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This paper presents novel models for the Integrated Procurement and Lot-Sizing Problem with multiple customers and backlogging. Since allowing backlog in a traditional cost minimisation model involves dealing with intangible costs of not fulfilling the demand on time, we propose optimising service-levels while keeping the costs minimal using a budget constraint. The motivation of this study emanates from a manufacturing company that assembles commercial and industrial refrigeration equipment, using both purchased materials and in-house products. Instead of considering an aggregate demand, we consider the specific demand from various customers. This allows us to incorporate demand fulfilment decisions into the model in case of stock-outs by deciding which customers will have their orders backlogged. The fill-rate, or β service-level, is considered both globally and for customers and products individually. Computational experiments show that the service-level-driven models improve service when compared to the traditional cost model and also end up enforcing service equity among different customers at the expense of a deterioration in the global fill-rate. [ABSTRACT FROM AUTHOR]

Maintenance records of complex industrial equipment contain a large amount of unstructured data (e.g. technician notes) pertaining to repair actions and associated equipment sub-components, degradation conditions, failure mechanisms, etc. These unstructured data can yield valuable insights to improve the equipment design and maintenance plans, resulting in higher productivity and lower operating costs. Since manual review of information is time-consuming, companies make limited use of the maintenance records. To address this opportunity, we propose a taxonomy-guided method for automatically analysing the unstructured data and inferring critical information, specifically the hierarchy of the equipment's sub-assemblies and constituent parts that malfunctioned or failed during a breakdown event. Our method leverages syntactic (related to word frequency) as well as semantic (related to word co-occurrence and their meaning) knowledge. A novel contribution of our work is that we provide a confidence score for the information inferred by our method. Only the maintenance records which receive a low confidence score will require manual review to confirm the automated method's results, thus ensuring minimal use of human resources. We demonstrate the performance of our method using a real-world data set from equipment used in oil rigs. [ABSTRACT FROM AUTHOR]

Bearings are essential in machinery. Damage to them can cause financial losses and safety risks at industrial sites. Therefore, it is necessary to design an accurate diagnostic model. Although many bearing fault diagnosis methods have been proposed recently, they still cannot meet the requirements of high-accurate prediction of bearing faults. There are several challenges in this: 1) In practical settings, gathering sufficient and balanced sample data for training diagnostic network models proves challenging. 2) The damage to bearings in real industrial production sites is not singular, and compound faults are also a huge challenge for diagnostic networks. To address these issues, this study introduces a novel fault diagnosis model called EMALKNet that integrates DCGAN with Efficient Multi-Scale Attention (EMAGAN) and RepLKNet-XL, enhancing the detection and analysis of bearing faults in industrial machinery. This model employs EMAGAN to explore the underlying distribution of raw data, thereby enlarging the fault sample pool and enhancing the model's diagnostic capabilities; The large kernel structure of RepLKNet-XL is different from the current mainstream small kernel and has stronger representation extraction ability. The proposed method has been validated on the Paderborn University dataset and the Huazhong University of Science and Technology dataset. [ABSTRACT FROM AUTHOR]

This review focuses on the definitions, modalities, applications, and performance of various aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this regard, the context around Industry 4.0 and even aspirations for Industry 5.0 are discussed. The many definitions and interpretations of DTs in this domain are first summarized. Subsequently, their adoption and performance levels for rotating and industrial machineries for manufacturing and lifetime performance are observed, along with the type of validations that are available. A significant focus on integrating fundamental operations of the system and scenarios over the lifetime, with sensors and advanced machine or deep learning, along with other statistical or data-driven methods are highlighted. This review summarizes how individual aspects around DTs are extremely helpful for lifetime design, manufacturing, or decision making even when a DT can remain incomplete or limited. [ABSTRACT FROM AUTHOR]

The present paper delves into the intricate interplay between human and social capital in shaping individual well-being. Drawing on the Mincerian model's foundation of linking wages, education, and experience, we investigate the extent to which these capital forms contribute to the well-being of individuals. Recognizing the limitations of traditional human capital theory, particularly its oversight of social networks and relationship dynamics, our study adopts a comprehensive approach. Utilizing advanced structural equation modeling, we analyze data sourced from the Moroccan General Census of Population and Housing in 2014. Our model encompasses the latent multidimensional concept of well-being as defined by Sen's framework. Through meticulous measurement and analysis, we authentically present a model that captures the nuanced connections among human and social capital variables, each containing multiple latent indicators. Our empirical findings illuminate various dimensions. Human capital, as represented by educational attainment and diploma type, significantly impacts well-being. Intriguingly, health indicators exhibit a distinct relationship pattern, suggesting a divergence from traditional human capital associations. Furthermore, the measurement model for social capital underscores the notable influence of marital status, particularly among married individuals, on overall well-being. Our results reveal that well-being, positioned as an endogenous latent variable, is positively affected by both human and social capital. This study not only substantiates the relevance of Sen's approach to well-being but also underscores the value of leveraging structural equation modeling for its comprehensive assessment. The findings reaffirm the theoretical underpinnings of the relationships between human and social capital variables and offer insights into policy implications aimed at enhancing the quality of life. [ABSTRACT FROM AUTHOR]

