Machine Reliability Research Articles

Effectiveness analysis of a novel rectangular tunnel boring machine with planetary transmission for box jacking

Traditional rectangular tunnel boring machines have low tunneling efficiency, poor soil mixing effects, which has hindered the construction of long-distance and large-section box jacking projects. To overcome these limitations, a new type of full-face excavation boring machine was developed based on a planetary transmission mechanism. This machine achieves full-face excavation by utilizing three eccentric cutter heads that revolve around both the central axis of the cutter plate and their individual shafts. The design methodology, feasibility, and principles of this new mechanism were introduced. Furthermore, a successful construction project of an underground highway passage in Japan was presented as a case study to demonstrate the design and application of this tunnel boring machine. The case details include the excavation face support mechanism, optimal cutter-head layout, obstacle removal strategies, and methods for reducing jacking resistance. Monitoring data from the project verified the applicability, reliability, and overall engineering performance of the rectangular shield machine developed using the planetary mechanism. This research demonstrates that the planetary transmission mechanism-based boring machine offers superior performance in terms of ground settlement and tunneling speed, potentially providing a comprehensive solution to the challenges in the development of rectangular shield machines for box jacking projects.

Transient Analysis of Flow Unsteadiness And Machine Instabilities In a Centrifugal Compressor at Surge and Second Quadrant Operation

Abstract Instabilities in turbomachinery operation originate from intrinsic flow unsteadiness, sudden variations of operating conditions, and interactions with the surrounding system, affecting both system stability and machine reliability. The flow unsteadiness, presenting a characteristic behaviour, requires a deep understanding of the underlying flow fundamentals, a careful analysis using advanced experimental methods and a clear distinction on how the system might lead to instabilities. The identified approach addresses deep surge and second quadrant characterization. The analysis presented here benefits from the responsiveness and flexibility of the experimental test rig, fine tuning of the system layout, the instrumentation installation, and the transient tests procedure. The main focus area is the time-frequency analysis of flow and pressure signals at transients, thus providing a proper reconstruction of flow mechanisms and instability evolution. Valuable techniques include: short-time fast Fourier transform, wavelet analysis, and Wigner-Ville distribution; coherence between flow and pressure, to trace waves propagation, is considered too. These, presented outlining their strengths and weaknesses, are evaluated considering real time field applicability with regards to computational resources too. The experimental campaigns and data processing are presented, with a test matrix covering a wide range of parameters, including system layout and surge volume size, rotational speeds, flow rates ranging from nominal point to full second quadrant operation. These data are vital for system modeling validation for a full compressor-system digital twin. Results indicate the importance of ensuring reliable data acquisition and analysis, with adequate instrumentation range, accuracy, synchronization, responsiveness and positioning.

Improving the operational life and reliability of the machine and tractor fleet by non-selective methods of tribotechnics

The fl eet of domestic tractors and combines has signifi cantly used up its operational life, has a reduced time to failure, frequent repairs and high operating costs: up to 50 billion rubles per year. The engineering service of agricultural enterprises has signifi cantly weakened, most of the maintenance work on domestic machines is carried out by the owners of the machinery themselves, which causes their low quality and frequent failures. In this regard, new methods and means are being developed to improve the reliability and service life of machines, which will also affect imported equipment.

A Method for Generating Toolpaths in the Manufacturing of Orthosis Molds with a Five-Axis Computer Numerical Control Machine

This paper proposes a new algorithm for the automatic generation of toolpaths for machining complex geometric positions, such as molds used in orthosis production. The production of individualized orthoses often requires the use of multi-axis machining systems, such as five-axis machines or industrial robots. Typically, complex and expensive CAD/CAM systems are used to generate toolpaths for these machines, requiring the definition of a machining strategy for each surface. While this approach can achieve a reliable and high-quality machining process, it is very time-consuming and makes it challenging to meet the criteria for rapid production of orthopedic aids. Given that their production is a custom-made process using individual shapes as inputs, the toolpath generation process becomes even more demanding. To address these challenges, this paper proposes an algorithm suitable for the automatic generation of toolpaths for such complex positions. The proposed algorithm has been tested and has proven to be robust and applicable.

