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Corrosion is the most significant factor affecting pipeline integrity management. It may lead to facility damage, production interruption, environmental pollution and even explosion. Corrosion management developed in recent years is a critical means to ensure the safety and reliability of pipeline systems. However, developing proactive corrosion management is difficult due to limited availability of data on target pipelines and corrosion factors. The motivation of this study is to develop a proactive corrosion management approach based on a predictive framework. The proposed framework integrates multiple corrosion factors, diverse working conditions, multi-phase degradation, and probabilistic analysis to offer an efficient analysis tool. A numerical analysis of corrosion prediction is conducted using the corrosion data of the training pipeline and the target pipeline, and the corrosion rate, corrosion depth, and remaining useful life (RUL) of the target pipeline are evaluated. Based on the RUL information, the maintenance time of the pipeline is further analyzed to maximize the benefits of maintenance. This study offers a comprehensive approach to pipeline corrosion prediction, facilitating proactive maintenance strategies and improving asset management in the pipeline industry.

Large-scale production systems such as offshore oil platform and subsea production system (SPS) usually have the characteristics of long service life, high economic value, high risk, and difficult to maintain. Existing reliability-based design methods are difficult to meet the requirements of high reliability, risk control, and maintenance economy in their full life cycle. In order to solve this problem, a life cycle structural integrity design method integrating operational reliability, risk controllability, and maintenance economy is proposed. Based on the existing reliability design theory, risk and maintenance are added, and the time dimension is extended to the full life cycle of structures. The interactions between the system, the subsystem, and the components are considered. Prediction of component degradation using existing mature theoretical methods or experimental studies is adopted. The advanced first-order second moment method is applied to calculate the structural reliability. Risk assessment and maintenance are based on the results of the reliability analysis. The SPS of an oilfield in the South China Sea is the application scenario and data is collected, and a surface-controlled subsurface safety valve is designed using the proposed method.

The installation of sand screens in open-hole completions in the wellbore is crucial for managing sand production. The main reason for using standalone screens in open-hole completions is their relatively reduced operational complexity compared to other sand control technologies. However, directly applying the screen to the bottom of the hole can lead to an incorrect screen type selection, resulting in an unreliable sand control method. To address this issue, a sand retention test is conducted to evaluate the performance of a standalone screen before field installation. Nevertheless, current sand retention test setups encounter several challenges. These include difficulties in identifying minimum retention requirements, interpreting results in the context of field conditions, and replicating field-specific parameters. The existing sand retention test introduces uncertainties, such as inaccurately replicating field requirements, inconsistent selection of wetting fluids, flow rates, and channel formation, leading to variations in the choice of the optimal screen using this test. In response to these challenges, this study aims to review the sand retention test and propose an improved sand retention method to overcome these problems. The focus of this article is to provide an in-depth analysis of previous sand retention test setups, their contributions to characterizing sand screens, and the parameters utilized in determining test outcomes. Additionally, this review outlines a procedure to investigate the impact of different particle sizes on screen erosion. Key findings emphasize the importance of using high-quality materials, proper screen design to resist damage and erosion, achieving acceptable natural packing behind the screen, and considering factors such as geology, wellbore conditions, and installation techniques. The analysis reveals that a high quantity of finer and poorly sorted sand increases sand production. The study recommends performing a sand pack test closer to reservoir conditions for better evaluation. Premium sand screens demonstrate the highest retention capacity, followed by metal mesh and wire-wrapped screens. Additionally, geotextiles show potential for enhancing sand retention, and screen design affects erosion resistance and service life.

End-to-end intelligent diagnosis of rotating machinery under speed fluctuation and limited samples is challenging in industrial practice. The existing limited samples methods usually focus on the data distribution or learning strategy with particularity. Generative adversarial network (GAN) provides a data generation solution with portability in fault diagnosis with limited samples. However, GAN has problems with gradient vanishing, weak extraction of global features, and redundant training. This article proposes a dual-threshold attention-guided GAN (DTAGAN) to generate high-quality infrared thermal (IRT) images to assist fault diagnosis. First, Wasserstein distance and gradient penalty are combined to design loss function to avoid gradient vanishing. Second, attention-guided GAN is constructed to extract global thermal-correlation features of IRT images. Finally, dual-threshold training mechanism is developed to improve the generation quality and training efficiency. The comparative experiments show that DTAGAN is superior to comparison methods in fault diagnosis of rotor-bearing system under speed fluctuation and limited samples.