Preventive maintenance is performed to sustain the safe and reliable operation of industrial equipments. In order to plan preventive maintenance or evaluate the existing maintenance plan, the failure behavior of the system must be modeled. The failure behavior of a repairable system is modeled utilizing counting processes. In this study failure behavior of propellers belonging to a small aircraft fleet is modeled. First non-parametric estimate of population mean cumulative function (MCF) is obtained. MCF helps discovering the special features of the maintenance data. The parametric model selection depends on the result of the trend analysis of the time between failures. In the second part of the study trend analysis is performed on propeller maintenance data. Based on the trend analysis two prospect parametric models are selected. Reliability measures are estimated using both models and results are compared to evaluate the existing preventive maintenance plan. [ABSTRACT FROM AUTHOR]

This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola–Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F 1 scores below 26% in both spectra, while VJ's scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms' strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach. [ABSTRACT FROM AUTHOR]

Products are often offered with warranties, which have become a common marketing strategy intended to indicate that product quality is guaranteed by the firm. Warranties can attract consumer purchases, but they also incur warranty costs, especially when firms falsely claim that their products are high quality; therefore, the determination of an appropriate warranty policy is of particular importance. This study considers a two‐dimensional warranty for complex repairable products, such as a vehicle, a lathe, or industrial machinery, with consideration of (1) burn‐in (run‐in), which is a supervised accelerating testing process performed before product launch to prevent defective products from being sold on the market, and (2) periodic preventive maintenance, which is performed during the usage period to prevent the occurrence of unexpected failures under normal usage conditions. The optimal burn‐in (run‐in) time and preventive maintenance intervals are determined with the aim of minimizing warranty costs for firms. The results of the study show that given a higher repair cost and a greater infant mortality failure rate, more frequent preventive maintenance is preferable, and the repair cost has the largest effect on the total warranty cost for the firm. [ABSTRACT FROM AUTHOR]

Power ultrasound is a kind of green and environmentally friendly processing technology. Still, because of its uneven distribution of acoustic pressure in the propagation medium, the large‐scale industrial application of ultrasound is limited. In order to make more effective use of ultrasound waves, this paper uses a counter‐current ultrasound‐assisted extraction container as the geometric model of simulation, calculates the distribution of the acoustic pressure field in the container by COMSOL Multiphysics software, and investigates the effect of acoustic pressure on the fluid field, and temperature field. The simulation results showed that the acoustic pressure increased with the increase of ultrasound power and frequency, tended to balance with the rise of propagation distance, and the cavitation effect was more likely to occur. The propagation of ultrasound in the fluid medium increased the velocity and vorticity of the fluid, which was conducive to the generation of mixing effects. Under the ultrasound pulse working mode for 4 min, the fluid temperature on the central axis increased in a stepped order, with a maximum increase of 19.3°C. We can see that the establishment of the simulation model provides some theoretical guidance for the more practical application of ultrasound in the food industry. Practical Applications: The limitation of ultrasound application mainly lies in the nonuniform distribution of acoustic pressure when ultrasound propagates in the fluid medium. Therefore, mastering the distribution of multiple physical fields in the reaction zone through simulation has important technical support and theoretical guiding significance for designing efficient ultrasound‐assisted extraction equipment and large‐scale industrial production of ultrasound. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The exponential increase in the production and transportation of petroleum-derived products observed in recent years has been driven by the escalating demand for energy, textiles, plastic-based materials, and other goods derived from petroleum. Consequently, there has been a corresponding rise in spills of these petroleum derivatives, particularly in water sources utilized for transportation or, occasionally, illegally utilized for tank cleaning or industrial equipment maintenance. Numerous researchers have proposed highly effective techniques for detecting these products, aiming to facilitate their cleanup or containment and thereby minimize environmental pollution. However, many of these techniques rely on the identification of individual compounds, which presents significant drawbacks, including complexity of handling, subjectivity, lengthy analysis times, infeasibility for in situ analysis, and high costs. In response, there has been a notable surge in the utilization of sensors or generalized profiling techniques serving as sensors to generate characteristic fingerprints of these products, thereby circumventing the aforementioned disadvantages. This review comprehensively examines the evolution of techniques employed for detecting petroleum-derived products in water samples, along with their associated advantages and disadvantages. Furthermore, the review examines current perspectives on methods for the removal and/or containment of these products from water sources, to minimize their environmental impact and the associated health repercussions on living organisms and ecosystems. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets. A crucial component of PdM is the remaining useful life (RUL) prediction for machines, which has garnered increasing attention. With the rapid advancements in industrial internet of things and artificial intelligence technologies, RUL prediction methods, particularly those based on pattern recognition (PR) technology, have made significant progress. However, a comprehensive review that systematically analyzes and summarizes these state-of-the-art PR-based prognostic methods is currently lacking. To address this gap, this paper presents a comprehensive review of PR-based RUL prediction methods. Firstly, it summarizes commonly used evaluation indicators based on accuracy metrics, prediction confidence metrics, and prediction stability metrics. Secondly, it provides a comprehensive analysis of typical machine learning methods and deep learning networks employed in RUL prediction. Furthermore, it delves into cutting-edge techniques, including advanced network models and frontier learning theories in RUL prediction. Finally, the paper concludes by discussing the current main challenges and prospects in the field. The intended audience of this article includes practitioners and researchers involved in machinery PdM, aiming to provide them with essential foundational knowledge and a technical overview of the subject matter. [ABSTRACT FROM AUTHOR]

