Operation Status Research Articles

Diagnosing sucker-rod pumping units by means of a neural network module implemented on the basis of an oilwell controller

Currently, sucker-rod pumping (SRP) units are widely used in oil wells, due to two important factors: costs and ease of use. To optimize production using sucker rod pumps, well Rod Pump Controllers (RPC) have been used for more than 20 years. Today the main method of controlling the sucker-rod pumping unit is the analysis of dynagraph card (determination of the state and mode of operation of the pump based on the dependence of the rod’s load on its position). The qualitative analysis of a dynagraph (DMG) comes down to diagnostics, which detects and identifies signs of possible malfunctions of the pump and the well as a whole. The task of determining malfunctions by a dynagraph is considered difficult for a computer analysis and involves the evaluation of a dynagraph card by a qualified technologist. This article discusses methods and algorithms for determining malfunctions from obtained RPC dynagraphs, implemented directly on base of the well controller. These methods can be divided into analytical and neural network methods. Analytical algorithms describe the DMG defect in the form of formulas or logical dependencies. Neural network methods approach the problem of defect recognition as recognizing a set of unrelated data. This helps to work with a system with a large number of parameters, such as a (SRP). The Naftamatika took into account the pros and cons of previous approaches and developed its own neural network model. The development consisted of selecting the type of network, the number of its layers, creating a spatial structure of neurons, creating conditions for training the network and selecting an adequate training sample. To train the controller, a program was used in which thousands of dynamograms with various defects and conditions were loaded. This network model is implemented in a Wellsim pump controller, which provides sufficient performance, the algorithm successfully detects insufficient filling, gas factor and other faults with an accuracy of 96 %.

Multidimensional Edge Perception Model for Rail Vehicle Operational States Based on Artificial Intelligence of Things

Multidimensional Edge Perception Model for Rail Vehicle Operational States Based on Artificial Intelligence of Things

Robots for the Energy Transition: A Review

The energy transition relies on an increasingly massive and pervasive use of renewable energy sources, mainly photovoltaic and wind, characterized by an intrinsic degree of production uncertainty, mostly due to meteorological conditions variability that, even if accurately estimated, can hardly be kept under control. Because of this limit, continuously monitoring the operative status of each renewable energy-based power plant becomes relevant in order to timely face any other uncertainty source such as those related to the plant operation and maintenance (O&M), whose effect may become relevant in terms of the levelized cost of energy. In this frame, the use of robots, which incorporate fully automatic platforms capable of monitoring each plant and also allow effective and efficient process operation, can be considered a feasible solution. This paper carries out a review on the use of robots for the O&M of photovoltaic, wind, hydroelectric, and concentrated solar power, including robot applications for controlling power lines, whose role can in fact be considered a key complementary issue within the energy transition. It is shown that various robotic solutions have so far been proposed both by the academy and by industries and that implementing their use should be considered mandatory for the energy transition scenario.

First-Principles Investigation on Ru-Doped Janus WSSe Monolayer for Adsorption of Dissolved Gases in Transformer Oil: A Novel Sensing Candidate Exploration.

Using first-principles theory, this work purposes Ru-doped Janus WSSe (Ru-WSSe) monolayer as a potential gas sensor for detection of three typical gas species (CO, C2H2, and C2H4), in order to evaluate the operation status of the oil-immersed transformers. The Ru-doping behavior on the WSSe surface is analyzed, giving rise to the preferred doping site by the replacement of a Se atom with the formation energy of 0.01 eV. The gas adsorption of three gas species onto the Ru-WSSe monolayer is conducted, and chemisorption is identified for all three gas systems with the adsorption energy following the order: CO (-2.22 eV) > C2H2 (-2.01 eV) > C2H4 (-1.70 eV). Also, the modulated electronic properties and the frontier molecular orbital are investigated to uncover the sensing mechanism of Ru-WSSe monolayer upon three typical gases. Results reveal that the sensing responses of the Ru-WSSe monolayer, based on the variation of energy gap, to CO, C2H2, and C2H4 molecules are calculated to be 1.67 × 106, 2.10 × 105, and 9.61 × 103, respectively. Finally, the impact of the existence of O2 molecule for gas adsorption and sensing is also analyzed to uncover the potential of Ru-WSSe monolayer for practical application in the air atmosphere. The obtained high electrical responses manifest strong potential as a resistive sensor for detection of three gases. The findings hold practical implications for the development of novel gas sensing materials based on Janus WSSe monolayer. We anticipate that our results will inspire further research in this domain, particularly for applications in electrical engineering where the reliable detection of fault gases is paramount for maintaining the integrity and safety of power systems.

