Severe Working Conditions Research Articles

A hysteresis model considering temperature effects for sintered NdFeB and its application in electromagnetic buffer

With the continuous expansion of the application field of sintered neodymium iron boron (NdFeB), its demagnetization behavior in specific devices deserves further attention. The systematic research on the demagnetization model of NdFeB and its application in specific devices was yet studied. To end this, a suite of tests was implemented to obtain the demagnetization curves at different temperatures of NdFeB. Referred to Jiles-Atherton (J-A) model, the hysteresis model used herein was fitted by the experimental data, so that a set of improved J-A model parameters for the tested materials was achieved. The model was further applied to the simulation calculation of electromagnetic buffer and the corresponding experimental research was also carried out. The simulation model was verified by comparing the electromagnetic buffering force and the buffering displacement obtained from both the simulations and experiments. The finite element model that had been verified by experiments was further applied to the calculation of more severe and complex working conditions. This article takes a specific application of sintered NdFeB as an example. Starting from the energy source, the influence of the nonlinearity of the sintered NdFeB hysteresis loop and temperature on the working process are fully considered. The calculation results of the simulation model provide a reference for the design of the electromagnetic buffer, and also provide ideas for the simulation and design of other electromagnetic devices

Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions.

Considering various fault states under severe working conditions, the comprehensive feature extraction from the raw vibration signal is still a challenge for the diagnosis task of rolling bearing. To deal with strong coupling and high nonlinearity of the vibration signal, this article proposes a novel multilocation and multikernel scale learning framework based on deep convolution encoder (DCE) and bidirectional long short-term memory network (BiLSTM). The procedure of the proposed method using a cascade structure is developed in three stages. In the first stage, each parallel branch of the multifeature learning combines the skip connection and the DCE, and uses different size kernels. The multifeature learning network can automatically extract and fuse global and local features from different network depths and time scales of the raw vibration signal. In the second stage, the BiLSTM as the feature protection network is designed to employ the internal calculating data of the forward propagation and backward propagation at the same network propagation node. The feature protection network is used for further mining sensitive and complementary features. In the third stage of bearing diagnosis, the classifier identifies the fault types. Consequently, the proposed network scheme can perform well in generalization capability. The performance of the proposed method is verified on the two kinds of bearing datasets. The diagnostic results demonstrate that the proposed method can diagnose multiple fault types more accurately. Also, the method performs better in load and speed adaptation compared with other intelligent fault classification methods.

Model based control of the inlet pressure of a sliding vane rotary expander operating in an ORC-based power unit

• Experimental analysis on Sliding Vane Rotary Expander in ORC plant. • Development of a new approach for volumetric losses calculation. • Inlet pressure control strategy based on expander speed variation. • Effectiveness assessment of the proposed control strategy. • Performance optimization through the proposed regulation strategy. Sliding vane rotary expanders (SVREs) are widely used in organic Rankine cycle (ORC)-based power units for low-grade heat recovery because of their capability to deal with severe off-design working conditions. In particular, the speed of SVREs is a very effective operating parameter, together with the speed of the pump, to regulate the recovery unit and to lead the involved components in an acceptable operating behaviour when they are far from the design conditions. In this study, a control strategy based on the variation in revolution speed of a SVRE was developed, where the inlet pressure of the expander is the main controlled property, which must be verified when the flow rate of the working fluid is changed to match the thermal power recovery at the hot source. In fact, pressure level control is a key point of the recovery unit for thermodynamic reasons and for the safety and reliability of the expander and, more generally, of the whole recovery unit. The proposed control strategy is based on an original theoretical procedure that relates the expander speed, inlet pressure, volumetric efficiency, and working fluid mass flow rate in an analytical form. This analytical formulation is widely nonlinear and is simplified for use as a tool for the model-based control of the inlet expander pressure. An experimental activity performed on a SVRE operating in an ORC-based power unit, fed by the exhaust gases of a supercharged diesel engine, was the base of the analytical formulation. This provided the possibility of deriving a simplified model-based control of the expander inlet pressure and assessing its effectiveness and limits during off-design conditions. Higher expander global efficiencies were obtained (up to 45%), allowing a greater mechanical energy recovery (up to 2 kW).

