Shutdown Of Equipment Research Articles

Fault Feature Extraction of Rolling Bearing Based on Maximum Second-Order Cyclostationarity Blind Deconvolution and CEEMD-Teager

The operation of rolling bearings is directly related to the reliability of the whole rotating machinery. If the rolling bearing failure cannot be diagnosed and repaired in time, it will probably lead to equipment shutdown. The feature extraction process is an important part of bearing fault diagnosis. Some existing feature extraction methods are sensitive to noise and interference, and cannot fully explore and characterize useful information in nonlinear and non-stationary signals in complex scenes, and sometimes even lose important information contained in the data, resulting in low diagnostic accuracy. Therefore, a new method combining Maximum Second-order Cyclostationarity Blind Deconvolution (CYCBD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) was proposed for feature extraction of rolling bearing faults. Firstly, the cyclic frequency is set according to the failure frequency, and the original signal is filtered by CYCBD. Then, the filtered signal is decomposed into multiple Intrinsic Mode Function (IMF) by CEEMD, and the effective IMF components are selected by kurtosis criterion for reconstruction. Finally, the Teager energy operator is used to enhance the transient impact of the reconstructed signals. The results of simulation and comparison experiments show that the proposed method can extract bearing characteristics in different fault states more effectively, and the accuracy of network model diagnosis is improved compared with the traditional methods.

Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network

In today’s interconnected industrial landscape, the ability to predict and monitor the operational status of equipment is crucial for maintaining efficiency and safety. Diesel engines, which are integral to numerous industrial applications, require reliable fault detection mechanisms to reduce operational costs, prevent unplanned downtime, and extend equipment lifespan. Traditional anomaly detection methods, such as thermometry, wear indicators, and radiography, often necessitate significant expertise, involve costly equipment shutdowns, and are limited by high usage costs and accessibility. Addressing these challenges, this study introduces a novel approach for fault detection in diesel engines by analyzing torsional vibration data in the time domain. The proposed method leverages short-term Fourier transform (STFT) and continuous wavelet transform (CWT) techniques, integrated with a convolutional neural network (CNN) to identify hidden patterns and diagnose engine conditions accurately. The method achieved a detection accuracy of 96.5% with STFT and 92.2% with CWT. To ensure robustness, the model was tested under various noise conditions, maintaining accuracies above 70% for noise levels up to 40%. This research provides a practical and efficient solution for real-time fault detection in diesel engines, offering a significant improvement over traditional methods in terms of cost, accessibility, and ease of implementation.

Experimental analysis of a pilot plant in Organic Rankine Cycle configuration with regenerator and thermal energy storage (TES-RORC)

Applying thermal energy storage in a Regenerative Organic Rankine Cycle system is a possible combination of energy storage for renewable and waste energy sources. This research presents an experimental analysis of a regenerative organic Rankine cycle pilot plant, incorporating a thermal energy storage chamber based on a phase change materials to obtain thermal inertia in the system. The primary heat source is the residual heat of combustion gases at a temperature of approximately 320 °C coming from a generating set that operates within a thermal power generation plant. The prototype uses a twin-screw expander modified to work as a turbine. Novec 649 was used as the working fluid which is an organic compound. The experimental evaluation of the regenerative organic Rankine cycle with thermal energy storage has been carried out through energy and exergetic efficiency analysis to verify the operation of the pilot plant. Values of 13.2 % were obtained for the exergetic efficiency and 2.67 % for the global efficiency of the cycle, with a maximum expander work of 11.8 kW. It was demonstrated that the thermal energy storage system provides energy in response to the plant's operating inertia for 17.5 min, sufficient time for the gradual shutdown of the plant equipment, maintaining the working power of the regenerative organic Rankine cycle.

