Safe Operation Research Articles

Robotic-assisted laparoscopic modified ureteroplasty using lingual mucosa grafts or appendiceal flaps for the management of complex ureteral strictures: a retrospective cohort study

ObjectiveTo evaluate the feasibility, safety and efficacy of robot-assisted laparoscopic modified ureteroplasty using a lingual mucosa graft (LMG) or an appendiceal flap (AF) for complex ureteral strictures and summarize our experience.MethodsA total of 16 patients with complex ureteral strictures (range: 1.5–5 cm) who underwent robotic-assisted laparoscopic-modified ureteroplasty and were admitted to our hospital from May 2022-October 2023 were retrospectively analyzed. We used modified presuture methods in patients who needed the posteriorly augmented anastomotic technique to reduce anastomotic tension. Perioperative variables and outcomes were recorded for each patient.ResultsThe operation under robot-assisted laparoscopy was successfully performed in all sixteen patients (12 with LMG ureteroplasty and 4 with AF ureteroplasty) without conversion to open surgery. The mean length of the ureteral structure was 2.90 ± 0.90 cm (range: 1.5–5 cm), the mean operation duration was 209.69 ± 26.74 min (range: 170–255 min), the median estimated blood loss was 75 (62.5) ml (range: 50–200 ml), and the duration of postoperative hospitalization was 10.44 ± 2.10 d (range: 7–14 d). The follow-up time in this group was 6 ~ 21 months. The success rate of the surgery was 100%.ConclusionRobot-assisted laparoscopic modified ureteroplasty using AF or LMG is a safe and feasible operation for complex ureteral strictures and deserves to be popularized.

Enhancing RODNet detection in complex road environments based on ESM and ISM methods

In autonomous driving, accurately identifying traffic targets is crucial for ensuring the safe and reliable operation of autonomous vehicles. Millimeter-wave radar, known for its low cost, long detection range, and excellent performance under various weather conditions. Deep learning algorithms, particularly the radar object detection network (RODNet), have been effectively applied to radar target detection by analyzing the range-azimuth (RA) heatmaps that capture complex target features. However, the low angular resolution of radar RA heatmaps, combined with the high sensitivity of millimeter-wave radar to metal objects, makes adjacent targets prone to misdetection and increases the likelihood of misclassification of target types due to metal reflections from road obstacles. To address these issues, this paper proposes an innovative extension suppression method to enhance RA heatmaps, reducing interference between adjacent targets and significantly improving target resolution. Additionally, the paper incorporates Gaussian filtering, peak detection, and amplitude suppression algorithms to design an interference suppression method, accurately identifying and mitigating strong reflections from non-target regions, thereby improving detection efficiency in complex environments. The effectiveness and superiority of these methods have been fully validated, with AP improvements of 18% in overlapping scenarios, 2% in metal obstacle scenarios, and around 10% in high-speed scenarios compared to the latest methods.

Method for Wind–Solar–Load Extreme Scenario Generation Based on an Improved InfoGAN

In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system.

Predicting water content in engine oil using SPA-ISSA-BP methods  

Contamination of engine oil with water can accelerate oil oxidation and decomposition, resulting in oil degradation. Therefore, the detection and prediction of water content in engine oil is crucial to ensure the long-term safe operation of engines. In this study, near-infrared spectroscopy was used to analyse engine oils with varying water contents. Spectral data were obtained, and various methods, including Savitzky-Golay (SG) smoothing, orthogonal signal correction (OSC), and successive projection algorithm (SPA) were employed to compare the performance differences of principal component regression models. An Improved Sparrow Searches Algorithm (ISSA) was then applied to optimise the Back-Propagation Neural Network (BPNN) for predicting water content in SAE 10W-30 engine oil. The results showed that the performance of the principal component regression model constructed from spectral data after SG smoothing, OSC, and the SPA feature wavelength selection had improved. The BPNN optimised by the improved Sparrow Search Algorithm accelerated the convergence speed of the model and effectively improved the prediction accuracy of the BPNN. The obtained coefficient of determination (R2) was 0.99103, the root mean square error (RMSE) was 2. 2136 10-4, and the mean absolute error (MAE) was 1. 8889×10-4. These results provide an effective method for detecting and predicting the water content in engine oil.