Structure, mechanical properties, thermal stability and oxidation resistance of arc evaporated CrAlBN coatings

CrAlN coatings are recognized as outstanding protective layers for machining tools owing to their high hardness and exceptional oxidation resistance. However, the thermal stability of CrAlN coatings is relatively inferior. For tailoring the thermal properties of CrAlN coatings and enabling them to serve for severe working conditions, the element of B was introduced into CrAlN coatings in this study. Cr0.42Al0.58N, Cr0.39Al0.58B0.03N and Cr0.35Al0.59B0.06N coatings were deposited by cathodic arc evaporation, and the influence of B-incorporation on the structure, mechanical and thermal properties of CrAlN coatings were studied in detail.X-ray diffraction determinations manifest that the three coatings reveal a face-centered cubic structure. The hardness of Cr0.48Al0.52N is increased from 30.2 ± 0.7 GPa to 35.0 ± 1.3 GPa (Cr0.39Al0.58B0.03N) and 36.4 ± 1.2 GPa (Cr0.35Al0.59B0.06N), respectively, due to solid solution strengthening and grain refinement. Adding B into CrAlN coatings postpones the formation of wurtzite (w-) AlN and hexagonal (h-) Cr2N, thus improving their thermal stability. Additionally, the oxidation resistance of CrAlN coatings is further optimized by B-addition owing to the promoted formation of alpha (α-) Cr2O3. The isothermal oxidation experiment at 1100 °C for 15 h reveals that the oxide thicknesses of Cr0.39Al0.58B0.03N and Cr0.35Al0.59B0.06N coatings are decreased to ~1.6 and 1.8 μm, compared with the ~2.7 μm of Cr0.42Al0.58N coating.

Failure analysis of the blackwater regulating valve in the coal chemical industry

Valves’ failure is common in the coal industry due to their severe working conditions, like high-velocity erosion, cavitation erosion, and so on. The damage morphologies of the blackwater regulating valve (BRV) in coal chemical equipment were investigated by stereo microscopy, metallographic microscopy, and scanning electron microscopy (SEM). Failure causes of the valve were analyzed from the material structure, hole defects, composition, hardness of the material, corrosive media, and computational fluid dynamics (CFD). The results showed the damage on the valve spool and seat was attributed to the combined effect of slurry erosion, cavitation erosion, and corrosion. The soft cobalt phase was preferentially removed followed by the pulling-off of WC particles, which is the failure mechanism of BRV.

Design and Optimization of Lightweight and HSD Excavator Bucket with Uncertain Load

Excavators are used primarily to excavate below the natural surface of the ground on which the machine rests and load it into trucks or tractor. Due to severe working conditions, excavator parts are subjected to high loads. The excavator mechanism must work reliably under unpredictable working conditions. Thus, it is very much necessary for the designers to provide not only a equipment of maximum reliability but also of minimum weight and cost, keeping design. safe under all loading conditions weight and cost, keeping design safe under all loading conditions. It can be concluded that, force analysis and strength analysis is an important step in the design of excavator parts. This paper presents a methodology for lightweight and high-strength design of an excavator bucket under uncertain loading. Uncertain loads are obtained by using the Monte Carlo simulation based on the existing soil-bucket interaction model in which the soil parameters are variable. And the well-known 3-sigma methodology is used for the quantification of the uncertain loads. Excavator bucket modelling is finished by using ANSYS Parameter Design Language (APDL). A multi-objective optimization model aiming to decrease the maximum von Mises stress and to reduce the weight of the bucket is established on the foundations of the uncertain load and the parametric geometry model. The structural shape and topology of the bucket are then designed by using the mixed variable genetic algorithm to solve the established optimization problem. The results show that the presented method can be effectively and efficiently applied for the optimization design of the excavator bucket and that the optimized bucket signifies obvious decreases in the weight and the stress compared with the initial reference model. The proposed methodology for structure optimization design considering uncertain loads not only provides the technical means for the design and development of high-performance bucket but also lays a preliminary theoretical foundation for the optimization design integrated machine-environment interaction.