Erosion and intergranular corrosion induced failure of S31254 stainless steel – Case study on Venturi scrubber in a natural gas purification plant

The flue gases such as SO2, SO3, and CO2 generated during the purification process of sulfur-containing natural gas need to be treated to a certain concentration before they can be discharged into the atmosphere. The flue gas unit of a purification plant in Sichuan adopts the new Cansolv flue gas treatment process. After running for a period of time, the Venturi scrubber experienced severe corrosion failure, resulting in equipment shutdown and significant economic losses. This paper utilizes an actual failed device as a case study to analyze the corrosion environment of the Venturi scrubber. It conducts a comprehensive failure analysis, encompassing material mechanical properties, metallographic analysis, and failure microstructure analysis, aiming to elucidate the reasons behind the Venturi scrubber failure. Systematic analysis shows that the manufacturing materials used in this scrubber meet all design requirements. However, flue gas at temperatures reaching 280 °C enters the scrubber tower at a flow rate exceeding 10 m/s. The dissolution of acidic gases leads to a significant drop in the pH of the cooling water to 1, manifesting evident signs of erosion corrosion and intergranular corrosion on the material surface. These observations suggest that erosion and intergranular corrosion in high-temperature acidic environments are the predominant factors contributing to the corrosion failure of the Venturi scrubber.

Application of bandage repair systems in the power sector to restore pipeline integrity

This work explores the possibility of repairing damaged pressure piping systems using composite repair bandaging technology. Loctite technology from Henkel was selected from several existing methods and applied at the weld joint. To assess the performance of the bandage, a bore was made at the weld joint to simulate a replacement defect of the composite bandage. The bandage, along with the welded joints, was subjected to microstructural and macrostructural analysis, and mechanical and fire resistance tests were also performed. Based on the obtained results, it is concluded that the properties of the Loctite composite system are suitable for repairing pressure piping in the power industry. However, it is not always possible to use this repair method due to certain boundary conditions for its application, such as maximum pipe overpressure, the temperature of the internal medium and the environment, degradation of the repair system due to the medium being transferred and the environment, reduction of the pipe wall at the repair site, possible surface treatment before application of the system, etc. For these reasons, the repair system may be used for a temporary period (approved by the power plant operator), and the spot will then be repaired in the standard way by replacing parts of the pipeline during a planned equipment shutdown.

Failure Analysis of Header Flange Cracking in Propane Dehydrogenation Unit

The boiler fault of propane dehydrogenation unit is investigated by macroscopic inspections, mechanical properties testing, chemical composition analysis, metallographic microstructure, X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS). The experiments confirm the reason for cracks occurrence of SA182-F347H boiler that the stress corrosion cracking occurs. The sulfur injected into the device reacts with the flange metal to produce sulfide corrosion products. The improper control of flange manufacturing process and the operating temperature in the sensitized range lead to the sensitization of flange materials and reduce the corrosion resistance. During the equipment shutdown, the sulfide corrosion products on the surface of the flange reacted with air and water to form polysulfuric acid, and causing stress corrosion cracking and leakage of the sensitized F347H stainless steel flange.

Double-ring high-frequency common-mode switching oscillation current sensor for inverter-fed machine winding insulation monitoring

Double-ring high-frequency common-mode switching oscillation current sensor for inverter-fed machine winding insulation monitoring

Principle of predicting the reduction of insulation properties of electric motor windings

The simplicity of the design of the asynchronous electric motors (AM) allows them to be widely used in industry. The AM fleet sometimes reaches 80% of the total load of the enterprise. According to statistics, more than 60% of AM failures are observed due to damage to the insulation of the AM stator winding. The reason is premature dynamic and thermal aging of the insulation, which leads to the failure of the conductive and grooved parts of the AM winding. As a result of a decrease in the dielectric properties of the insulation, damage occurs in the stator windings of AM, such as turn, phase-to-phase short circuits, which tend to develop into a short circuit. Short circuits in the stator windings of the AM are emergency and lead to a sudden disconnection of the AM from the power supply network by existing relay protection means, for example, current cut-off or thermal protection. Sudden, non-sanctioned disconnection of the electric motor from the power supply leads to the shutdown of technological equipment, damage to equipment, raw materials and economic losses. The aim of the work is to develop the principles of timely monitoring and prediction of the state of housing and interphrase insulation of stator windings of AM using the energy of capacitors with local compensation of reactive power. To achieve this goal, research was carried out at a laboratory institution with AM, in which branches of turns were made. The transient processes of capacitor discharge are considered and new methods for monitoring and predicting the residual insulation life are proposed. The new method is based on monitoring the condition of the insulation after each disconnection of the AM from the power supply, while using the energy that remains in the capacitors. The use of a change in the velocity characteristic of the constant discharge time of the capacitor made it possible to predict the residual service life of the AM. The personality of the study is that under the conditions of local compensation of reactive power, it is possible to use the energy remaining in the capacitors to diagnose and predict the state of insulation, as well as the change in the velocity characteristic of the capacitor discharge time constant, which is a criterion parameter for predicting the residual service life of the AM. The carried out studies confirmed the development of test diagnostics in conditions of reactive voltage compensation, which is a new direction in systems non-poisonous diagnostics, which requires continued research and applications in industry.

Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism.

Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses.

Dynamic Inventory Management Model for Equipment Replacement with Probabilistic Distribution of Failures

The article deals with the issues of inventory management modeling of components to ensure the prompt replacement of deep-water pump installations for the oilfield industry in case of equipment failure. The review of literary sources on the subject of the study and the problem statement is given. The rationale for choosing a mathematical model for problem solution is given. The task of dynamic inventory management is to provide the necessary stock with minimal temporary losses from forced downtime at minimal component storage and supply cost for repair or replacement. The planning horizon is divided into several time periods where optimal amount of inventory is to be ensured. It is indicated that when planning stocks in the case of a large number of pieces of equipment operating under different conditions, it is advisable to use a probabilistic approach. An event distribution function is set for probabilistic flow of equipment shutdown. The problem is solved for each segment defining the margin value by means of the Wilson model for deterministic flow of events. The results of simulation modeling on the shutdowns of electric drive centrifugal pump installations at the wells of the Vyngapurovskoye field are presented. A histogram of equipment shutdown frequencies for various time intervals is given. An exponential best approximation function is constructed to approximate the number of stops from installation time to equipment stop, the function coefficients and the standard approximation error are calculated. The characteristic curve of the relative proportion of the operating equipment requiring replacement, as well as the planned number of installations supplied for equipment replacement, with time is given. The dynamics of safety margin changes, which ensures the guaranteed replacement of failed equipment on time with respect to time, is presented.

An Advanced Early-Stage Production Forecasting Model for Middle-High Rank Coal Development

Reasonable production prediction of coalbed methane (CBM) is of great significance for improving the economic benefit of CBM reservoirs. Current prediction methods for CBM production focus on the later stages of development, with few studies on early production forecasting. The objective of this work is to provide a reliable new idea for the early production prediction of CBM through various analyses and demonstrations. First, the CBM development modes are classified according to the production characteristics of the Panhe demonstration block of Shaanxi Province, China. Second, an efficient and feasible early production prediction model is established based on the geological potential and development potential. Finally, using the proposed model, different modes’ production characteristics and optimization strategies are analyzed. The research shows that the gas production profiles can be divided into two modes: single-peak mode (SPM) and multipeak mode (MPM). The peak production and average EUR of the SPM are 49.6% and 32.4% higher than those of the MPM, but the stable production period is only 0.2~1 year. In terms of the geological potential of CBM wells, the gas content, critical desorption pressure, and formation coefficient of the SPM are 6.7%, 13.3%, and 37.8% higher than those of the MPM, and the gas wells are mainly located in the high part of the coal seam (the average height difference is about 20 m). Besides, the concept of quasidesorption degree Pdq is innovatively introduced to characterize the development potential of gas well. The Pdq has an exponential relationship with CBM production, and the coefficient of the exponential term in SPM is approximately 22% larger than that in MPM. Moreover, the production of gas wells is greatly affected by the continuity of production. In the process of gas production, the influence of factors such as equipment shutdown should be minimized. To examine the applicability of the proposed method, the model is applied to an actual CBM well in Panhe, and the prediction accuracy is higher than 85%.