Research on Gate Charge Degradation of Multi-Chip IGBT Modules in Power Supply for Unmanned Aerial Vehicles

In recent years, with the burgeoning application of high voltage in various industrial sectors, the deployment of unmanned equipment, such as industrial heavy-load Unmanned Aerial Vehicles (UAVs), incorporating high-capacity Insulated-Gate Bipolar Transistors (IGBTs), has become increasingly prevalent. The demand for high-voltage IGBT modules in UAV is continuously growing; therefore, exploring methods to predict fault precursor parameters of multi-chip IGBT modules is crucial for the operational health management of unmanned equipment like UAVs. This paper analyzes the gate charge degradation in multi-chip IGBT modules after thermal cycling, which can be used to evaluate the operational state of these modules. Furthermore, to delve into the electrical response of a gate drive circuit caused by local damage within the IGBT module, an RLC model incorporating parasitic parameters of the gate drive circuit is established, and a sensitivity analysis of the peak current in the gate charge circuit is provided. Additionally, in the experimental circuit, an open sample of an IGBT module with partial bond wires lifted off is used to simulate actual faults. The analysis and experimental results indicate that the peak current of the gate charge is closely related to L and C. The significant deviation in the gate current, influenced by the partial bond wires lift-off, can provide a basis for the development of predictive methods for IGBT modules.

A novel weighted-guided tensor completion missing data imputation method for health monitoring data of planar parallel mechanism

Abstract High-end mechanical equipment plays a crucial role in the manufacturing industry, making the monitoring of its operational status highly significant. Due to various factors such as environmental influences, the absence of monitoring signals in mechanical equipment status is a common issue, leading to a decline in data quality. To ensure data quality, this paper proposes an adaptive weighted low-rank tensor missing data imputation method. Firstly, based on the motion characteristics of a planar parallel mechanism (PPM), a new low-rank tensor model is established using periodicity, sliding windows, and time series. Secondly, the tensor truncation nuclear norm is defined, and a novel rate parameter is introduced to control the truncation degree of all tensor modes, thereby obtaining weights for each dimension. Finally, within the framework of the alternating direction method of multipliers (ADMM), adaptive weights for each dimension are obtained during each iteration, completing the filling of missing data. Two types of missing patterns are studied on a PPM experimental platform, and the results showed that as the missing rate increased, the mean absolute percentage error is less than 0.018, and root mean square error is less than 0.001, and the rate of change is less than 0.08, which was significantly better than other compared algorithms.

State estimation of a biogas plant based on spectral analysis using a combination of machine learning and metaheuristic algorithms

