities but also the variations in the shape of absorption lines for its predictions.

A novel method to determine bias-dependent source and drain parasitic series resistances in AlGaN/GaN high electron mobility transistors

The AlGaN/GaN high electron mobility transistors (HEMTs) with T-gate that suitable for high frequency applications were fabricated. A novel method to extract the bias-dependent source and drain parasitic series resistances (Rs and Rd) of AlGaN/GaN HEMTs is proposed. By analyzing the distributed capacitance and current generator network in the velocity saturated regions of the AlGaN/GaN HEMTs, a new restriction relationship between small-signal equivalent circuit elements is found. The Rs and Rd can be determined under active bias through wideband S-parameter measurements, which can better reflect the physical mechanism of AlGaN/GaN HEMTs under normal operation. The S-parameters and extrinsic transconductance calculated based the small-signal equivalent circuit element values extracted by the method proposed in this paper are very consistent with the experimental values, which reflects the accuracy of this element extraction method. In this paper, the physical mechanism that causes Rs and Rd to vary with bias voltage is also studied. This study has a deeper insight into the bias-dependence of Rs and Rd, which modifies the understanding for physical mechanisms of AlGaN/GaN HEMTs. The research results provide new ideas for establishing small-signal equivalent circuit models containing more physical effects and is of great significance to GaN-based integrated circuit design.

A systematic approach for data generation for intelligent fault detection and diagnosis in District Heating

This study introduces a novel systematic approach to address the challenge of labeled data scarcity for fault detection and diagnosis (FDD) in District Heating (DH) systems. To replicate real-world DH fault scenarios, we have created a controlled laboratory emulation of a generic DH substation integrated with a climate chamber. Furthermore, we present an FDD pipeline using an isolation forest and a one-class support vector machine for fault detection alongside a random forest and a support vector machine for fault diagnosis. Our research analyzed the impact of data sampling frequencies on the FDD models, revealing that shorter intervals, such as 1-min and 5-min, significantly improve FDD performance. We provide detailed information on six scenarios, including normal operation, a minor valve leak, a valve leak, a stuck valve, a high heat curve, and a temperature sensor deviation. For each scenario, we present their signature, quantifying their unique behavior and providing deeper insights into the operational implications. The signatures suggest that, while variable, faults have a consistent pattern seen in the generic DH substation. While this work contributes directly to the DH field, our methodology also extends its applicability to a broader context where labeled data is scarce.

Optimal placement of wind turbine in distribution grid to minimize energy loss considering power generation probability

This research proposes an algorithm to determine the optimal position of wind turbines in a distribution grid for minimizing its annual energy loss. In this paper, the probability distribution of power generation at each wind turbine node is considered and the nonsimultaneous occurrence of the power generation at wind turbine nodes is also interested. Moreover, we also consider the wind energy exploitability at each node in the grid. This algorithm is coded in MATLAB environment and it is verified via a sample IEEE 33 bus distribution grid with a sample data of power generation probability. The verifying results indicated that the optimal number of wind turbines at each node to obtain a minimal annual energy loss. Results also indicated that the node voltage is varied because of the change of power generation; however, the voltage at all nodes is in normal operation range and its median value is between about 1.01 pu to 1.05 pu. The power factor of wind turbines has an impact on the optimal number of wind turbines at nodes and the efficiency of wind turbine installation.

An analysis of the effect of the COVID-19-induced joint replacement deficit in England, Wales, and Northern Ireland suggests recovery will be protracted.

The COVID-19 pandemic has disrupted the provision of arthroplasty services in England, Wales, and Northern Ireland. This study aimed to quantify the backlog, analyze national trends, and predict time to recovery. We performed an analysis of the mandatory prospective national registry of all independent and publicly funded hip, knee, shoulder, elbow, and ankle replacements in England, Wales, and Northern Ireland between January 2019 and December 2022 inclusive, totalling 729,642 operations. The deficit was calculated per year compared to a continuation of 2019 volume. Total deficit of cases between 2020 to 2022 was expressed as a percentage of 2019 volume. Sub-analyses were performed based on procedure type, country, and unit sector. Between January 2020 and December 2022, there was a deficit of 158,994 joint replacements. This is equivalent to over two-thirds of a year of normal expected operating activity (71.6%). There were 104,724 (-47.1%) fewer performed in 2020, 41,928 (-18.9%) fewer performed in 2021, and 12,342 (-5.6%) fewer performed in 2022, respectively, than in 2019. Independent-sector procedures increased to make it the predominant arthroplasty provider (53% in 2022). NHS activity was 73.2% of 2019 levels, while independent activity increased to 126.8%. Wales (-136.3%) and Northern Ireland (-121.3%) recorded deficits of more than a year's worth of procedures, substantially more than England (-66.7%). It would take until 2031 to eliminate this deficit with an immediate expansion of capacity over 2019 levels by 10%. The arthroplasty deficit following the COVID-19 pandemic is now equivalent to over two-thirds of a year of normal operating activity, and continues to increase. Patients awaiting different types of arthroplasty, in each country, have been affected disproportionately. A rapid and significant expansion in services is required to address the deficit, and will still take many years to rectify.