A Bearing Fault Diagnosis Method Based on Dilated Convolution and Multi-Head Self-Attention Mechanism

Rolling bearings serve as the fundamental components of rotating machinery. Failure to detect damage early in these components can result in equipment shutdown, leading not only to economic losses but also to a threat to worker safety. Given the diverse range of rotating parts, it is crucial to promptly identify and accurately diagnose early bearing failures during the maintenance of large-scale machinery. To achieve quick and precise fault diagnosis, this study proposes a method based on dilated convolution, a Bidirectional Gated Recurrent Unit (BiGRU), and a multi-head self-attention mechanism. The key advantage lies in its ability to directly process raw 1D sampled data without requiring complex time–frequency domain conversion. To validate the model’s accuracy and stability, we conducted empirical studies using both the HUST bearing dataset proposed by Thuan, Nguyen et al. and the CWRU bearing dataset from Western Reserve University. The results demonstrate that our model achieves an impressive accuracy rate of 99.94%, along with an f1 value for the test set when dealing with multiple operating conditions for all five types of bearings in the HUST dataset. Moreover, when applied to the CWRU dataset, these two metrics even reached 99.95%. Furthermore, the proposed model achieves a significant prediction accuracy of more than 98.5% on two datasets containing different types of noise and different levels of white Gaussian noise, highlighting its great potential in practical applications of early bearing fault diagnosis.

Intermittent fault diagnosis of analog circuit based on enhanced one-dimensional vision transformer and transfer learning strategy

As the major cause of false alarms in built-in test (BIT) system, intermittent faults of analog circuits may trigger abnormal equipment shutdown and lead to catastrophic accidents. With complete randomness and great non-repeatability, intermittent faults are arduous to be detected. To enhance the reliability and safety of electronic systems, an end-to-end approach based on enhanced one-dimensional Vision Transformer (1DViT) is proposed to realize intelligent diagnosis for intermittent faults of analog circuits. The signal anomaly caused by intermittent faults can be regarded as a kind of random anomaly from global perspective, and there are also rich local feature information in the fault interval. Completely composed of self-attention mechanism, Vision Transformer possesses prominent performance on extracting global features and modelling global representations, thus can be applied to identify intermittent faults. Meanwhile, to further enrich the feature representation, one multi-scale convolution fusion module (MSC) incorporating a series of convolution operations is designed and combined with 1DViT to extract and fuse the valuable local information. However, in practical test, due to the complex operation process, it is cumbersome to collect sufficient fault data to guarantee the effective training of the proposed model. To cope with this problem, transfer learning strategy is introduced. The model will be first pre-trained with adequate simulation data which is easily accessible, and then fine-tuned with a relatively small amount of actual fault data to help match the practical feature distribution. Experiments on two typical circuits demonstrate that the proposed method could achieve excellent diagnostic result in practical test.

Assessing the fatigue wear effect on traction coefficient and dynamic performance of roller bearing

Rolling element bearings are commonly employed in rotating machinery to support rotating shafts. These machinery elements are vulnerable to failure due to increased friction and heat, which cause progressive wear on the rolling interaction surfaces of the bearing. The failure of bearings causes an unpredicted shutdown of equipment, leading to an increase in downtime and financial losses. Various condition monitoring techniques have been developed for periodic assessment of failure in roller-bearing systems. The focus of this work is to establish a relationship between surface fatigue wear, film thickness [Formula: see text], asperity load ratio [Formula: see text], lubricant film stiffness [Formula: see text] and traction coefficient [Formula: see text] parameters. A periodic assessment of the above-mentioned parameters was made on the test bearing, which was subjected to a fatigue test for 2000 h. The results acquired from the experimental study highlighted that, as surface fatigue wear on the bearing interaction surfaces and lubricant degradation occurred with an increase in fatigue load cycles, the asperity load ratio [Formula: see text] and traction coefficient [Formula: see text] parameters showed a gradual rise in trend which further resulted in a difference in the dynamic behaviour of the bearing system.

Сравнение методик расчета инспекционного интервала при реализации риск- ориентированного подхода к инспекции технологического оборудования