Investigation of the Tribological Properties of Different Textured Lead Bronze Coatings under Severe Load Conditions

The purpose of this paper is to investigate the variation in the coefficient of friction (CoF) and also the wear in a lead bronze coating under different texture conditions. The tribological tests were performed using a tribometer with pin on disk configuration. Several kinds of textures, realised by a surface laser texturing, were tested by varying the diameter, depth, and density of the dimples under severe working conditions. The innovative aspect concerns the behaviour of the textured lead bronze coating and the lubrication conditions when the sample is subjected to extreme load conditions. Confocal microscopies and SEM (Scanning Electron Microscopy)/EDS (Energy Dispersive X-Ray Spectroscopy) analyses were performed to evaluate the texture behaviour and also the surface deterioration of the coating. The results show that the application of texture processing leads to an improvement in the tribological properties of the coating. By analysing separately the variation of the different geometric parameters of the dimples, it has been shown that the best results are obtained with a diameter of 50 μm, a density of 5%, and a depth of 5 μm.

Analysis of the influence of small quantity secondary lubricant on water lubrication

The use of water-lubricated bearings has increased dramatically in recent years because of environmental concerns. However, due to its low viscosity water generates easily broken lubrication film, which may cause wear and noises when water-lubricated bearings are subjected to short-time sever working conditions, e.g., low speeds and impact loads. A block-on-ring test rig is used in our work to simulate working conditions of water-lubricated bearings under the mixed lubrication regime and the coefficient of friction (COF) is monitored during the experiment. After the COF is stable between the rubber block and the stainless-steel ring lubricated by water, a small quantity of secondary lubricating medium (#5 white oil) is injected into the contact region. The COF decreases accordingly and its stable value is smaller than that under water-lubricated condition. The flow of water and #5 white oil, as well as the interaction between these two phases, are examined by CFD simulation. The results show that #5 white oil can break though the reverse water flow at the inlet and enter into the contact region to participate into the lubrication, and small oil droplets are formed during the injection, which are distributed throughout the contact area and stuck on the block and the ring to improve the lubrication performance. Therefore, the injected #5 white oil could enhance the film-forming ability of water, reduce the COF and improve the tribological behaviors of the water-lubricated contacts. Our work thus provides a new lubrication method to alleviate the friction and wear of water-lubricated bearings when subjected to short-time severe working conditions.

Mn-based spinels evolved from layered manganese dioxides at mild temperature for the robust flexible quasi-solid-state zinc-air batteries

• Mn-based spinels evolved from the birnessite type MnO 2 at mild temperature is firstly proposed. • Mn-based spinel of CMN-231H exhibits superior bifunctional oxygen catalytic activity and stability. • Introduce cellulose into PAM-based gel electrolyte can greatly enhance its tensile property. • Assembled quasi-solid-state ZAB show high reversibility and outstanding robustness. Flexible quasi-solid-state zinc-air batteries (ZABs) with outstanding power density, charge–discharge efficiency and robustness are extremely desired for wearable electronic devices. Therefore, it is urgent to develop superior bifunctional oxygen catalysts and robust solid-state electrolytes. Mn-based spinels (MMn 2 O 4 ) attract great attention due to their high abundance, low toxicity and well-balanced activity-stability performances. However, the Mn-based spinels are generally synthesized at high temperature, which usually causes large particle size, low surface area and insufficient active sites, being detrimental to their oxygen catalytic activities. To solve this issue, we firstly develop a two-step mild-temperature method to synthesize the Mn-based spinels by the evolution from layered birnessite-type MnO 2 . The optimized Mn-based spinel with the nominal composition of CoMn 1.5 Ni 0.5 O 4 (CMN-231H) demonstrates the remarkable oxygen catalytic activities with the half-wave potential ( E 1/2 ) for oxygen reduction reaction (ORR) and potential at 10 mA cm −2 ( E j =10 ) for oxygen evolution reaction (OER) of 0.780 V and 1.643 V, respectively, and the bifunctional oxygen catalytic activity of CMN-231H surpasses most reported spinels. Meanwhile, cellulose is introduced into polyacrylamide gel electrolyte to obtain the novel solid-state electrolyte (PAMC), demonstrating high ionic conductivity, water retention capability and excellent tensile property. Combining the CMN-321H and PAMC, the fabricated flexible quasi-solid-state ZABs show high power density, charge–discharge reversibility, cycling stability as well as robustness under severe working conditions. Our work supplies an effective way to synthesize Mn-based spinels under mild conditions and provides a promising approach for the development of quasi-solid-state flexible ZABs with high power density and strong robustness.