The application of blockchain technology in industrial product quality traceability is analyzed to construct a new model of product quality traceability that is mainly based on blockchain technology and supplemented by an identity system. The blockchain-enabled overall technical architecture of an industrial product quality traceability system is explored, and a blockchain-based industrial product full life cycle information traceability system is constructed. First, the weights of the information indicators of different links of the industrial equipment information traceability system were calculated using the EAHP hierarchical analysis method. The manufacturing link had the largest weight, with a value of 18.8%. Second, the system's functional module design is based on the weights. We designed and developed the industrial product information traceability platform based on the hybrid blockchain chain structure of private chain + alliance chain. Finally, a manufacturing enterprise in the Xinjiang region is taken as the research object, query validation is carried out for the products produced by the enterprise, and the average query time of the system is measured to be 65.376 ms. It can meet the traceability needs of consumers and enterprise users. The research can provide theoretical support and reference for the whole life cycle information traceability of industrial products. [ABSTRACT FROM AUTHOR]

The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which may diminish its performance or even lead to its breakdown, rendering it non-operational. Lately, predictive maintenance methods have been considered for industrial systems, such as power generation stations, as a proactive measure for preventing failures. Such methods use data gathered from industrial equipment and Machine Learning (ML) algorithms to identify data patterns that indicate anomalies and may lead to potential failures. However, industrial equipment exhibits specific behavior and interactions that originate from its configuration from the manufacturer and the system that is installed, which constitutes a great challenge for the effectiveness of ML model maintenance and failure predictions. In this article, we propose a novel method for tackling this challenge based on the development of a digital twin for industrial equipment known as a Remote Terminal Unit (RTU). RTUs are used in electrical systems to provide the remote monitoring and control of critical equipment, such as power generators. The method is applied in an RTU that is connected to a real power generator within a Public Power Corporation (PPC) facility, where operational anomalies are forecasted based on measurements of its processing power, operating temperature, voltage, and storage memory. [ABSTRACT FROM AUTHOR]

Uniform magnetic field coils are widely used as electromagnetic equipment in industrial, medical, and research applications, with Helmholtz coils being a common configuration. For applications requiring a relatively high magnetic field (∼mT), Helmholtz coils typically feature a large coil cross-section. However, this characteristic makes them unsuitable for describing the magnetic field generated by a current loop model during the design process. In this work, we model the magnetic field of a large cross-section Helmholtz coil system, often referred to as a thick-walled Helmholtz coil. By employing a genetic algorithm, we transform the design problem of a Helmholtz coil into a constrained optimization problem. Subsequently, we propose a method for reverse designing a Helmholtz coil based on constraints on the target magnetic field. Finite element simulations verify the accuracy of the established magnetic field calculation model in describing the magnetic field generated by the thick-walled Helmholtz coil. Moreover, the designed Helmholtz coil effectively meets the design constraints and objectives. This method addresses the issue of significant errors in calculating the magnetic field and its uniformity resulting from the cross-sectional effect during the design of thick-walled Helmholtz coils. Furthermore, it satisfies the constraints for coil operating time and lightweight design. [ABSTRACT FROM AUTHOR]