The continuous monitoring of the state variables of a biogas plant remains a challenge due to the necessity of an appropriate measuring device. The collection, transportation, and laboratory measurement of the biogas sample are required, and regularly performing these activities is expensive and time-consuming. The objective of this study is to investigate a potential solution for the real-time monitoring of the state variables of a biogas plant, which involves the integration of spectral data from near-infrared (NIR) sensors with advanced machine learning (ML) and metaheuristic algorithms. A total of 635 samples were prepared in a laboratory and subsequently analyzed using portable NIR sensors mounted in sensor brackets that were 3D-printed for this study. The resulting spectral data were subjected to several preprocessing processes, and this study combines numerous methods rather than selecting the optimal one. Several ML models were then trained on the processed data, which have a realistic range as in the actual biogas plant. The developed algorithm exhibits an accuracy level greater than 82 % when classifying the volatile fatty acids (VFA)/total alkalinity (TA) ratio and acetic acid concentration of biogas samples. Furthermore, the dry matter content of biogas samples can be accurately predicted with an RMSE of approximately 1.6 %. In addition, the significance of selecting suitable hyperparameters is demonstrated, which may substantially influence the outcome. These findings indicate that NIR sensors are a realistic option for monitoring the operational status of biogas plants.

Bearing faults classification using novel log energy-based empirical mode decomposition and machine Mel-frequency cepstral coefficients

The accurate diagnosis of faults in bearing components is crucial for the safe and efficient operation of electrical and power drives. These machines generate sound and vibration signals that indicate their operational state. While vibration signals are often utilized for fault diagnosis, they require costly transducers. On the other hand, sound signal transducers are more affordable, but their lower signal-to-noise ratio complicates the differentiation between healthy and faulty bearings. This paper addresses these challenges by introducing a machine sound-based bearing fault diagnosis system. The proposed method employs a novel Log Energy-based Empirical Mode Decomposition and Reconstruction for advanced sound preprocessing. Feature extraction is performed using Machine Mel-frequency Cepstral Coefficients, with feature selection facilitated by a Genetic Algorithm. Classification is achieved through Support Vector Machines. The system demonstrated a high classification accuracy of 99.26% on the SUBF v2.0 dataset, outperforming other diagnostic methods, even in noisy conditions. This approach is particularly suited for industrial applications, offering a reliable solution for preventing downtime and ensuring the reliability of equipment.

An investigation of PPP-B2b coverage and its performance in ZTD estimation and positioning in different regions

This study comprehensively evaluates the Beidou navigation satellite system's PPP-B2b service for real-time positioning and zenith total delay (ZTD) estimation. Despite its operational status, PPP-b2b's effective coverage is underexplored. We define this coverage by analysing satellite visibility and geometry and evaluating ZTD and positioning accuracy at 48 stations. Results show PPP-B2b achieves ZTD Errors of 10–20 mm in China and 15–30 mm in Europe, with kinematic experiments in China and Japan showing consistent accuracy within 20 mm. This study highlights PPP-b2b's potential for enhancing atmospheric monitoring and precise positioning in its effective regions.

Intelligent Monitoring and Optimization of Wind Turbine Operation Status on Basis of Vibration Signals

In response to the large monitoring errors and incomplete consideration of fault locations in the operation status of wind turbines, this paper combined vibration signals and used a model constructed using nonlinear state estimation technology (NSET) to monitor and optimize the operation status of wind turbines. Firstly, it collected relevant SCADA (supervisory control and data acquisition) and vibration sensor data of Unit 5 of Guangdong Yangjiang Shaba Offshore Wind Farm on site, and used wavelet transform to denoise the vibration signal. Then, full consideration can be given to the faulty parts such as wind blades and gearboxes, and a model constructed by NSET using Markov distance to construct a process memory matrix can be used to monitor the status of the wind turbine equipment. Finally, the multi-level fuzzy comprehensive evaluation method can be used to optimize the comprehensive evaluation of the operating status of wind turbines, reducing monitoring errors.