Research on Wind Turbines Fault Diagnosis Technology Based on CMS Data Feature Extraction

As a rich clean and environmentally friendly renewable resources, wind energy has emerged as a strategic choice for countries around the world. Because the wind turbines often operate in severe working conditions such as variable load and large temperature difference they are prone to failures and possible shutdowns. The shutdowns however seriously affect the economic benefits of the wind turbines. Initiative maintenance has become a worldwide recognized scientific method for planning and determining preventive maintenance work, the implementation of this strategy relies on real-time condition monitoring and fault signal identification methods. The condition monitoring of wind turbine can help master the health state and power generation performance of wind turbine, so as to timely formulate maintenance strategies and adopt technical modification measures to improve power generation performance, reduce the down time of wind turbine, avoid the occurrence of major faults, save maintenance cost and improve power generation capacity. Therefore, a condition monitoring system is built on a wind turbine of Zhangjiakou, and a systematic signal analysis method is proposed, time-domain synchronous averaging technology, based on variable period, impulse signal feature extraction technology based on Teager and signal decomposition technology based on CEEMD. The proposed method realizes the signal analysis and feature extraction of non-stationary nonlinear, weak signal and frequency aliasing signals, and successfully diagnose the gearbox secondary meshing failure during the long-term monitoring. This confirms that the monitoring system methods and signal analysis technology proposed in this paper can effectively realize the condition monitoring and fault diagnosis of wind turbines.

Deep learning-based damage detection of mining conveyor belt

The mining conveyor belt is an important component of the coal mine belt conveyor, which plays the role of carrying materials and transmitting power. Aiming at the problem that mining conveyor belts are easily damaged under severe working conditions, based on the reclassification and definition of conveyor belt damage types, a special data set for conveyor belt damage was established and a new detection method that can simultaneously detect multiple faults based on improved Yolov3 algorithm was proposed. The EfficientNet was adopted as the backbone feature extraction network instead of Darknet53 in the improved algorithm, comprehensively considers the balance between network depth, width, and image resolution for network scaling to improve the accuracy of the algorithm in limited computing resources. Experiments have proved that the improved algorithm in this paper takes into account both detection speed and detection accuracy. The detection speed can reach 42 FPS, and the mean average precision can reach 97.26%. Compared with the original Yolov3 algorithm, the accuracy is increased by 10.4%, with the speed 45.9%, which provides new ideas and methods for ensuring the safe and stable work of conveyor belts.

A Novel Wear Detection Method Based on FPC: A Case Study on the Hoisting Cage Sliding Sleeve

Under severe working conditions in mines, common wear detection methods are difficult to apply to the hoisting cage sliding sleeves. This article proposes a wear detection method based on flexible printed circuit (FPC), which is conceived for the wear detection of sliding sleeves or the edge of complex curved surfaces under severe conditions. First, a new type of sensor based on FPC is proposed, and its detection principle is presented. Then, according to the wear mechanism of sliding sleeves, a wear detection system is designed and manufactured. In order to express wear value, a formula about the relationship between the number of broken wires and wear value is established. Finally, a series of verification experiments are carried out, and the obtained results confirm the feasibility of proposed approach and explain the causes of voltage fluctuations.

Multisensor Fusion Using Fuzzy Inference System for a Visual-IMU-Wheel Odometry

This article presents an adaptive fusion method for a multisensor odometry using a fuzzy inference system and applies it to a visual-inertial measurement unit (IMU)-wheel odometry. This article first presents the mechanism of the multisensor odometry with an error state Kalman filter and points out that the assumption of the constant covariance in the prediction and measurement models are invalid due to dynamical interior and exterior abnormities. The previous work of fuzzy logic Kalman filter can only deal with uncertain uncertainty in the measurement model and thus not suitable for this double stage variant uncertainty problem. This article provides a solution using adaptive network fuzzy inference systems. The abnormal working conditions of the two models can be detected online separately, and the fusion gains are tuned accordingly. The training method of the networks is also provided to further improve the fuzzy inference system when the ground truth of the trajectories is available. Experimental results show that the proposed adaptive fusion method can significantly improve the robustness and accuracy of the fused trajectory estimation, even under severe working conditions.