Real-time and accurate predictive maintenance of industrial equipment is fundamental for ensuring the safety and stability of advanced manufacturing processes. Current fault diagnosis methods based on data mining rely on a large number of labeled samples, and obtaining sufficient labeled data for diagnosing industrial equipment faults is challenging. Meta-learning can achieve the diagnosis of few-shot samples to a certain extent, but the effect is not ideal. Semi-supervision can effectively leverage a large number of unlabeled samples, which is of great practical significance for handling scenarios involving limited labeled samples. However, noise interference can occur when unlabeled samples appear that do not belong to known categories. Therefore, this study proposes adaptive semi-supervised meta-learning networks (ASMNs) for noisy few-shot gearbox fault diagnosis. Firstly, a residual network with a Morlet Wavelet layer is used to extract signal features. Next, sample-level attention is defined to select unlabeled samples that are more similar to labeled sample prototypes, thereby reducing the influence of noisy samples. The adaptive metric is used to obtain the relational distance functions of labeled samples and unlabeled samples. Adaptive semi-supervised ASMNs uses unlabeled data to refine prototypes for better fault diagnosis. The effectiveness and anti-noise performance of the proposed method are verified by using two gearbox datasets with various few-shot noise scenarios. [ABSTRACT FROM AUTHOR]

United Tractors Company prioritizes sustainable waste management practices. This study investigated their current system, aiming to identify opportunities for improvement in efficiency and environmental impact. The research focused on how effectively United Tractors manages their waste streams, from minimizing generation at the source to responsibly processing and disposing of unavoidable waste. The analysis revealed a successful integrated waste management system at United Tractors, encompassing practices like composting, recycling, and proper treatment of hazardous materials. This approach demonstrates their commitment to minimizing their environmental footprint. The study went beyond current practices, identifying promising advancements in waste processing technologies like gasification and bioethanol production. These techniques offer exciting possibilities for further reducing waste and potentially even generating valuable resources from waste materials. The effectiveness of United Tractors’ system can be attributed to their comprehensive approach. The study conducted a detailed waste collection study to understand the composition and volume of waste generated across various departments. This data-driven approach allowed for a thorough cost analysis and an evaluation of the operational efficiency within the waste management system. Importantly, the study highlighted the potential benefits of incorporating advanced waste processing techniques for even greater sustainability in the future. While further feasibility studies are needed before full-scale implementation, these findings pave the way for United Tractors to develop and implement even more sustainable waste management practices in the coming years. [ABSTRACT FROM AUTHOR]

Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated fault data for model training. However, in practice, the availability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited information. To address this issue, this paper proposes a novel few-shot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation injection and meta-confidence learning to improve detection accuracy. Experimental results demonstrate the superior performance of the proposed method, which introduces perturbations, compared to existing methods. The impact of perturbation effects and class numbers on fault detection is analyzed, confirming the effectiveness of our learning strategy. [ABSTRACT FROM AUTHOR]

The strip steel is a common metallic material with a wide range of applications in various industries. However, the issue of surface defects that possess high concealment and low discrimination, which arises during the process of inspecting the quality of strip steel, imposes limitations on the overall quality of strip steel products. The challenging task of industrial quality inspection stems from the difficulty and inefficiency of detecting these defects. This paper addresses the aforementioned challenges by introducing a fast segmentation network model called the feature reused network (FR-Net), which aims to improve the detection of small defects and enhance real-time detection performance in the strip steel surface quality inspection process. In FR-Net, a feature fusion module is used to construct a feature reused fusion bypass to improve the segmentation accuracy of small defects. In addition, a lightweight feature refinement module is proposed to enhance the expression capability of the feature extraction network without increasing the computational effort. Finally, an atrous spatial pyramid pooling module with residual connectivity is proposed to fuse deep features of different scales and enhance the perception of objects of different scales. Experiments on the publicly available datasets showed that the proposed FR-Net achieved the mean intersection over union (mIoU) for NEU-Seg and SD-Saliency-900 datasets were 84.53% and 87.24%, respectively. Meanwhile, the detection speed on a single GPU was 58 FPS. Finally, the size of the proposed FR-Net model is only 45 MB, achieving a good trade-off between accuracy, speed, and model size, thus, providing a new solution for network model deployment on industrial equipment. [ABSTRACT FROM AUTHOR]