Direct energy deposition for smart micro reactor

ABSTRACT Nuclear microreactors (MRs) have attracted significant attention for their versatility in installation locations, especially with the growing demand for sustainable green energy in large data processing facilities. However, ensuring their safe operation necessitates frequent inspections, which can be challenging for practical efficient management. This study proposes a novel approach using direct energy deposition to incorporate an optical fiber sensor into MR components, enabling real-time monitoring with artificial intelligence. The embedded optical fiber generates real-time data that allows for AI-driven in-vivo thermal deformation analysis. Our smart MR component can detect structural anomalies, identify abnormal operations, and assess operational conditions through augmented reality interfaces and AI technology.

Simulation Study on Factors Affecting the Output Voltage of Extended-Range Electric Vehicle Power Batteries

The power battery configuration of an extended-range electric vehicle directly affects the overall performance of the vehicle. Optimization of the output voltage of the power battery can improve the overall power and economy of the vehicle to ensure its safe operation. Factors affecting the output voltage of power batteries under different operating conditions, such as nominal voltage and the number of series and parallel connections of the battery cells, have been studied. This study uses AVL Cruise to establish an overall model of an extended-range electric vehicle to simulate the output voltage characteristics under the different operating conditions of the NEDC (New European Driving Cycle), WLTC (World Light Vehicle Test Cycle) and CLTC (China Light Duty Vehicle Test Cycle). The influence of the output voltage of the power battery under different operating conditions is studied to ensure that the power battery can output energy with high efficiency. The operating conditions have an impact on the output voltage with an idle voltage fluctuation of the operating conditions. The nominal voltage variation and the number of series and parallel connections of the battery cells affect the frequency and time of breakdown.

Впровадження автоматизованих систем моніторингу мостових споруд в Україні: Світовий досвід та національні перспективи

ntroduction. Highway bridges are an important element of the transport infrastructure of any country, and Ukraine is no exception. Their safe operation is critical to ensure the sustainable functioning of logistics systems and economic development of regions. However, due to constant loads, the impact of natural factors and insufficient funding for maintenance, a significant number of bridges in Ukraine are in poor technical condition. Accordingly, it is necessary to take measures to diagnose the condition of bridges in a timely manner and prevent possible accidents. In this context, automated condition monitoring systems (ACMS) are an effective tool for ensuring the smooth operation of such structures. Problem Statement. There are more than 16 thousand bridges on public roads of national importance in Ukraine, more than half of which do not meet modern standards for dimensions and load capacity. The problem is particularly acute for bridges that have been in operation for more than 40 years, as they are more prone to accidents. Due to the lack of systematic monitoring and control of the technical condition of such structures, there is a threat of their destruction. In the world practice, automated monitoring systems are actively used to prevent such situations. In Ukraine, the need to implement such systems is becoming increasingly relevant in the face of increased requirements for infrastructure security.

Hybrid modeling with data enhanced driven learning algorithm for smart generation control in multi-area integrated energy systems with high proportion renewable energy

Multi-area integrated energy systems (MA-IES) with a high proportion of renewable energy have become a trend in social development. The access to a large number of distributed energy sources makes the safe, stable, and economic operation of MA-IES suffer serious challenges. To reduce the frequency deviation and area control error (ACE) of MA-IES and to improve the economy of operation, this study proposed a hybrid modeling with data enhanced driven learning algorithm, which combines model-driven and data-driven algorithms, named combined proportion-integral-derivative and deep reinforcement learning (CPIDDRL). Firstly, the frequency deviation signals are collected and judged, and the smaller frequency deviation signals, which are sent to the model-driven part, are regulated by proportion-integral-derivative (PID) controllers. Then, the ACE and large frequency deviation signals are collected and transmitted to the data-driven part, and the Transformer deep learning network is applied to predict the multi-feature timeseries, which are applied to strategy selection for state-action-reward-state-action reinforcement learning. Finally, the model-driven part and data-driven part work together to generate adjustment commands every 4 s. The control effects of CPIDDRL, PID, Q-learning, and sliding model control algorithms are compared in two and four areas integrated energy systems. The results show that, compared to the comparison algorithms, the CPIDDRL reduces the mean values of frequency deviation and ACEs by at least 46.78% and 6.83%, respectively, and the total generation cost by at least 8.20%. CPIDDRL has practical significance in improving system stability, enhancing renewable energy integration capabilities, and promoting the development of smart grids.