Due to the high price of the product and the limitation of laboratory conditions, reliability tests often get a small number of failed samples. If the data are not handled properly, the reliability evaluation results will incur grave errors. In order to solve this problem, this work proposes an artificial intelligence (AI) enhanced reliability assessment methodology by combining Bayesian neural networks (BNNs) and differential evolution (DE) algorithms. First, a single hidden layer BNN model is constructed by fusing small samples and prior information to obtain the 95% confidence interval (CI) of the posterior distribution. Then, the DE algorithm is used to iteratively generate optimal virtual samples based on the 95% CI and small samples trends. A reliability assessment model is reconstructed based on double hidden layers BNN model by combining virtual samples and test samples in the last stage. In order to verify the effectiveness of the proposed method, an accelerated life test (ALT) of the subsurface electronic control unit (S-ECU) was carried out. The verification test results show that the proposed method can accurately evaluate the reliability life of a product. And compared with the two existing methods, the results show that this method can effectively improve the accuracy of the reliability assessment of a test product.

Difficult-to-cut materials, such as superalloys and titanium alloys, have been increasingly used in aerospace and other fields. However, commonly used mechanical milling methods for difficult-to-cut materials have problems, including low processing efficiency and severe tool wear. Electrical discharge assisted arc milling (EDAAM) is a novel high-efficiency machining method proposed by the authors to solve these processing problems. The machining efficiency achieved by EDAAM when processing difficult-to-cut materials is much higher than that achieved by mechanical milling and conventional electrical discharge machining (EDM) milling. However, if EDAAM adopts the traditional EDM servo control method, it will easily form deep and large discharge craters on the surface of the workpiece, which may lead to high machining errors and cause the workpiece to be scrapped. To ensure normal processing, low-energy discharge parameters that may reduce the processing efficiency have to be adopted. To address the above issues and achieve the optimal machining condition of difficult-to-cut materials, mathematical models and system of intelligent servo for EDAAM are proposed. An EDAAM discharge information dataset is obtained, and the intelligent recognition and classification method for discharge states is proposed. The EDAAM intelligent servo control system is proposed based on mathematical models created. The experimental results of superalloy Inconel 718 by EDAAM show that, compared with the nonintelligent servo control system, the intelligent servo control system can increase the material removal rate by up to ten times, reduce the relative electrode wear rate by 37%, and avoid the scrapping of the workpiece.

The preparation of reinforcing layer with good performance on the internal surface of oil/gas pipeline is the key to ensure the long-term service of the pipeline in harsh environment. Common treatment methods are mainly adding lining, covering organic coating and surface strengthening, which have problems such as high economic cost, complicated process and serious pollution. In this paper, a new method for active/inert metal combined anode jet electrodeposition (A/IMCA JED) is presented. The method uses metallic nickel and iron as the active anode and metallic titanium as the inert anode. The inert anode runs through and is in close contact with the active anode, and the inert anode is closer to the internal surface of the pipeline. In the A/IMCA JED process, as the metal ions in the plating solution are consumed, the active anode participates in the reaction to replenish them. The inert anode provides a continuous and stable inter-electrode electric field without participating in the reaction, which is beneficial to obtain a coating with high structural and organizational uniformity. The influence of the plating solution composition on the microstructure, hardness and adhesion force of Ni-Fe-SiC coating was studied, and the optimal parameters were obtained: FeCl2of 15 g/L and SiC particles of 4 g/L. Under these process parameters, a Ni-Fe-SiC coating was prepared on the internal surface of the short oil pipe. Compared with conventional JED, A/IMCA JED has higher deposition current density, which increases the deposition rate by 231.9 %. A/IMCA JED can effectively promote nucleation and microstructure strengthening, which can reduce the average grain size by 44.1 % and increase the hardness by 16.9 %. A/IMCA JED can significantly reduce the pinholes and protrusions of the coating surface, and the surface roughness Ra can reach 0.064 μm. Furthermore, the wear and corrosion resistance of pipeline have been significantly improved. This research has an important significance for the promotion of JED technology and the preparation of high-performance pipelines.

The application of convolutional neural network (CNN) has greatly promoted the scope and scenario of intelligent fault diagnosis and brought about a significant improvement of intelligent model performance. Solving the feature extraction and fault diagnosis of machinery with heavy noise is beneficial for stable industrial production. However, the local properties of CNN prevent it from obtaining global features to collect sufficient fault information, leading to the degradation of fault diagnosis performance of CNN under heavy noise. In this article, a novel framework named Convformer-NSE is developed to extract robust features that integrate both global and local information, aiming at improving the end-to-end fault diagnostic performance of gearbox under heavy noise. First, Convformer is constructed to improve the nonlinear representation of the feature map, in which the sparse modified multi self-attention is used to model the long-range dependency of the feature map while keeping attention on local features. Then, the extracted spatial features at various scales are fused and fed in the designed novel Senet (NSE) for channel adaptivity learning. The Convformer-NSE is used for the analysis of raw vibration data of different gearbox systems. The experimental signal analyses demonstrate that our developed framework is superior to others.