Literature review of the health of technical intern trainees in Japan: A study using the International Classification of Functioning, Disability, and Health

This study aimed to clarify the health of technical intern trainees and factors that affect their health and examine the necessary support. Articles related to the health of technical intern trainees, published between January 2010 and September 2019, were extracted from databases (Ichushi-Web, CiNii Articles, and news archives) using the keywords "technical intern training" and "health." Additional articles were extracted by hand-searching related reports and magazines. The articles were sorted using the partial Bernard Berelson method of content analysis and classified into the components of the International Classification of Functioning, Disability, and Health (ICF). There were 24 categories, 71 subcategories, and 422 recording units extracted from 127 documents. Using the ICF classification, the 【Environmental factors】 component had the largest number of recording units, with the "support from supervising and implementing organizations" (n = 108) and "inadequate enforcement of labor-related laws" (n = 48) categories. This was followed by the 【Health condition】 component, with the "death of technical intern trainees" (n = 27) and "increase of tuberculosis cases" (n = 24) categories. It is assumed that while some technical intern trainees are in a high-risk situation because of the difficulty of receiving medical care and severe working conditions that may worsen their health, including the development of brain and heart disease and tuberculosis, some receive positive effects such as early detection and treatment of diseases through health checks. In addition to improving the working environment, it is important to translate the results of health checks into trainees' understandable languages, provide supplementary explanations of the results, and support the promotion of multicultural coexistence.

Research on Coordinated Control Strategy of ITER Power Supplies and Reactive Power Compensation System

The stable operation of the ITER power supply is inseparable from the optimal control of the reactive power compensation (RPC, consisting of thyristor controlled reactor and turned filters) system. However, the traditional RPC control scheme with high latency can no longer meet the actual reactive power demand from the load side under the severe working conditions of the plasma control system. A new control law has been proposed and analyzed for RPC system based on the Lyapunov’s theory by designing a Lyapunov function, which derivative is always negatively definite globally. With the help of the Lyapunov’s asymptotic stability analysis, a new controller, which called Q Lyapunov-function-based control (QLC), has been designed to realize good dynamic performance comparing to the existed solutions. The simulation results show that the reactive power consumption of the grid side controlled by QLC has been greatly reduced during the transient change of the reactive power generated by the loads. Hence, the AC busbar voltage will have strong robustness. Moreover, it is also shown that the RPC system can be stabilized globally for handling severe signal disturbances.

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder.

Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

Reliability modeling for competing failure processes with shifting failure thresholds under severe product working conditions

Considering the complex product working circumstances under severe conditions, this paper first specifies the shock processes that a product experiences. The multi-stage description of competing failure processes is accomplished by considering various factors affecting the performance degradation process as well as by introducing the soft failure threshold in order to effectively illustrate the overall failure process of the product. The concepts of shock damage and intensive shocks are introduced to modify the traditional shock models. The difficult problem of adding the arrival time of shocks to the reliability model is solved by using the multivariable integrals. The competing failure model based on the extreme shock damage and the delta-shock competing failure model considering the intensive shocks are presented. Finally, the proposed models are applied to evaluate the reliability of a certain micro engine. The influence of the parameters on the model reliability is analyzed, and the proposed models are compared with an existing reliability model. The results demonstrate that the proposed models can effectively describe the competing failure process and show certain feasibility and theoretical reference values.