Harmonic absorption method for cement kiln power supply system under clean energy integration

Abstract To maintain the normal operation of the power supply system, a method for harmonic absorption in the cement kiln power supply system under clean energy access is proposed. We design a data acquisition structure for the cement kiln power supply system and extract the harmonic current signal of the power supply system through variational mode decomposition, harmonic compensation for the cement kiln power supply system under clean energy integration, and harmonic absorption of cement kiln power supply system through the current equivalent model. The experimental results show that the harmonic signal extraction accuracy of this method is high, and the difference between the consumed discharge power and the power demand is minimal. It can achieve a balance between power supply and demand through harmonic compensation, which is conducive to the stable operation of the power supply system.

The active magnetic compensation coil.

The active magnetic compensation coil is of great significance for extensive applications, such as fundamental physics, aerospace engineering, national defense industry, and biological science. The magnetic shielding demand is increasing over past few decades, and better performances of the coil are required. To maintain normal operating conditions for some sensors, active magnetic compensation coils are often used to implement near-zero field environments. Many coil design methods have been developed to design the active compensation coil for different fields. It is opportune to review the development and challenges associated with active magnetic compensation coils. Active magnetic compensation coils are reviewed in this paper in terms of design methods, technology, and applications. Furthermore, the operational principle and typical structures of the coil are elucidated. The developments of the forward design method, inverse design method, and optimization algorithm are presented. Principles of various design methods and their respective advantages and disadvantages are described in detail. Finally, critical challenges in the active magnetic compensation coil techniques and potential research directions have been highlighted.

Self-Healing Control of Nuclear Power Plants Under False Data Injection Attacks

The transition from analog to digital instrumentation and control (I&C) systems introduces new threats caused by cyberattacks in the nuclear industry. This paper proposes a self-healing strategy to respond to a false data injection attack that targets digital I&C systems, which is a type of cyberattack commonly targeting nuclear power plants with the potential to cause serious physical impacts. This resilience strategy for self-healing control contains three components: (1) an anomaly detection model that can detect false data injection attacks, (2) a device-level control that utilizes inferred values to perform control under a detected false data injection, and (3) a system-level control that leverages another controller that is not under attack to lead the system back to a safe operation state when the device-level control is unavailable. Anomaly detection and device-level control use an autoencoder while system-level control utilizes reinforcement learning. The proposed self-healing resilience strategy is demonstrated with a generic pressurized water reactor (GPWR) simulator under false data injections, targeting the steam generator water level. The results show that the proposed strategy effectively leads the system back to a normal operation state under various false data injection cases.

Pressure prediction of water supply network based on 1DCNN-GRU-multi-head attention

ABSTRACT The water supply network is a crucial component of urban construction and plays a pivotal role in the normal operation of the city. The complexity and periodicity of pressure variations in the water supply networks pose significant challenges to traditional prediction models. In this study, we introduce a novel short-term pressure prediction model, termed the 1DCNN-GRU-multi-head attention (CGMA) model, which incorporates a multi-head attention mechanism alongside an integrated network consisting of one-dimensional convolutional neural networks (1DCNNs) and gated recurrent units (GRUs). Initially, the model employs a 1DCNN network to extract features from the pressure data. Subsequently, the extracted features are input into the GRU neural network, leveraging its long-term dependency capabilities to improve prediction accuracy. Then, the attention mechanism is incorporated into the GRU component to highlight key information, enabling the model to focus on more important data features, thereby improving the prediction performance. We implemented this model in a real urban water distribution system equipped with five pressure sensors. The model achieved a mean absolute error of 0.00197 and a root mean square error of 0.00262. When compared with alternative approaches, our method demonstrated superior predictive performance for pressure data, thereby confirming its efficacy in practical applications.

HTG transformation: an amplitude modulation method and its application in bearing fault diagnosis

Abstract Rolling bearings are critical components in modern mechanical equipment, and the health monitoring and predictive maintenance of bearings are crucial for the normal operation of machinery. Hence, there is a compelling need to delve into advanced methodologies for enhancing the detection of fault characteristics in bearings. Faulty bearings produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, in some complex conditions, the relevant fault frequencies may be hidden within interfering components. This paper presents an amplitude modulation technique called the hyperbolic tangent Gaussian (HTG) transformation, designed to extract weak fault components from signals. Firstly, a family of amplitude modulation functions, known as the HTG functions, is constructed. These functions modulate signals with normalized amplitudes to obtain a series of modulated signals. Simultaneously, a frequency domain amplitude ratio metric is used for the automatic selection of the optimal components. Finally, the HTGgram is introduced, a spectral decomposition method based on trend components, aiming to identify the best combination of filtering and modulation components. Simulations with multi-component bearing fault signals and experimental signals with composite bearing faults demonstrate that this method not only highlights fault features and suppresses noise interference but also adaptively selects frequency bands related to faults, enhancing fault information. This approach exhibits excellent adaptability and effectiveness in complex operating conditions with multiple interference components.