Copyright of Chimica & L'Industria is the property of Societa Chimica Italiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In industrial environments, acoustic detection of equipment can often significantly change the acoustic characteristics between training and testing data due to the absence of anomalous data guidance, as well as changes in machine operating conditions or environmental noise. In addition, during model learning, there are cases where the target data is inaccessible, but an accurate model is still needed for the invisible target domain. To address these challenges, we propose an unsupervised approach for anomalous sound detection applied to unlabeled anomalous sound detection scenarios, which is an optimization algorithm based on joint deep learning and variational Gaussian mixture models, by two jointly training neural networks for feature extraction and using the variational Gaussian mixture model to analyze the obtained embeddings for clustering. We propose a new hybrid example data enhancement method, to generate examples in multiple ways, combining various methods to align the distribution between different domains. The improved sub-cluster AdaCos loss function is used to exclude potential anomalies by learning fewer finite distributions than the standard AdaCos. Experimental results show that the proposed method achieves an average area under curve of 82.70 % and an average F1 score of 69.42 % on the datasets of seven industrial equipment machine types. The average area under curve for the source domain is 85.49 % and for the target domain is 75.63 % . [ABSTRACT FROM AUTHOR]

A vibration energy harvesting system based on fully printed piezoelectric transducers is realized. The transducers have a butterfly‐like architecture based on two single‐optimized cantilevers with a resonance frequency tuned to 49.5 Hz and can be easily mounted to an industrial engine. By comparing single‐ and multistack configurations of the piezoelectric layers combined with full‐wave or voltage doubler rectifiers, the power transfer characteristics and impedance can be matched to the electrical requirements of the sensing circuitry. A single stack with 21 μm thickness results in the maximum power output of 14.4 μW at a vibration velocity of 11.5 mm s−1, typical for industrial engines. The system is used to power a wireless sensor node on a 1 kW rotary pump in normal operation. The system can harvest up to 138 mJ within 24 h, sufficient for daily remote monitoring of the engine's vibration spectrum and temperature state. The system is thus suitable as a low‐cost, ecofriendly power source for industrial IoT applications. [ABSTRACT FROM AUTHOR]

Heat dissipation performance is crucial for the operational reliability of industrial equipment, which can be monitored by detecting the wind or airflow temperature of the radiator. The emergence of triboelectric nanogenerators (TENGs) provides new routes for wind energy harvesting and self‐powered sensing. Herein, a rotary wind‐driven triboelectric nanogenerator (RW‐TENG) with soft‐contact working mode is newly designed to achieve tunable contact areas by utilizing the reliable thermal response of NiTi shape memory alloy (SMA) to air/wind temperature. The RW‐TENG can generate different triboelectric outputs under air stimulation with different speeds or temperatures, which is demonstrated as a power source for online monitoring sensors, self‐powered wind speed sensing, and airflow temperature monitoring. Specifically, a self‐powered sensor of wind speed is demonstrated with a sensitivity of 0.526 µA m−1 s between 2.2 and 19.6 m s−1, and a self‐powered monitoring device of high airflow temperature, which show relatively short response time (109 s), strong anti‐interference ability and outstanding long‐term durability. This study introduces an innovative route for real‐time detection of airflow temperature in wind‐cooled industrial equipment, showing broad application prospects for information perception and intelligent sensing of the industrial IoTs. [ABSTRACT FROM AUTHOR]

A great amount of thermodynamic losses and mechanical damages in industrial equipment occur due to the condensation phenomenon and two-phase flows in such equipment. In this study, supercooled vapor suction has been passively used in the 3D (three-dimensional) steam turbine stationary blade. Supercooled vapor suction is one of the techniques used in turbines for resisting corrosion and erosion. For the supercooled flow suction, the design is as follows: an embedded channel inside the turbine blade in the nucleation zone, which has the utmost non-equilibrium mode; furthermore, the impacts of the location and surface of the channels devised in the turbine blade for supercooled vapor suction on the following parameters have been investigated: the two-phase flow, the suction ratio, condensation losses, erosion ratio, the average droplet growth, and kinetic energy. Based on the results, in the optimal case (case F), the condensation losses, erosion ratio, average droplet radius, and kinetic energy decrease by 3%, 24%, 6.5%, and 2%, respectively; also, the suction ratio is 3.6%. The present research reveals that the supercooled vapor suction, due to a decrease in the surface necessary for the condensation, decreases turbine blade corrosion and erosion. This fact can provide the turbine designers with beneficial information. [ABSTRACT FROM AUTHOR]

Vibration monitoring is essential for maintaining the optimal performance and reliability of industrial machinery, which experiences dynamic forces and vibrations during operation. This study delved into a comprehensive analysis of vibration monitoring in hydraulic presses, utilizing advanced measurement systems equipped with accelerometers. The proposed system included a three-axis accelerometer, data acquisition unit, and dedicated measurement software, facilitating the precise monitoring and analysis of vibrations. The influence of the sensor placement and sampling frequency on the measurement results was examined. A time and frequency analysis of the recorded measurements was performed. The results demonstrated the correlation between vibration levels and various production parameters, such as the number of parts simultaneously produced and press pressure. These findings underscore the potential for vibration measurement as a pivotal component in controlling production parameter settings. [ABSTRACT FROM AUTHOR]