A large amount of quality-related but difficult-to-measure variables usually make industrial processes unable to be operated safely and efficiently. Nonlinearity, time delay, and dynamics usually add further complexity and difficulties to this issue. To achieve accurate and reliable prediction of such quality-related but difficult-to-measure variables, a novel hierarchical soft sensor, termed spatiotemporal information transformation autoregressive moving average (SIT-ARMA), is proposed in this article to enable single-step and multistep-ahead prediction of these hard-to-measure variables. Inspired by hierarchical ensemble learning, this method applies the spatiotemporal information transformation (SIT) model to model the original dataset and the autoregressive moving average (ARMA) model to model the residuals from the SIT model. Furthermore, to increase the robustness of the SIT-ARMA model, this article uses the median and the median absolute deviation (MAD) to approach the Cauchy distribution, which can identify and smooth the outliers of the prediction derived by the SIT model. In addition, the moving window (MV) approach is applied to the SIT-ARMA model to update the parameters online and improve the adaptive performance of the model. The superiority of the proposed SIT-ARMA method has been verified through two studies of wastewater treatment plants (WWTPs) with different process characteristics.

In closed-loop feedback control systems, faults are propagated through the feedback link, which eventually leads to the abnormality of the entire system. Generally, it is very difficult to identify the faults of systems under the influence of the closed-loop feedback link. The existence of redundancy improves the reliability of the system. Meanwhile, it also poses new challenges to the fault diagnosis of multiple redundant systems. In this regard, a causality-based method is proposed for the fault diagnosis of closed-loop feedback control system with multiple modular redundancy. The dynamic Bayesian networks for fault diagnosis are established based on sensor data and system parameters. The networks consist of four layers, which are sensors, performances, monitors, and faults, respectively. Furthermore, the conditional probabilities of the fault nodes are calculated by Noisy-OR and Noisy-MAX models. The proposed method can dynamically evaluate system performance and integrate other monitoring information as evidence to assist faults diagnosis and location. A double modular redundant control system for a subsea blowout preventer is used as a case to demonstrate the proposed method, and the results show that the proposed method has high accuracy. The influence of sampling frequency, noise, and redundancy mode on diagnosis results is studied and discussed in the case study.

The nickel-based superalloy Inconel 718 is treated with Coupled Ultrasonic and Electric Pulse Treatment (CUEPT), and the surface grain is refined from the average size of 9550.0 nm to 287.9, 216.3, 150.5, 126.3, 25.8 nm by different effective treatment currents, respectively. The ultra-precision turning experiments are carried out on the processed workpiece after CUEPT. The experimental results show that the average cutting force increases with the decrease of surface grain size. Moreover, a mathematical model that can describe the relationship between grain size and cutting force is established, and the calculated results match the experimental results well. The calculated results also indicate that the variation of cutting force caused by the same variation of grain size decreases as the degree of grain refinement increases. Finally, the influence mechanism of grain refinement on cutting force is analyzed. The improvement of stability of grain boundaries and the increase of number of grain boundaries cause the increase of cutting force after grain refinement.

In this paper, the novel rotating magnetic field-assisted electrodeposition method to prepare the Ni-SiC nano-composite coating is proposed. The microstructure, nanoparticle content and thickness of the coating are characterized by scanning electron microscopy, energy dispersive spectroscopy and X-ray diffraction. Moreover, the micro-hardness, corrosion resistance of the coating and adhesion between the substrate and coating are tested. Results show that the rotating magnetic field can improve the surface morphology and reduce the surface defects. When the bath temperature is 50℃, pH is 3.5 ± 0.1, current density is 10 A/dm2, electroplating time is 30 minutes, stirring speed is 400 rpm, and the concentration of SiC in the plating solution is 1 g/L, compared with the traditional immersion electrodeposition and the constant magnetic field-assisted electrodeposition, the rotating magnetic field-assisted electrodeposition can increase the coating thickness by 10.4% and 3.4%, the coating micro-hardness by 9.0% and 3.1%, the adhesion between the substrate and coating by 29.3% and 19.1%, and reduce the self-corrosion current density by 96.3% and 79.0%, respectively.