Development of double-layer rotors for asynchronous drives of mining and transport machines

Due to the simplicity of the design and high energy indicators, asynchronous electric motors (AM) are the main consumers in the power supply systems of mining enterprises. Large starting currents of motors lead to heating and damage to the insulation of the stator windings. An urgent problem to improve the reliability of mining and transport machines is the development of AM, providing smooth transients with increased starting torque and reduced starting current. In an AM with a closed-loop rotor, a magnetic conductor is performed by a charged cylinder on the rotor shaft, which is made of electrotechnical steel with high magnetic permeability. The electrical conductor is often a squirrel cage-type short-circuited rotor made of a material with high specific conductivity. A technology has been developed for producing ferro-copper alloys for the active outer cylinder of a double-layer rotor, replacing a "squirrel cage". These alloys combine the properties of an electric current conductor and a magnetic flux. This allows us to obtain the effect of displacement and reduction of current in the rotor, which is accompanied by an increase in the effective resistance of the rotor and the electromagnetic torque during the start-up process. Full-scale experiments have been carried out with asynchronous motors with various rotors having a displacement effect. Motors having double-cage or deep-groove rotors develop an increased starting torque at a relatively lower starting current. The starting quality of these motors is, on average, 1,5 times higher than that of a motor without current displacement. Replacing a double-cage rotor with a double-layer rotor leads to a decrease in the starting current by 25% and an increase in the starting torque by an average of 2,5 times. The quality factor of starting an asynchronous motor with a double-layer rotor is 3-4 times higher than the quality factor of starting similar motors with a conventional rotor cage without current displacement, 2-2,5 times compared with a double cage or a deep groove and 1,3-1,5 times compared with a rotor with a high electrical resistivity. The use of double-layer rotors limits voltage fluctuations in the network, ensures a reliable, smooth start, eliminates vibration and extends the service life of all equipment.

Application of diamond abrasive tools in the processing of technical ceramics in parts of mining machines

Technological development of mining equipment is largely initiated by the improvement of technologies and tools for its production, including in the field of surface treatment quality, which, in turn, determines the reliability and productivity of machines. The use of technical ceramics in designing parts and assemblies of mining machines allows for significantly improved technical and produc-tion characteristics since the quality of the parts' surface treatment is traditionally carried out with diamond-containing tools. The dia-mond tools with ceramic bonds have the highest grinding perfor-mance at the abrasive shaping of ceramics. The tribotechnical characteristics of tools made of a material with a ceramic structure hardened with microdipersed diamond grains are investigated in this work. On the basis of the classical approach to the deformation of rough surfaces, a model has been constructed that makes it possible to estimate the influence of the structural components of a dia-mond mineraloceramic tool on the wear of technical ceramics. The relations for calculation of grinding productivity, diamond content in the worn layer of the abrasive tool and the value of specific dia-mond consumption have been obtained. Experimental studies con-firmed the theoretical conclusions. It has been established that the greatest influence on grinding productivity is exerted by diamond granularity, loading and speed modes; diamond concentration significantly determines the relative wear resistance of the abrasive tool. The obtained relations will be useful in the design of diamond-containing tools for processing technical ceramics, which is increas-ingly used in the design of mining machines since its market is grow-ing dynamically around the world.

Most important performance evaluation methods of production lines: A comprehensive review on historical perspective and emerging trends

Production is one of the most significant building blocks that strengthen the sustainable economy of companies and thus contribute to the countries’ welfare. Performance indicators of the production line affect planning operations and the efficiency of the supply chain to which the factory is connected. The key indicators for production line designers and performance analysts to monitor and improve include production rate, resource utilization rate, and average inventory level. The production rate is the most important indicator closely affecting an industrial plant's productivity and efficiency levels. From this perspective, accurate and fast estimation of this indicator is very critical. Production rate can be calculated by simulation, analytical technique, or artificial intelligence methods according to the production line characteristics. In this comprehensive review, the most important performance evaluation methods are discussed historically and systematically about the buffer allocation problem using the snowball sampling method. With this explicit motivation, 145 papers were reviewed and classified according to production line topology, hypothetical/real-case line, machine reliability, previous method on which the method is based, and originality and/or line characteristics. To present a comprehensive comparison, the methods considered were analyzed according to different criteria. This review provides general/in-depth qualitative and quantitative discussions and highlights insights to practitioners and scholars. In addition, the impact of recent key work on production line analysis in the field is assessed along with emerging trends, evolving manufacturing paradigms are discussed, and the challenges associated with performance analysis are addressed.