The article deals with the problems of conducting periodic inspections of equipment. This is due to the high frequency of equipment shutdown for its technical diagnostics and examination in accordance with the requirements of regulatory and technical documentation, in particular the Federal norms and rules «Rules for the safe operation of process pipelines». It is noted that the inspection is an important tool for ensuring the integrity of the equipment and, as a result, the industrial safety of the facility. At the same time, it is indicated that the inspection can be implemented on the basis of one of two approaches: regulated and risk-oriented. A general description of the inspection methodology, taking into account risk factors, is presented. It is pointed out that a risk-based approach to inspections is widespread in a number of countries. Various methods of implementing this approach are given, taking into account a large number of standards. The basics of API 581 3rd Edition and S-RBI techniques used for inspections are described. The technological process of the gas condensate stabilization system of a plant for the production of liquefied natural gas is described. A process pipeline is considered as an object of the study, for which an inspection interval is determined using the API 581 3rd Edition methodology. At the first stage of determining the inspection interval according to the S-RBI methodology, the process of assessing the criticality of the failure is described. The second step describes the process of determining the confidence factor, where the focus is on the estimation of the uncertainty in predicting the corrosion rate. At the third stage, the coefficient of the inspection interval is determined based on the data on the criticality of the failure and the reliability factor. Then an example of calculating the inspection interval for the object under study is given. Based on the results obtained, a conclusion was made about the comparability of API 581 3rd Edition and S-RBI methods. Both methods have their advantages and disadvantages, but at the same time they provide optimization of the scope of inspection work while maintaining a high level of industrial safety of the facility. In order to improve production efficiency, reliability and safety of pressure equipment, and optimize inspection intervals (which will allow to reduce the financial costs of enterprises), it is advisable to apply the above practices in the Russian Federation, providing the required legal and regulatory framework.

Sensor Data Recovery of Faulty Rocket Engine Based on Multistage Graph Convolutional Network

Engine, the indispensable core of a rocket, has a significant impact on space exploration, especially the high-thrust liquid-propellant rocket engine. Most new-generation manned rockets for space stations or lunar exploration prefer the engine for its high performance and environmental friendliness. However, the engine is susceptible to failure under extreme conditions, which could cause catastrophic consequences without timely warning. Real-time state detection and fault location can prevent some catastrophic outcomes, but they require reliable sensor data. Nevertheless, some sensor data could be lost due to signal interruptions or equipment shutdown caused by system faults. Therefore, recovering the lost data based on the remaining measurements is a critical challenge that involves dealing with the distribution gap between normal and faulty data. To tackle the data drift and achieve real-time and high-precision sensor data recovery of the faulty engine, a multistage model based on graph convolutional networks is proposed in this paper. Trained by a multiloss function, the model primarily recognizes the status of the engine and passes the status to the next stage. Then the second stage recovers the lost data by two graph convolutional networks specific to the normal or faulty state. Evaluated on the practical experimental data from Xi’an Aerospace Propulsion Institute, our method successfully identifies the state of the system with accuracy above 99.99% and recovers the incomplete sensor data with a mean absolute error under 0.0065. Moreover, some ablation studies demonstrate that the blocks of two-stage and graph convolution could achieve a 26% improvement over the vanilla neural network.

Effects of air blower and pump failures on the performance of A2O processes for wastewater treatment

The unexpected failure of equipment such as pumps and fans in wastewater treatment plants can reduce wastewater treatment efficiency, leading to direct leakage of untreated wastewater into the environment. It is hence important to predict the possible consequences of equipment failure to minimize the leakage of harmful substances. This study examines the impacts of equipment shutdown on the performance and recovery time of a laboratory-scale anaerobic/anoxic/aerobic system with regard to reactor conditions and water quality. Two days after the air blowers are stopped, the concentrations of the soluble chemical oxygen demand, NH4–N, and PO4–P in the effluent of the settling tank increase to 122 mg/L, 23.8 mg/L, and 46.6 mg/L, respectively. These concentrations return to their initial values after 12, 24, and 48 h of restarting the air blowers. The concentrations of PO4–P and NO3–N in the effluent increase to 58 mg/L and 20 mg/L, respectively, about 24 h after stopping the return activated sludge and mixed liquor recirculation pumps, owing to the release of phosphates in the settling tank and inhibition of denitrification.