Artificial Intelligence enabled self-powered sensing and wind energy harvesting system for bridges monitoring

With the advancement of society, the significance of Artificial Intelligence (AI) and big data in facilitating the intelligent development of bridges is increasing. Bridges function as crucial urban links, and ensuring their safe and stable operation is paramount. Therefore, this paper proposes an Artificial Intelligence enabled self-powered sensing wind energy harvesting system (WEHS) designed specifically for bridges. The system mainly consists of two electromagnetic generators (EMG1 and EMG2) and a rolling ball triboelectric nanogenerator (RB-TENG). WEHS achieves the recycling of ambient wind energy through the lower electromagnetic generator (EMG2). A single-channel triboelectric nanogenerator (RB-TENG) enables the sensing of wind speed within the system, while the top electromagnetic generator (EMG1) utilizes a single magnet with coils of different diameters to realize wind direction sensing. Reynolds wind tunnel that the WEHS has an average power output of 392 mW and a power density of 414.86 W/m3, which is adequate to power the sensor. The accuracy of wind speed and direction prediction based on the Artificial Intelligence GRU deep learning algorithm model is 96 % and 99.04 %, respectively. Furthermore, by implementing deep learning models for wind speed and direction, it becomes feasible to forecast the variations in wind direction and speed in the vicinity of bridges. The WEHS exhibits significant potential for bridges application and can contribute to developing AIoT-based smart bridges.

Analysis of extremely low water hammer pressures of draft tubes for double units in pumped storage power stations under successive load rejection conditions

With large-scale integration of intermittent energy into power systems, the operating conditions of pumped storage power stations (PSPSs) change frequently, thereby intensifying the risk of load rejections. For multiple units sharing a water conveyance system, when one or more units reject the load, other units also reject the load after a few seconds; this is defined as successive load rejection (SLR). During SLR conditions, pumped storage units produce extreme water hammer pressures, jeopardizing the safe operation of PSPSs. This study reveals the generation mechanism of extremely low draft tube pressure (DTP) during SLR, and clarifies the generating unit and corresponding occurrence moment characteristics of the minimum DTP. The results indicated an extremely low DTP, attributed to the sharp decline in the discharge owing to increased rotational speed in unit's S-shape region and the hydraulic interference owing to the increased discharge of the other unit. When the guide vane closing time (GVCT) was short, the minimum DTP occurred in the subsequent load rejection unit (SLRU) near the first peak of its rotational speed, with the discharge of initial load rejection unit (ILRU) in the rising stage. Conversely, for a large GVCT, the minimum DTP occurred in the ILRU near the second peak of its rotational speed, with SLRU discharge in the rising stage. Finally, engineering measures to improve the extremely low DTP were proposed. Overall, the findings underscore crucial engineering insights for ensuring the safety of PSPSs under extreme conditions.

Detection of corrosion effects on prestressed concrete bridge deck slabs from the champlain bridge through non‐destructive testing techniques

AbstractAs aging infrastructures raise public concerns, evaluating their performance is crucial for maintaining structural integrity, especially for corroding prestressed concrete members. These structures may experience substantial tendon cross‐sectional area loss before any visible deterioration becomes detectable. While various non‐destructive techniques (NDT) have proven effective in labs, correlating corrosion‐induced damage in field members remains a challenge. Establishing these correlations is key for understanding the overall performance of aging structural concrete elements and ensuring their continued safe operation through non‐invasive means. This paper investigates various NDTs on a concrete bridge deck, aiming to correlate results. Visual inspection, Schmidt rebound hammer, Ultrasonic Pulse Velocity (UPV), corrosion detection techniques, Ground Penetrating Radar (GPR), Ultrasonic Pulse Echo (UPE), and Impact Echo (IE) methods are evaluated for detecting concrete deck damage. Results show the methods' capabilities in detecting defects to a certain extent, highlighting their potential in assessing aging concrete infrastructures.

Thermal Runaway Diagnosis of Lithium-Ion Cells Using Data-Driven Method

Fault diagnosis is crucial to guarantee safe operation and extend the operating time while preventing the thermal runaway of the lithium-ion battery. This study presents a data-driven thermal runaway diagnosis framework where Bayesian optimization techniques are applied to optimize the hyperparameter of various machine learning techniques. We use different machine learning models such as support vector machine, naive Bayes, decision tree ensemble, and multi-layer perceptron to estimate a high likelihood of causes of thermal runaway by using the experimental measurements of open-source battery failure data. We analyze different evaluation metrics, including the prediction accuracy, confusion metrics, and receiver operating characteristic curves of different models. An experimental evaluation shows that the classification accuracy of the decision tree ensemble outperforms that of other models. Furthermore, the decision tree ensemble provides robust prediction accuracy even with the strictly limited dataset.