Field manual labor behavior recognition is an important task that applies deep learning algorithms to industrial equipment for capturing and analyzing people's behavior during field labor. In this study, we propose a field manual labor behavior recognition network based on an enhanced SlowFast architecture. The main work includes the following aspects: first, we constructed a field manual labor behavior dataset containing 433,500 fast-track frames and 8670 key frames based on the captured video data, and labeled it in detail; this includes 9832 labeled frames. This dataset provides a solid foundation for subsequent studies. Second, we improved the slow branch of the SlowFast network by introducing the combined CA (Channel Attention) attention module. Third, we enhanced the fast branch of the SlowFast network by introducing the ACTION hybrid attention module. The experimental results show that the recognition accuracy of the improved SlowFast network model with the integration of the two attention modules increases by 7.08%. This implies that the improved network model can more accurately locate and identify manual labor behavior in the field, providing a more effective method for problem solving. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary fault feature extraction methods are still in desperate need. Order spectrum can reveal the rotational–speed–related time–varying frequency components as spectral peaks in order domain, thus facilitating fault feature extraction under time–varying speed conditions. However, the speed–unrelated frequency components are still nonstationary after angular–domain resampling, thus causing wide–band features and interferences in the order spectrum. To overcome such a drawback, this work proposes a rotating machinery fault diagnosis method based on adaptive separation of time–varying components and order feature extraction. Firstly, the rotational speed is estimated by the multi–order probabilistic approach (MOPA), thus eliminating the inconvenience of installing measurement equipment. Secondly, adaptive separation of the time–varying frequency component is achieved through time–varying filtering and surrogate test. It effectively eliminates interference from irrelevant components and noise. Finally, a high–resolution order spectrum is constructed based on the average amplitude envelope of each mono–component. It does not involve Fourier transform or angular–domain resampling, thus avoiding spectral leakage and resampling errors. By identifying the fault–related spectral peaks in the constructed order spectrum, accurate fault diagnosis can be achieved. The Rényi entropy values of the proposed order spectrum are significantly lower than those of the traditional order spectrum. This result verifies the effective energy concentration and high resolution of the proposed order spectrum. The results of both numerical simulation and lab experiments confirm the effectiveness of the proposed method in accurately presenting the time–varying frequency components for rotating machinery diagnosing faults. [ABSTRACT FROM AUTHOR]

The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs' VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ -modification to assure robustness after combining control and monitoring variables. Lyapunov's second method and the Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with a standard proportional–integral controller (PIC) and direct APBC show the proposed CAPBC's effectiveness and robustness under normal and changing conditions. [ABSTRACT FROM AUTHOR]

Rotating machines are an important part of industrial equipment. It is essential to improve their performance while reducing the manufacturing, operating, and maintenance costs. Ensuring their reliability is also crucial because a machine breakdown can result in significant costs and potential environmental and safety damage. Reliability-based optimization is an approach that aims to find an optimal and robust design that guarantees a machine's reliability. In this study, we focused on optimizing the shaft diameter and oil temperature of a rotor supported by hydrodynamic bearings. We considered the materials' elastic moduli, density, and bearing clearance as uncertain parameters. Our goal was to ensure 99% reliability regarding both the vibration amplitude and stability threshold. To model the machine, we used the finite element method and represented the bearings using stiffness and damping coefficients, considering the linear short bearing model. Due to the complexity of the model, we employed surrogate models to solve the reliability-based optimization problem. Our results showed that the optimization problem could be solved successfully using Kriging, polynomial chaos expansion, and polynomial chaos Kriging. [ABSTRACT FROM AUTHOR]

As one of the most important power equipment in industrial society, permanent magnet synchronous motor has a large number of uses in many fields. The traditional three-phase permanent magnet synchronous motor using vector control technology cannot meet the industrial high precision control requirements. So, the motor is analysed, the permanent magnet motor control model is constructed, and the chicken cluster algorithm is used to optimize the vector control parameters of the permanent magnet synchronous motor. Considering that the traditional cluster model faces the problem of local optimal solution, hypercube sampling and particle swarm model are used to optimize the chicken flock model, and an improved chicken flock motor control model is constructed to achieve efficient control of the motor. In the test of Rastrigin function, the particle swarm model, the traditional flock model and the improved flock model converge after 91, 72 and 20 iterations, respectively, and the best objective function values of 3.91e-4, 9.27e-5 and 1.79e-5 are obtained for the three models. The improved flock model has better convergence efficiency and convergence accuracy. In the test of different motor control methods, the overall fluctuation of the improved flock model is smaller, and the stability is improved by 31.65% compared with the traditional vector control, and 11.35% compared with the traditional flock model. The research content has important reference value for intelligent control of motors. [ABSTRACT FROM AUTHOR]