Reliability and Maintainability Analysis of Table Saw Machine

Reliability and Maintainability Analysis of Table Saw Machine

Some aspects of reliability prediction of chemical industry and hydrogen energy facilities (vessels, machinery and equipment) operated in emergency situations and extreme conditions

This work is aimed at a comprehensive solution to the problem of reliable and safe operation of a transport energy system with a high energy concentration based on a universal energy carrier - cryogenic liquid hydrogen.The article discusses the possibility of using various methods and techniques to assess the reliability of machines and equipment operated in emergency situations and extreme conditions. The obtained results are analyzed.Currently, the oil and gas complex pays great attention to the development of hydrogen technologies, as well as hydrogen energy in connection with the relevance of the Climate Agenda. In this regard, hydrogen energy facilities are of the greatest interest: cryogenic hydrogen reservoirs, cryogenic hydrogen pipelines, cryogenic oxygen reservoirs and cryogenic oxygen pipelines, as well as cryogenic reservoirs and pipelines for storing process nitrogen gas. An important role for global energy exchange is played by LH2 tankers for transporting cryogenic hydrogen. For example, Australia and Japan built the first LH2 tanker to transport hydrogen from Australia to Japan. In addition, another 85 LH2 tankers are expected to be built.After transportation, cryogenic hydrogen is stored in cryogenic hydrogen storages, usually also representing cryogenic hydrogen tanks with piping in the form of cryogenic pipelines, as well as cryogenic nitrogen tanks for storing process nitrogen gas. Further, hydrogen is used in road transport, aviation, ship fleet, industry, and energy. The main elements of mobile, stationary and airborne hydrogen storage systems are under critical loads and are in the area of increased study and attention.In this regard, we considered the functions of changing the main operational characteristics, made proposals on the possibility of predicting the development of accumulated faults and proposals for ensuring safety and extending the life of objects, taking into account the determination of local and integral damage to cryogenic tanks and pipelines.

Flexible boron nitride-based composite membrane with superhydrophobic/electrothermal synergistic response for enhanced passive anti-icing and active de-icing

Ice crystals and snow adhesion can affect the operation and reliability of machines in cold, snowy conditions, so anti-freeze systems are often installed in machines. Commonly used ice protection systems usually place electrothermal and a hydrophobic layer in one layer, or create a hydrophobic layer at top of the electrothermal layer. However, the hydrophobic layer ruptures and there is a risk of a short circuit when water comes into contact with the electrically heated layer. In this paper, a method of using boron nitride film to separate the functional layer is proposed to reduce the risk of short circuit in the electric heating layer. Finally, boron nitride film is successfully used as the intermediate layer to connect the superhydrophobic layer with the electric heating layer, which realizes the passive. The results show that the contact angle of the BN-based composite membrane can reach more than 160°. Under the heating power of 0.54 W/cm2, the BN-based composite membrane can reach the steady state temperature of 82.6 °C in 6 min, the deicing efficiency can reach 15.435 kg h−1 m−2, and the removal of 6 mm thick melting ice takes only 185 s. In addition, after 10 electric heating cycles, the BN-based composite membrane can still maintain a temperature of about 80 °C, indicating that it has good durability. This study is of great significance for practical ice control applications.

Lightweight pyramid attention residual network for intelligent fault diagnosis of machine under sharp speed variation

Deep learning based diagnostic methods have obtained great success in intelligent fault diagnosis of machines. However, most of the existing methods are developed for fault diagnosis task under stable working conditions, and cannot handle the data generated under sharp speed variation favorably. Besides, the large model parameters and high computing costs of these methods fail to meet the requirement of small memory and fast-precise prediction in practical applications. To address these challenges, a lightweight pyramid attention residual network (PARNet) is proposed in this paper. First, an efficient pyramid squeeze convolution module is proposed to capture multi-scale information from raw signals. Then, a soft squeeze-and-excitation attention is designed to establish long-range channel dependency, and fuse features of different scales. Based on the above improvements, a pyramid attention residual block is proposed as basic feature learning unit, and a concise CNN-based model PARNet is built using PAR block. Extensive experiments in two case studies are conducted to validate the effectiveness and adaptability of PARNet under different speed variation conditions. The test results demonstrate the superiority of the proposed method for machine fault diagnosis and reliability assessment in terms of recognition accuracy and model parameter complexity, compared with other advanced diagnostic methods.