Research on power operation typical characteristic state of proton exchange membrane fuel cell based on principal component analysis

The stability, reliability, and lifespan of proton exchange membrane fuel cells (PEMFCs) are closely related to power dynamic response. Therefore, it is necessary to study in depth the relationship between the power operation state and typical characteristics. In this paper, the fuel cell system (FCS) power dynamic response based on constant power change amplitude and various power change amplitude is analyzed. Then, three typical power characteristics of the FCS, namely, low single-cell voltage, drastic voltage uniformity change, voltage undershoot and voltage overshoot, are investigated. Furthermore, to solve the high-dimensional datasets problem, a quick evaluation method based on data dimensionality reduction (DDR) is proposed. The innovation of this method is based on principal component analysis to extract typical characteristic relationships of power operation levels. The results demonstrate that the extracted new one-dimensional (1-D) eigenvectors index exhibits a positive correlation with power levels. Meanwhile, the power operation state empirical model is established through data regression. The Pearson coefficients of the empirical model are all greater than 0.9981. Thus, the new index and proposed method offer novel insights for improving the power adaptive control of fuel cells and quickly assessing their health state.

A strain-based J-integral formulation for an internal circumferential surface crack of pipeline under inner pressure and large axial deformation

The presence of cracks on pipelines poses a potential threat to their operational status, and it is critical to assess the permissibility of pipelines containing cracks. The dimensional analysis combined with the finite element method is applied to investigate the fracture behavior of circumferential crack on the internal surface of pipe under internal pressure and large axial deformation. Dimensionless parameters are determined to represent the effects of crack size, pipe geometry, pipe material, and external load on the crack front driving force, and a strain-based J-integral formulation is obtained by a stepwise coefficient fitting approach rather than a polynomial fitting method. This J-integral formula can be used to quickly assess the crack front driving force of a pipe in service condition and subjected to axial displacement. The diameter-to-thickness ratio of the pipe and the dimensionless pressure of the pipe are found to act together in a combined form on the crack front driving force. Increases in dimensionless crack depth, dimensionless crack length, the ratio of circumferential stress to yield strength of the pipe, and strain hardening exponent cause an increase in the crack front driving force. The effect of dimensionless crack depth on crack front driving force is more significant than other dimensionless parameters. Changes in the other dimensionless parameters do not significantly change the crack front driving force when the dimensionless crack depth is small. Other dimensionless parameters have a progressively greater influence on the crack front driving force as the crack dimensionless crack depth increases. Large deformations in the ligament zone and increasing axial stress are the main reasons for the high crack front driving force. The J-integral formula has a similar form to that of the J-integral in the Electric Power Research Institute (EPRI) method when the effect of internal pressure is not considered. It can be reduced to predict the crack front driving force of a surface cracked plate subjected to uniaxial tensile loading. For the interaction between an internal surface crack and an embedded crack, re-characterizing the crack size using BS 7910 will overestimate the equivalent crack depth, and a more accurate equivalent crack size can be obtained using the J-integral formula proposed.

Operational State Complexity of Block Languages

Operational State Complexity of Block Languages

Decentralized distributionally robust chance-constrained operation of integrated electricity and hydrogen transportation networks

Hydrogen energy offers a promising pathway to decarbonizing future energy systems. Renewable energy-based hydrogen production and road transportation-based hydrogen delivery have strengthened the coupling between the power distribution network (PDN) and the hydrogen transportation network (HTN). In this article, we propose a decentralized distributionally robust chance-constrained (DRCC) method for the integrated electricity and hydrogen transportation network (IEHTN) to foster synergies between PDN and HTN. First, an optimal scheduling framework is developed to coordinate hydrogen production and transportation in the IEHTN, where multiple operational states of water electrolyzers and the time-constrained tube trailer routing are modeled. Second, a data-driven DRCC approach is proposed to model the uncertain behaviors of renewable power generation and hydrogen demand. A conditional value-at-risk approximation is then employed to convert the DRCC problem to a tractable mixed-integer linear program that can be solved by the off-the-shelf solver. Third, an alternating direction method of multipliers (ADMM) based solving scheme is designed to solve the problem in a distributed manner with limited information exchange between PDN and HTN. To address the non-convexity brought by binary variables in the ADMM, a tractable alternating optimization procedure (AOP) strategy is utilized to guarantee the convergence of solution. Finally, case studies are conducted on an IEHTN composed of the modified IEEE 33-bus PDN and 52-node HTN to verify the validity of the proposed method.