• Analytical protocols for the conservation assessment of operating Industrial Heritage machinery. • Material study and condition monitoring of a 1900s operating steam engine. • Engine wear identified by presence of metallic particles in lubricant oil. • Cavitation phenomenon and wear of the feed pump disclosed by vibrational analyses. The condition assessment of Industrial Heritage machinery in operation is an open research subject of utmost importance for defining conservation and maintenance plans. The present work approaches this issue by proposing the study of Industrial Heritage machinery that includes the assessment of substrates, coatings, lubricants and operating mechanisms by applying analytical techniques currently used for heritage studies together with techniques employed in condition monitoring of modern machinery. An early 20th century steam engine from the Vale de Milhaços Gunpowder Factory (Portugal), in operation for demonstration purposes, was selected as a case study. The engine metallic alloys and coatings were studied by XRF and Raman spectroscopies, and the main properties of lubricants studied according to EN ISO and ASTM standard tests. Metallic particles present in the oil due to wear were assessed by XRF. The mechanisms were examined by gamma radiography, magnetoscopy, thermography and vibrational analysis. Data showed the use of distinct alloys according to the engine components' function similar to those reported for the same period. Green copper phthalocyanine and toluidine red identified as coatings' pigments are typical from the late 20th century and thus ascribed to a previous repair. The properties of the fresh and used lubricants are slightly different but without evidence of a significant alteration that could endanger the machine's functioning. However, the presence of metallic particles in the oil suggested the existence of wear phenomena. The engine's operating speed (51 rpm) and the vapour temperature (≈154 °C) attained inside the cylinder were indirectly determined. The presence of cracks on the engine structure was assessed, and possible faults that can imperil the engine functioning were analysed. The vibrational analysis of equipment in operation revealed a lack of lubrication and wear on the feed pump journal bearings. [ABSTRACT FROM AUTHOR]

Energy conservation and consumption reduction have always been the goals pursued by the power industry. Based on these goals, this study explored a new type of whitening and water-saving technology for industrial equipment. Unlike existing direct heating and condensation heating technologies, the main innovation of this work lies in not changing the saturated wet flue gas before it is discharged into the atmospheric environment. A series of experiments were conducted on electrode plates with different wind speeds, supersaturation levels, and porosities using three principles, namely droplet electrostatic adsorption, ionic-wind-enhanced condensation, and droplet dipole deflection, through the construction of a parameter-adjustable and -controllable enthalpy and humidity chamber and a pilot development platform with a wind volume of 10,000 m3/h. In addition, the collection efficiency was calculated using thermodynamic laws. The results showed that, under the working conditions of white mist supersaturation of 5.77 g/kg, a hole opening rate of 70%, and a wind speed of 3 m/s, the water collection efficiency was the highest—close to 60%—verifying the feasibility of this technology. This technology not only eliminates white smoke but also saves water resources and has certain economic benefits, providing support for the development of industrial equipment for smoke removal in the future. [ABSTRACT FROM AUTHOR]

Conical machines with a unique conical rotor and stator design have several advantages over electrical machines with traditional designs. The conical shape allows for greater torque and efficiency, which makes these machines ideal for use in heavy machinery and industrial equipment. Besides, their compactness in comparison with traditional machines makes them easier to integrate into manufacturing processes. Furthermore, since magnetic gears (MGs) have provided a solution to overcoming mechanical challenges and disadvantages of mechanical gears, their development has become a hot research topic in the last few years. Due to asymmetric geometry and special flux paths, the analysis should be modeled in 3-D finite element tools. In this paper, a hybrid structure, named conical MG, is introduced in order to benefit simultaneously from the advantages of radial flux and axial flux structures of MG. This topology can provide more contact surface of permanent magnets (PMs) and, thus, increase the torque density, with a proper design and the optimization of dimensions. The proper shaping of the PMs and providing the correct structure of MG can lead to a high impact on the output characteristics of the system. This design improves the transmission torque by forming the magnetic fields in air gaps. To compare the proposed topology, the optimal design of the model is compared with the conventional radial flux structure using the genetic algorithm and 3-D finite element method to obtain maximum torque density. The results are compared and the superiority of the proposed model is proved. [ABSTRACT FROM AUTHOR]