Analisis Perawatan Heading Machine dengan Metode Reliability Centered Maintenance (RCM)

The heading machine is a critical type of machine used in the production of header tubes. If a failure occurs during the production of header tubes, the product cannot proceed to the next process and must be discarded. However, this machine frequently encounters component failures that require halting the machine for repairs, which is detrimental to the company. This research aims to enhance the machine's reliability by identifying the critical components and scheduling appropriate maintenance based on the heading machine's needs. The research method used is RCM, which includes collecting data on machine downtime and repairs, creating an FMEA table, identifying critical components, calculating TTF and TTR, determining component distribution patterns, establishing parameters, calculating the reliability of critical components, and scheduling maintenance. The research findings identified the critical components as conical spring for orifice and jaw for thread. The calculation of the reliability value using the Weibull distribution equation with scale and shape parameters for the conical spring for orifice components are 1001.78 and 1.12285, respectively. For the jaw for thread component, the scale and shape parameters are 532.083 and 1.33233, respectively, based on values derived from statistical software. As a result, the maintenance schedule should be set when the reliability value of the conical spring for orifice and jaw for thread reaches 0.5 at (t) 720 hours or 30 days, and (t) 390 hours or 17 days, respectively.

Cell Formation Problem with Alternative Routes and Machine Reliability: Review, Analysis, and Future Developments

The Cell Formation Problem (CFP) is a widely studied issue that aims to group machines effectively based on criteria such as productivity, lower costs, and greater efficiency. In recent years, more characteristics were summarized relating to this problem. This paper provides a bibliographic examination of methodologies addressing the CFP in cellular manufacturing, focusing on novel approaches such as alternative routes and machine reliability. The articles were obtained from Scopus and Web of Science and filtered using the PRISMA methodology. Classification based on objective functions, constraints, and methodologies facilitated informative visualizations for analysis. Findings indicate a focus on capital utilization optimization, with cost reduction via intercellular moves minimization as the primary objective. Common constraints include limits on the number of machines per cell, restricting machines to a single cell and singular production routes per part. The genetic algorithm predominates as a non-exact solution approach, while the “ε-constraint” method is commonly used. This study offers insights into contemporary trends in solving the CFP with alternative routings and machine reliability, aiding researchers and professionals in the field to improve the quality of their investigations.

A New Modeling Approach and Comprehensive Monitoring of Electrical Faults Through Spectral Analysis in DSIM

The objective of this study is to evaluate the dependability of double-star induction machines by employing meticulous monitoring and intelligent handling of potential electrical malfunctions that may arise during their functioning. Specifically, the research delves into the examination of three distinct fault types: rotor bar breakage, stator phase opening, and inter-turn short circuit. Utilizing a mathematical model developed in the natural reference frame (abc), our aim is to comprehensively delineate both normal and faulty operational scenarios. To categorize and gauge the gravity of identified faults, we employ a methodology rooted in spectral analysis. In order to ensure continuous machine operation, especially in instances of stator phase opening, our proposed mitigation approach entails intentionally opening a secondary phase positioned at a 90-degree angle to the faulty phase. This strategic alteration transforms the machine into a dual two-phase configuration. Through rigorous simulation analyses, our findings underscore the efficacy and pragmatic viability of the devised fault detection technique, offering valuable insights into the domain of electrical machine reliability and fault management.

Increasing the strength of potato digger elevator rods

Abstract Currently, high-quality steels are used to increase the reliability of potato digging machines and the durability of elevator rods. Recently, new materials, new technologies and non-metallic materials that are not inferior in strength to steel have been used in agricultural machines. Such materials significantly reduce the mass of the working parts. This article examines the deformation of a new elevator rod design due to the action of soil and tuber-bearing mass. The rods, covered with double-cavity rubber profiles, vibrate the component of the potato heap. During vibrations, in addition to static deformations, dynamic deformations occur, depending on the magnitude of the impact force. As a result, it was found that to eliminate the loss of tubers between the bars of the separating elevator, given the maximum deflection value of 0.03 m and the weight of the soil-tuber-bearing mass, the rubber bar must have the following parameters: elasticity model E = 50 N/mm2; rod perimeter 11x11 mm; the maximum jump height of a mass weighing 130 kg is no more than 0.6 m.

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

Frictional Properties of Two-Dimensional Materials: Data-Driven Machine Learning Predictive Modeling.

Friction, typically associated with reduced efficiency and reliability of machines and devices, occurs when two objects are displaced against each other. This is a strongly material-dependent phenomenon, and the emergence of many 2D materials has opened up new opportunities to design systems with desired tribological properties. Here, we combine high throughput simulations and machine learning models to develop a statistical approach of adhesion, van der Waals, and corrugation energies of a large dataset of monolayered materials. The machine learning models are used to predict these closely related to friction energetic properties and link them to easily accessible atomistic and monolayer features. This approach elevates the materials' perspective of frictional properties. It demonstrates that data-driven models are extremely useful in discovering important structure-property functionalities for frictional property interpretations as a fruitful route toward desired tribological materials.