Crack-Based Composite Flexible Sensor with Superhydrophobicity to Detect Strain and Vibration.

Vibration sensors are widely applied in the detection of faults and analysis of operational states in engineering machinery and equipment. However, commercial vibration sensors with a feature of high hardness hinder their usage in some practical applications where the measured objects have irregular surfaces that are difficult to install. Moreover, as the operating environments of machinery become increasingly complex, there is a growing demand for sensors capable of working in wet and humid conditions. Here, we present a flexible, superhydrophobic vibration sensor with parallel microcracks. The sensor is fabricated using a femtosecond laser direct writing ablation strategy to create the parallel cracks on a PDMS film, followed by spray-coating with a conductive ink composed of MWCNTs, CB, and PDMS. The results demonstrate that the developed flexible sensor exhibits a high-frequency response of up to 2000 Hz, a high acceleration response of up to 100 m/s2, a water contact angle as high as 159.61°, and a linearity of 0.9812 between the voltage signal and acceleration. The results indicate that the sensor can be employed for underwater vibration, sound recognition, and vibration monitoring in fields such as shield cutters, holding significant potential for mechanical equipment vibration monitoring and speech-based human-machine interaction.

Laparoscopic Cholecystectomy for Gallbladder Polyps: Is It Overtreatment?

The gallbladder polyp (GP) is an accepted risk factor of gallbladder cancer and an indication for laparoscopic cholecystectomy (LC). Generally, the pathologic result of GPs is benign, but it is difficult to distinguish a potential malignancy or a stone without pathological evaluation. This study compared the indication and pathologic result of cholecystectomy performed due to GP in our clinic. This study employed retrospective data analysis. Patients who underwent LC from August 2021 through August 2024 were included in the study. Demographic features, operation status, indications for surgery, hospital stay, concomitant surgery, pathologic outcomes, and complications were recorded from patients' data. Polyp sizes and number of polyps were taken from ultrasonography (USG) data. A total of 533 patients were included in the study. The mean age was 44.31 ± 12.14, and 64.35% (n = 343) were of female gender. Twenty patients (3.75%) underwent surgery for GP. The mean polyp size was 7.47 mm (2-15); 65% of the patients (n = 13) had multiple polyps, and 35% (n = 7) had a single polyp. The mean hospital stay was 1.59 ± 0.88 days. The pathologic result of GP was pseudopolyp in 55% (n = 11) of cases and non-polyp in 45% (n = 9). One patient (0.18%) who underwent an operation for gallstone had a malignancy. The sensitivity of USG in detecting polyps was found to be 64.7%. The complication rate was 1.5% (n = 8). The pathological result of many patients who undergo cholecystectomy due to GPs is pseudopolyp or adenoma. In our study, no carcinoma was observed in any patient who underwent surgery for polyps. Further studies are needed to determine the indication for surgery due to GP.

A fault diagnosis method based on an improved diffusion model under limited sample conditions.

As a critical component in mechanical systems, the operational status of rolling bearings plays a pivotal role in ensuring the stability and safety of the entire system. However, in practical applications, the fault diagnosis of rolling bearings often encounters limitations due to the constraint of sample size, leading to suboptimal diagnostic accuracy. This article proposes a rolling bearing fault diagnosis method based on an improved denoising diffusion probability model (DDPM) to address this issue. The practical value of this research lies in its ability to address the limitation of small sample sizes in rolling bearing fault diagnosis. By leveraging DDPM to generate one-dimensional vibration data, the proposed method significantly enriches the datasets and consequently enhances the generalization capability of the diagnostic model. During the model training process, we innovatively introduce the feature differences between the original vibration data and the predicted vibration data generated based on prediction noise into the loss function, making the generated data more directional and targeted. In addition, this article adopts a one-dimensional convolutional neural network (1D-CNN) to construct a fault diagnosis model to more accurately extract and focus on key feature information related to faults. The experimental results show that this method can effectively improve the accuracy and reliability of rolling bearing fault diagnosis, providing new ideas and methods for fault detection and prevention in industrial applications. This advancement in diagnostic technology has the potential to significantly reduce the risk of system failures, enhance operational efficiency, and lower maintenance costs, thus contributing significantly to the safety and efficiency of mechanical systems.