Smart crawfish: A concept of underwater multi-bolt looseness identification using entropy-enhanced active sensing and ensemble learning

With the rapid development of the oil industry, more and more subsea pipelines enter into service, and thus we are facing the challenge of safe operation and maintenance of these underwater infrastructures. For instance, as a common component to connect subsea pipelines, the flange is prone to losing sufficient integrity due to severe working conditions and harsh environments under the sea. However, most of the current structural health monitoring (SHM) and nondestructive testing (NDT) techniques are targeted at onshore structures, and we require a more practical method for multi-bolt looseness identification of subsea flanges. In this paper, attempting to solve the above difficulty, we propose the “Smart Crawfish”, which is a concept that employs a subsea robot equipped with a gripper and a pair of Lead Zirconate Titanate (PZT) transducers to implement the active sensing method. Particularly, the main computational contribution of this paper is that we develop two new entropy indexes, i.e., multiscale range entropy (MRangeEn) and multiscale bubble entropy (MbEn) to enhance the current active sensing method (i.e., the entropy-enhanced active sensing method). To detect underwater multi-bolt looseness, we first obtain stress wave signals under different categories (i.e., different cases of multi-bolt looseness) by the active sensing and then compute their MRangeEn and MbEn to construct the feature dataset. Subsequently, via the dataset, a stacking-based ensemble learning classifier is trained to classify different categories. Finally, we conduct a lab-level experiment to demonstrate the effectiveness of the proposed method, which outperforms the current entropy-enhanced active sensing. To the best of our knowledge, this paper is the first attempt to address the detection of underwater multi-bolt looseness, which has great potential for future industrial applications.

Thermal-Hydraulic Analysis on Quench Behavior of Indium-Tin Soldered REBCO Composite Conductor

The second-generation high-temperature superconductor REBCO is one of the most valuable candidates that meet the requirements of future magnetic confinement fusion device. Quench behavior of an improved high-current REBCO composite conductor is simulated by a 1-D finite element method. Indium-tin alloy foil placed in the gap between stabilizer, jacket, and superconducting tape stacks can significantly reduce thermal contact resistance, which leads to an increase on the minimum quench energy of composite conductor. Short perturbations have a better response to this improvement than long perturbations, due to the low normal zone propagation velocity (NZPV) of REBCO conductor. For short perturbation (perturbation length (l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> = 0.1 m), the peak temperature in cable and helium drops 12.76 and 17.99%, respectively, after soldered. Due to the change of heat transfer in a composite conductor caused by helium wave propagation, the development and maximum length of normal zone significantly decrease from 13.06 to 4.78 m under long perturbation (l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> = 10 m) circumstance. High current REBCO composite conductor with proper indium-tin alloy soldered can enhance the stability and safety of composite conductors under severe working conditions, but it also slows down the NZPV and hinders the quench detection of high-temperature superconducting magnet.

Experimental Research and Numerical Simulation of Weld Repair with High Energy Spark Deposition Method

For long-serviced pressure equipment that is under severe working conditions such as a high temperature, high pressure, and corrosion, the material properties and structure will be unavoidably damaged or degraded, especially cracks and other damages at key positions such as welded joints, which seriously threaten the safe operation of the equipment. In order to promote the sustainable development of industries such as the chemical and petrochemical industries, remanufacturing technology has emerged worldwide, and various surface repair processes have also rapidly developed. As an important branch of surface repair technology, the high energy spark deposition (HESD) process is a new pulse cold welding repair technology developed from electro-spark deposition, which combines the advantages of multiple surface repair processes. The HESD process has the characteristics of a smaller heat affected zone and lower welding residual stress. It is a new type of repair method that is worthy of popularization and application. The process has been initially applied in the fields of surface modification and die steel repair. In this paper, the application of the HESD process to the repair of welded joints was introduced, the mechanical properties of the joints and the residual stress distribution after welding were analyzed, and the feasibility of HESD as a repair welding method for pressure structures was discussed. First, a numerical simulation of the temperature and stress field of HESD was proposed by using ABAQUS and the related subprograms, and the validity of the simulation results was verified by the residual stress test with the indentation strain method. Due to the precise control of the heat and pulse discharge working mode, the heat-affected zone and deformation caused by the HESD were extremely small, and the residual stress that was generated was low and only concentrated on the repair welding seam. Second, according to the numerical simulation and the test results of the mechanical properties of the welded joint, the optimal repair welding process parameters were obtained through the orthogonal experiment: peak current 45 A, pulse width 90 ms, and output voltage 10 V.