Anions exacerbate microbial corrosion of X80 pipeline steel induced by sulfate-reducing bacteria in the sea mud environment

Sulfate-reducing bacteria (SRB) present in sea mud pose a challenge to the safe operation of subsea pipelines. Chlorides and sulfates in sea mud promote the microbial corrosion of subsea pipelines by SRB. This study investigates the effects of sulfate and chloride on the microbial corrosion of X80 pipeline steel in the sea mud environment. The results indicate that sulfate serves as an electron acceptor, promoting the growth of SRB, while chloride regulates the osmotic pressure of SRB cells, affecting their bioactivity. SRB exhibit more aggressive corrosion behavior in sea mud with high concentrations of both chloride and sulfate.

Classification and extraction method of hidden dangers along railway lines based on semantic segmentation network

ABSTRACT Foreign objects invading high-speed railway lines can cause danger. One existing solution is to use remote sensing images to analyse the dangerous areas along the railway line, thereby providing a certain amount of investigation time. Considering the spatial and temporal resolution characteristics of existing remote sensing technologies in identifying floating objects and the reality of rapid land use changes, this paper identifies areas on the ground where floating objects may be generated by using semantic segmentation techniques oriented to remotely sensed imagery and provides early warnings to staff along the route. However, these regions that need to be analysed have different semantics and scales. To address these challenges, this paper proposes a Dual-branch Parallel Fusion Network (DPFNet) based on Transformer, aimed at enhancing multi-class semantic segmentation in remote sensing images. To leverage global contextual information, we introduce a Swin Transformer-based backbone network, which employs self-attention to capture a comprehensive scene context, facilitating better segmentation by considering the entire scene’s context. For multi-scale semantic features, we propose one approach that involves independent branching feature expression and a Multi-scale Feature Space Fusion Module (MFSFM). The former enriches multi-scale information, while the latter fuses features across different levels to capture diverse semantic features. Experimental results demonstrate that DPFNet can effectively identify the hidden danger area, and the fusion of multi-scale features makes the network more accurately identify and segment the risk area of different sizes, improving the segmentation accuracy and robustness, and is of great significance to the formation of the ‘prevention’ as the core of the railway safety operation.

Relevance-Based Reconstruction Using an Empirical Mode Decomposition Informer for Lithium-Ion Battery Surface-Temperature Prediction

Accurate monitoring of lithium-ion battery temperature is essential to ensure these batteries’ efficient and safe operation. This paper proposes a relevance-based reconstruction-oriented EMD-Informer machine learning model, which combines empirical mode decomposition (EMD) and the Informer framework to estimate the surface temperature of 18,650 lithium-ion batteries during charging and discharging processes under complex operating conditions. Initially, based on 9000 data points from the U.S. NASA Prognostics Center of Excellence’s random battery-usage dataset, where each data point includes three features: temperature, voltage, and current, EMD is used to decompose the temperature data into intrinsic mode functions (IMFs). Subsequently, the IMFs are reconstructed into low-, medium-, and high-correlation components based on their correlation with the original data. These components, along with voltage and current data, are fed into sub-models. Finally, the model captures the long-term dependencies among temperature, voltage, and current. The experimental results show that, in single-step prediction, the mean squared error, mean absolute error, and maximum absolute error of the model’s predictions are 0.00095, 0.02114, and 0.32164 °C; these metrics indicate the accurate prediction of the surface temperature of lithium-ion batteries. In multi-step predictions, when the prediction horizon is set to 12 steps, the model achieves a hit rate of 93.57% where the maximum absolute error is within 0.5 °C; under these conditions, the model combines high predictive accuracy with a broad predictive range, which is conducive to the effective prevention of thermal runaway in lithium-ion batteries.