Spike load smoothing strategy considering behavioral decision under vehicle-road-network convergence

To reduce the impact of large-scale electric vehicles on traffic and electric load peaks, we propose a "vehicle-road-network" integration strategy that takes into account behavioral decisions. To reduce the impact of large-scale electric vehicles on traffic and electric load, we propose a peak load suppression strategy that takes into account behavioral decisions under "vehicle-road-network" integration. Firstly, a "vehicle-road-network" fusion model is constructed based on the large-scale access of EVs; then, a load model is built considering the large-scale access of EVs; finally, based on the third-generation foreground theory, the behavioral decision of EV users is analyzed based on the operation state of the fusion system, and a decision model is built by the foreground value to minimize the operation cost. The spike load smoothing strategy scheme is established with minimum operation cost. By using the fusion of Sioux Falls urban road network system and IEEE33 node grid system as the simulation example, the simulation results show that the proposed peak load smoothing strategy can coordinate the distribution of charging load and relieve traffic pressure during peak load hours, and reduce the travel and charging costs of EV users.

Review on the Evaluation and Improvement Measures of the Carrying Capacity of Distributed Power Supply and Electric Vehicles Connected to the Grid

With the increasing number of distributed power sources such as photovoltaic power and wind power and electric vehicles connected to the grid, the structure and operation state of the traditional distribution network have undergone great changes. Therefore, through the establishment of a distributed power grid-connected evaluation system, it has become an important research topic to evaluate the access of distributed power and the carrying capacity of electric vehicles in the distribution network of new power systems. Firstly, the situation of distributed power supply and electric vehicles and the impact of grid connection are introduced. Secondly, the traditional distribution network carrying capacity evaluation method is studied and introduced. Then, the distribution network carrying capacity evaluation considering uncertainty is reviewed and investigated. Finally, in view of the lack of existing research, further research is needed, aiming to provide a reference for the realization of distributed power supply and grid-connected EV carrying capacity evaluation and a scientific basis for the future operation planning of distributed power supply and EV integration into the distribution network.

Optimizing energy consumption in smart buildings: A model for efficient energy management and renewable integration

In the current landscape of sustainable energy, integrating photovoltaic (PV) generation and electric vehicles (EVs) into household energy management systems (EMSs) has become a pivotal strategy to enhance grid sustainability in residential areas and provide economic benefits to homeowners. This article introduces an innovative approach to an energy administration framework designed for intelligent households, to seamlessly integrate EVs, both with and without PV generation. The proposed plan meticulously optimizes residential electricity expenses. It standardizes the load gradient using Time-of-Use (TOU) cost, residential electricity use fluctuations, PV generation patterns, and specific variables of EVs, including entry and exit times and charging statuses. The management method is divided into two distinct phases based on charging constraints and initial charge status. Stage A comprises three operational states contingent upon the availability of PV generation, while Stage B involves five operational states predicted based on anticipated PV generation. Through this research, the advanced control strategy facilitates precise adjustment of energy absorption by EVs from the grid and/or PV generation, along with injected energy derived from EVs to the grid. It ensures minimal residential electricity expenses and synchronizes the electrical load profiles. The findings indicate a significant reduction in residential electricity expenses and a substantial normalization of power load characteristics. Furthermore, simulation results emphasize the superior performance of the proposed plan for intelligent households fitted with both EVs and PV generation, compared to households with EVs alone, in terms of lowering expenses and normalizing load scenarios.