The unproductive time is very high due to drill string jamming. So the main objective of this research is to determine the influences of the parameters in the accidents of stuck pipes using the construction of XGBoost models. To develop the model, drilling parameters are taken from daily drilling reports of the construction of 30 wells in the West Qurna field, Iraq. The data includes well drilling reports from 2013 to 2020. The results show that the factors such as Measured depth (MD), Rate of penetration (ROP), 10-sec gel (GEL1), Plastic Viscosity (PV), Mud weight (MW), Yield point (YP) contribute positively to the model predictions. In contrast, the factors such as Flow rate (FR), Rotation per minute (RPM), Filtrate (API/HPHT-FILTR and Bottom hole assembly (BHA) length has a negative contribution to the final model predictions. This research concluded that pipe sticking in the borehole is primarily due to inclination, penetration rate, and flow rate. This study is useful in the drilling of any field.

Abstract: Silicon carbide (SiC) ceramic has been widely used in modern industry. However, the beneficial properties of SiC ceramic make machining difficult and costly by conventional machining methods. This paper proposes a new process of machining SiC ceramic using end electric discharge (ED) milling. The process is able to effectively machine a large surface area on SiC ceramic at low cost and no environmental pollution. The effects of emulsion concentration, emulsion flux, milling depth, copper electrode number, and copper electrode diameter on the process performance such as the material removal rate, electrode wear ratio, and surface roughness have been investigated. In addition, the microstructure of the machined surface is examined with a scanning electron microscope, and the material removal mechanism of SiC ceramic during end ED milling is obtained.

Nickel-iron alloy is broadly utilized in the industrial field due to its fabulous mechanical properties. How to prepare nickel-iron alloy with better corrosion resistance more efficiently is an important concern. Based on this, the WC particles enhanced nickel-iron coatings were fabricated by jet electrodeposition (JED) at a high current density of 100A/dm2. It was found that increasing the deposition temperature appropriately during JED is beneficial to obtain Ni-Fe-WC coating with the higher number of hard particles. Specifically, the maximum WC particles proportion reaches 4.47 wt% at 55°C. The electrochemical corrosion behavior of coating was measured by polarization curve and electrochemical impedance spectroscopy (EIS). The polarization curve test results displayed that the Ecorrpositively shifted from -0.402 to -0.281 V and the icorrreduced from 25.13 to 7.05 μA/cm2with the increase of WC particles proportion. Meanwhile, the EIS test results exhibited that the impedance of coating enhanced from 12720 to 59140 Ω·cm2. The neutral salt spray test (NSS) was used to simulate the corrosion of coating in a seawater environment. The NSS results showed that with the increase of WC particles, the corrosion products decreased gradually and the degree of corrosion decreased. The analysis showed that WC particles can help the grain growth and release the internal stress in the coating. Larger grain sizes reduces intergranular corrosion and smaller internal stress help to alleviate stress corrosion cracking. The above research can provide theoretical support for the efficient preparation of particle enhanced composite coating with strong corrosion resistance.

Nanoparticle-reinforced metal matrix composite coatings have significant potential in mechanical part surface strengthening owing their excellent mechanical properties. This paper reports a phenomenon in which the grain orientation gradually evolves to (220) as the deposition current density increases when preparing nanoparticle-reinforced nickel-based composite coatings through jet electrodeposition (JED). During the preparation of the Ni-SiC composite coatings, the deposition current density increased from 180 A/dm2to 220 A/dm2, and TC(220) gradually increase from 41.4% to 97.7%. With an increase of TC(220), the self-corrosion potential increases from -0.575 to -0.477 V, the corrosion current density decreases from 9.52 µA/cm2to 2.76 µA/cm2, the diameter of the corrosion pits that after 10 days of immersion in a 3.5 wt% NaCl solution decreases from 278–944 nm to 153–260 nm, and the adhesion of the coating increases from 24.9 N to 61.6 N. Compared a conventional electrodeposition (CED), the Ni-SiC composite coating using JED has the advantages of a smooth surface morphology, high corrosion resistance, and strong adhesion, which are more obvious with an increase in TC(220).

This study focuses on the load and resistance factor design (LRFD) procedure for anisotropic long composite cylinders under external pressure. The theoretical analysis is performed, the performance function is presented, the target reliability levels are determined, the statistical characteristics of random variables are researched and the LRFD-based code is calibrated. The results show the LRFD-based design procedure provides explicit and consistent reliability for long composite cylinder, which could lead less conservative design.

A subsea blowout preventer experimental prototype is developed to meet the requirement of training operators, and the prototype consists of hydraulic control system, electronic control system and small-sized blowout preventer stack. Both the hydraulic control system and the electronic system are dual-mode redundant systems. Each system works independently and is switchable when there are any malfunctions. And it significantly improves the operation reliability of the equipment.