Machine- and deep-learning methods are used for industrial applications in prognostics and health management (PHM) for semiconductor processing and equipment anomaly detection to achieve proactive equipment maintenance and prevent process interruptions or equipment downtime. This study proposes a Pruning Quantized Unsupervised Meta-learning DegradingNet Solution (PQUM-DNS) for the fast training and retraining of new equipment or processes with limited data for anomaly detection and the prediction of various equipment and process conditions. This study utilizes real data from a factory chiller host motor, the Paderborn current and vibration open dataset, and the SECOM semiconductor open dataset to conduct experimental simulations, calculate the average value, and obtain the results. Compared to conventional deep autoencoders, PQUM-DNS reduces the average data volume required for rapid training and retraining by about 75% with similar AUC. The average RMSE of the predictive degradation degree is 0.037 for Holt–Winters, and the model size is reduced by about 60% through pruning and quantization which can be realized by edge devices, such as Raspberry Pi. This makes the proposed PQUM-DNS very suitable for intelligent equipment management and maintenance in industrial applications. [ABSTRACT FROM AUTHOR]

Gearboxes are critical equipment in many industrial applications such as machine manufacturing, petrochemical industry, renewable energy, etc. However, due to their complex structure and regularly harsh working environment, gearboxes are inevitably prone to a variety of faults and defects during operation. Therefore, intelligent condition monitoring techniques are crucially important for early gear and bearing fault recognition and detection to avoid any industrial failure due to machine breakdowns. In this paper, an intelligent algorithm for gear and bearing fault diagnosis is suggested based on several approaches mainly: robust empirical mode decomposition (REMD), time domain features are used for the feature extraction step, while equilibrium optimizer (EO) in the feature selection. For feature classification, random forest (RF), ensemble tree (ET) and nearest neighbors (KNN) are chosen as classifiers. REMD is used to alleviate the mode mixing problem by monitoring the sifting process and selecting the optimal iteration number. EO is a recent optimization approach based on the laws of physical theory in nature. EO reduces the high-dimensional data problem, by filtering redundant features, and increasing model generalization efficiency by avoiding the over-fitting curse. The proposed approach is applied to real-time vibration signals from a healthy gearbox and four different faulty gear and bearing conditions. According to our approach, data signals are decomposed by REMD to several intrinsic mode functions (IMFs). Thereafter, time-domain features are computed for each IMF to construct the feature matrix for every gear and bearing health status. After that, EO is applied to every matrix in the feature selection step. Finally, RF, ET and KNN are used to calculate classification accuracy and give the confusion matrix. Compared to several feature selection techniques, experimental results prove the efficiency of the proposed approach in detecting, identifying, and classifying all gear and bearing defects even under different operating modes. [ABSTRACT FROM AUTHOR]

We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization techniques. Initially, concept drift detection mitigates the impact of anomalous data on model updates. Data without concept drift are used for online model training and updating, enabling accurate predictions for the next processing cycle. Bayesian optimization is then employed for inverse optimization and process parameter design. Within OPPD, we introduce the online accelerated support vector regression (OASVR) algorithm for enhanced computational efficiency and model accuracy. OASVR simplifies support vector regression, boosting both speed and durability. Furthermore, we incorporate a dynamic window mechanism to regulate the training data volume for adapting to real-time demands posed by diverse online scenarios. Concept drift detection uses the EI-kMeans algorithm, and the Bayesian inverse design employs an upper confidence bound approach with an adaptive learning rate. Applied to single-crystal fabrication, the OPPD framework outperforms other models, with an RMSE of 0.12, meeting precision demands in production. [ABSTRACT FROM AUTHOR]

This article is dedicated to the lesser-known aspects of Soviet policy regarding the compulsory seizure of industrial equipment in territories liberated by the Red Army from the fascist bloc states in 1944—1945. Based on an analysis of decrees by the State Defense Committee, the principles of interaction between representatives of industrial People’s Commissariats and military authorities in liberated territories are revealed, with the aim of dismantling and transporting the most promising industrial assets back to the USSR for the needs of restoring the country’s metallurgical complex. The paper provides examples of property disputes among leading economic entities — the industrial People's Commissariats of the USSR — over the right to dismantle industrial facilities for their own benefit. It also sheds light on the activities of Special Assembly Managements under the People’s Commissariat for Construction of the USSR and their authority in the process of seizing industrial assets in territories freed from German control in Eastern Europe. Using the case studies of non-ferrous and ferrous metallurgy enterprises located in territories of fascist bloc countries (Germany, Hungary) liberated by the Red Army, typical approaches to organizing the compulsory dismantling of their equipment are analyzed. The research conducted vividly demonstrates the significance of reparations seizures of industrial equipment in Eastern Europe for the modernization and recovery of the USSR’s metallurgical complex during 1944—1945.