Rolling element bearing fault diagnosis under time-varying speed conditions based on improved wavelet packet transform and envelope order tracking

Abstract Rolling element bearings (REBs) are crucial components of rotating machinery and play an important role in industrial production. Failure of REBs can lead to significant economic losses and catastrophic accidents. In order to ensure their safe and stable operation, we propose a novel approach called EOT-IWPT which combines the envelope order tracking (EOT) with an improved wavelet packet transform algorithm (IWPT) for diagnosing failures in REBs under time-varying speed conditions. First, the time- domain vibration signal is resampled at constant angle intervels according to the phase of the rotating shaft to obtain the angle-domian signal. Next, the resampled signal is decomposed to sub-band signals using IWPT. Third, the squared envelope spectra (also called envelope order spectra) of the last layer wavelet packet coefficients are obtained by the Hilbert transform and the Fourier transform. Finally, the diagnostic results can be easily determined based on the squared envelope spectra. The procedure and performance of the proposed approach are illustrated and evaluated through simulation analysis, proving that the proposed method can effectively extract the fault features. Furthermore, experimental analysis results indicate that the proposed method outperforms both EOT-WPT and SK-EOT-BPF.

Blood trauma in veno-venous extracorporeal membrane oxygenation: low pump pressures and low circuit resistance matter

BackgroundVeno-venous extracorporeal membrane oxygenation (VV ECMO) has become standard of care in patients with the most severe forms of acute respiratory distress syndrome. However, hemolysis and bleeding are one of the most frequent side effects, affecting mortality. Despite the widespread use of VV ECMO, current protocols lack detailed, in-vivo data-based recommendations for safe ECMO pump operating conditions. This study aims to comprehensively analyze the impact of VV ECMO pump operating conditions on hemolysis by combining in-silico modeling and clinical data analysis.MethodsWe combined data from 580 patients treated with VV ECMO in conjunction with numerical predictions of hemolysis using computational fluid dynamics and reduced order modeling of the Rotaflow (Getinge) and DP3 (Xenios) pumps. Blood trauma parameters across 94,779 pump operating points were associated with numerical predictions of shear induced hemolysis.ResultsMinimal hemolysis was observed at low pump pressures and low circuit resistance across all flow rates, whereas high pump pressures and circuit resistance consistently precipitated substantial hemolysis, irrespective of flow rate. However, the lower the flow rate, the more pronounced the influence of circuit resistance on hemolysis became. Numerical models validated against clinical data demonstrated a strong association (Spearman’s r = 0.8) between simulated and observed hemolysis, irrespective of the pump type.ConclusionsIntegrating in-silico predictions with clinical data provided a novel approach in understanding and potentially reducing blood trauma in VV ECMO. This study further demonstrated that a key factor in lowering side effects of ECMO support is the maintenance of low circuit resistance, including oxygenators with the lowest possible resistance, the shortest feasible circuit tubing, and cannulae with an optimal diameter.

Application of SHAP and Multi-Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry Enterprises

Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of the tasks of enhancing the energy efficiency of gas industry enterprises. In order to reduce the risks of making incorrect decisions based on the results of short-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a multi-agent approach for the decomposition of production processes using self-generation agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjusting the operation modes of self-generation units and energy-storage systems, optimizing the power consumption schedule, and reducing electricity and power costs. A comparative analysis of various algorithms for constructing decision tree ensembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features. The experiments demonstrated that using the developed method and production process factors reduced the MAE from 105.00 kWh (MAPE of 16.81%), obtained through expert forecasting, to 15.52 kWh (3.44%). Examples were provided of how the use of SHapley Additive exPlanation can increase the safety of the electrical system management of gas industry enterprises by improving experts’ confidence in the results of the information system.

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Research on the stiffness characteristics and contact status of matched angular contact ball bearings considering the effect of varying spatial position of rolling ball

In engineering practice, bearings used as support components are usually installed in pairs in various rotating machines. The mechanical and contact characteristics of bearings play a crucial role in determining the operational accuracy, safety performance and stability of equipment. Numerous studies focused on single bearings have been reported form diffident aspects for enhancing service performance. However, the literature reports on the matched bearings are scarce due to the mutual coupling of rolling elements and the extremely complex stress conditions. Especially the stiffness fluctuation and internal contact status affected by the ball position are difficult to be observed and tested experimentally because of the complex and compact structure, which significantly impacts the safe and reliable operation of rotating machines. To overcome the above shortcomings, this paper investigates the effect of ball position on the stiffness characteristics and contact status of matched angular contact ball bearings based on the coordinate matrix transformation approach and Jones’ statics model. Then, the stiffness results of published literature are used as a benchmark to validate the accuracy of solution method of this paper. Finally, 7206B bearings with different installing forms are used as a numerical example to reveal the effect of ball position on the stiffness characteristics and contact status of matched angular contact ball bearings under different working conditions. The study provides a scientific guidance to investigate the mechanical characteristics and contact mechanism of multi-bearing system.