Dynamic reliability evaluation of TBM components in tunnel construction

The complex geological conditions in tunnels pose a huge challenge to the reliability of tunnel boring machine (TBM). However, existing reliability studies typically focus on core structures such as cutters and cutterheads, with less consideration given to the rest of the components that frequently fail. In this study, the reliability analysis and dynamic evaluation of TBM components with high failure rates are carried out relying on the Shanxi Central Yellow River Diversion Project. The life distribution and reliability variation characteristics of TBM components under different rock mass classes are investigated in terms of tunnelling time and tunnelling distance as two types of life data indexes. And the life index which is more suitable for the reliability evaluation of TBM components is identified by comparison. On this basis, a dynamic evaluation method for the reliability of TBM components under the condition of multi‐classes surrounding rock is proposed. This method can quickly evaluate the current reliability of TBM components and serve as the basis for preventive maintenance. The results of this study play a certain role in supplementing the reliability research of TBM and also provide a scientific basis for optimizing the design and maintenance strategy of TBM components.

Fault Diagnosis and Prediction of Remaining Useful Life (RUL) of Rolling Element Bearing : A review state of art

Fault diagnosis of rolling element bearings is a critical aspect of machine maintenance and reliability. Bearings are extensively used in various industrial applications, and their failure can lead to costly downtime and equipment damage. Rotating machinery under continuous overload conditions can indeed significantly degrade bearing life and lead to various other issues. To identify issues in rolling element bearings (REB), several techniques and methods are employed. Diagnosing faults in ball bearings while simultaneously estimating the Remaining Useful Life (RUL) of the bearing is a crucial aspect of predictive maintenance. This can be achieved through a combination of signal processing techniques, machine learning methods, and RUL prediction models. The estimation of a bearing Remaining Useful Life (RUL) is of significant importance in predictive maintenance strategies to avoid unexpected failures, reduce downtime, and optimize maintenance costs. This literature review aims to explore the methodologies, techniques, and advancements in predicting the remaining useful life of bearings.

AUTOMATED COMPLEX FOR MEASURING THE WINDING TEMPERATURE OF TRACTION ELECTRIC MACHINES DURING THEIR HEATING TEST

Purpose. The development and implementation of automated complexes for testing traction electric machines of rolling stock at the current stage is an extremely important task. Automated test complexes provide an opportunity to significantly increase the quality and efficiency of tests on traction electric machines. Thanks to the use of advanced technologies, these complexes ensure accurate data collection and analysis during tests. One of the main advantages of automated complexes is the reduction of cost and labor costs for conducting tests. Instead of manual intervention by operators, automation systems can perform many tasks automatically, saving time and reducing labor costs. Methodology. The methodological basis of the research is analytical research methods based on the compilation of a calculation system of differential equations based on the accepted test scheme, as well as the development of an algorithm for conducting tests on a designed stand. Findings. The proposed test complex allows to conduct tests of collector traction electric machines, to determine the excess temperature of the windings of electric machines without the need to stop them. The functional scheme of the measuring complex allows you to automatically record the parameters of the tested electric machines, and if necessary, it is possible to output the test results to a computer with the subsequent formation of the test protocol in electronic form. The accuracy of the determination of the measured parameters is ensured by the accuracy of the measuring devices and the algorithm of the measuring complex. Originality. The proposed automated measuring complex is a big step in the direction of improving the efficiency and reliability of heating tests of traction electric machines. One of its key advantages is the ability to perform tests without stopping the machine, which significantly reduces the time and costs of conducting tests. Automatic registration of measured parameters allows to record several controlled indicators at the same time, which provides a more complete and objective assessment of the state of the electric machine during testing. This complex not only facilitates the testing process and reduces the cost of time and resources, but also increases the reliability of the obtained results, which is important for manufacturers and operators of transport systems. In general, the automated measuring complex opens up new opportunities for increasing the efficiency and reliability of testing electric traction machines, contributing to the further development of transport technologies. Practical value. The implementation of the obtained results by enterprises for the repair of traction electric machines of rolling stock will allow to reduce the material costs of carrying out repairs while simultaneously improving the quality of the conducted tests.