Study on Wear Properties of the Graphite-Sealing Surfaces in a Triple Eccentric Butterfly Valve Based on EDEM-Fluent Coupling

When using valves and pipes, erosion wear is a major issue. Erosion wear can result in equipment shutdown, material replacement, and other issues, as well as the failure of sealing surfaces. The depth of erosion wear is primarily determined by particle velocity, particle size, target material, and use conditions. A combination of the discrete element method (DEM) and computational fluid dynamics (CFD) was used in this study. The dynamic process of particle collision with the sealing surface is also considered. The wear depth was then calculated using Archard’s abrasive wear theory. The erosion wear process of the graphite-sealing surface by gas-solid two-phase flow medium is numerically simulated in a high-temperature triple eccentric butterfly valve using the above theory and method. The erosion wear patterns of graphite-sealing surfaces were investigated under various particle velocities, particle sizes, target materials, and service conditions. The findings indicate that particle velocity and particle size are positively related to wear rate. Soft target wear depth is greater than hard target wear depth. The wear depth decreases as the ambient temperature rises. As a result, graphite has excellent resistance to erosion and wear at high temperatures. When feeding, however, particle velocity and particle size must be considered. The erosion wears characteristics of a high temperature three eccentric butterfly valve investigated in this paper can be used to optimize erosion wear prevention.

Nanoparticle lubricant additives applied in a CNC lathe ball screw component for improving the quality of machined workpieces: A case study

A lathe ball screw is a mechanical actuator that translates rotational motion to linear motion. These machine elements are lubricated to reduce friction and wear since they can affect dimensions of the machined workpiece, measured by Cp and Cpk process capability indicators, and the number of equipment shutdowns. In this work, different nanoparticles (NPs) were added to a commercial lubricant for machine tool slideways with the purpose of reducing coefficient of friction (COF) and wear. NPs of SiO2, CaCO3 and montmorillonite clay (MMT) were added to the lubricant in concentrations on 0.01 and 0.05 wt%. Four ball tests performed under scuffing conditions showed that the best concentration was 0.01 wt% for all NPs, with improvements in load-carrying capacity of 13–15%. Higher concentrations resulted in agglomeration and these large NP clusters were not able to infiltrate the contact area to reduce friction and wear. Block-on-ring tests showed that 0.01 wt% MMT reduced wear and COF by 83% and 81%, respectively. Finally, machining tests were performed in a CNC lathe with the 0.01 wt% MMT nanolubricant for the ball screw component. Improvement was measured by keeping track of the dispersion of the workpiece dimensions, the process capability indicators Cp and Cpk and the number of equipment shutdowns for adjustments. The addition of MMT NPs to the lubricant improved Cp and Cpk by 34% and 52%, indicating a lower variation on the work piece measurements. Furthermore, equipment shutdowns were reduced by up to 50%, improving the process efficiency and lowering manufacturing costs.

Methods of mining machine components reconditioning and hardening by means of concentrated energy fluxes

Introduction. The value of resources is becoming increasingly important to mining proprietors, since the maintenance of mining equipment is quite expensive. Increased scope and cost of repairs are largely determined by the quality of mining machines and equipment operation in production environment, as well as by their reliability and maintainability. The costs of preventive maintenance are over the limit by 40–50%, in most instances, due to equipment shutdowns caused by subsystem failures. Components with hidden defects as well as the methods of components reconditioning that do not ensure long-term operation cause a significant overrun of costs for breakdown maintenance. The quality of mining equipment maintenance is in direct relationship with the effective methods of components reconditioning, including the methods of components hardening both during their manufacture at machinebuilding plants and during their repairs at repair works that carry out complex, mainly major repairs of mining equipment. Adherence to the flow process of reconditioning/hardening of the component surface in contact with another component is of great importance. Therefore, to preserve fixed assets, in particular, mining and transport equipment, it is essential to find that reconditioning methods which allow to preserve consumer properties and operability of equipment for a sufficiently long time. Subject matter. The article provides an overview of methods of mining machine components reconditioning/hardening involving concentrated energy fluxes. In the authors’ opinion, these are the most effective methods to increase the resource and reliability of mining machine components by 2–3 times. The choice of components reconditioning/hardening method depends entirely on the service centre capacities, necessary technological equipment availability, personnel qualifications and, most importantly, economic feasibility of using a particular method of reconditioning/hardening. Research objective is to consider the most effective methods of mining machine components reconditioning/hardening. Methods of research include the interpretation of a number of experiments conducted at mining enterprises to obtain comparative characteristics of the most effective methods of mining machine components reconditioning/hardening. Research results. The most effective methods of components reconditioning/hardening are identified and recommended practices are provided. Conclusions. It has been established that the application of a particular components reconditioning/hardening method should be based on the data from tribological maps, as well as necessary equipment availability and economic feasibility.