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The laser powder bed fusion (L-PBF) additive manufacturing process offers many advantages. However, its industrial integration still needs to be improved due to the certification constraints of the produced parts. Thus, this paper proposes a phenomenological causal chain of process failures to ensure the control of the L-PBF process and further certify the quality of the parts produced. Consequences of unsuitable thermal regimes are analysed using a failure mode and effects analysis (FMEA) approach, allowing a more global understanding of all the causal chain-related data. The proposed approach is based on standard acquisition equipment available on industrial machines and proves relevant for identifying process failures. Experimental validation of the proposed monitoring method is implemented on a use case using dedicated software. Finally, it is shown that the method is relevant in this confrontation and compatible with the time scale of the process for future NC integration and real-time monitoring. [ABSTRACT FROM AUTHOR]

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]

Industrial machinery relies heavily on accurate fault diagnosis to maintain its reliability and operational efficiency. However, analyzing vibration signals for early fault detection is complex, as these signals often suffer from low signal-to-noise ratios, background noise, and random interferences. While wavelet filters are frequently employed for identifying informative frequency bands, determining the optimal filter parameters is crucial for accurately extracting repetitive transients related to faults. This research proposes a novel approach that utilises a crayfish optimization algorithm (COA) to adaptively optimise the wavelet filter. The COA employs correlated kurtosis (CK) as a fitness function to guide the optimization process. This method addresses limitations related to inaccurate CK period estimation through a continuous update mechanism, ensuring more precise parameter selection. Through its application to various industrial cases, the proposed methodology demonstrates its superiority over existing techniques in accurately extracting informative frequencies. This advancement enables more precise fault diagnosis, leading to improved maintenance strategies and minimized downtime in industrial environments. [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]

Copyright of Nonferrous Metals (Extractive Metallurgy) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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.)

Accurate prediction of remaining useful life (RUL) for in‐service systems plays an important role in ensuring efficient operation of industrial equipment and in preventing unexpected equipment failures. In this paper, we present a prognostic framework for real‐time RUL prediction based on joint modeling of both degradation signals and time‐to‐event data. The proposed model employs a change point‐based general path model to capture signal non‐linearity and Neural network (NN) based Cox model to link the time‐to‐event data with the estimated degradation trend. An empirical two‐step scheme for hyperparameter estimation is proposed to enhance prognostic accuracy. Furthermore, an efficient Bayesian model updating procedure, integrated with recursive particle filtering, is used to facilitate online prediction, achieving accurate RUL prediction in real‐time and accounting for uncertainties in RUL prediction. Simulation and real‐life case studies demonstrate the advantages of the proposed method over existing approaches. [ABSTRACT FROM AUTHOR]

To address the issues of sparse training data, complex entity structures, and uneven entity distribution in the industrial equipment failure domain, this study constructs an industrial equipment failure named entity recognition corpus. Due to the difficulty of character level named entity recognition models in representing the professional vocabulary information in the field of industrial equipment failure, this study proposes a character enhanced industrial equipment failure named entity recognition model to address this problem. In the embedding layer, professional vocabulary information is directly fused between the Transformer layers of ROBERT WWM (Robustly Optimized BERT Pretraining Approach with Whole Word Masking) to allocate word information to each of its constituent characters for enhanced semantics. The global semantic information is obtained through a BiLSTM (Bidirectional Long Short Term Memory), and the CRF (Conditional Random Field) is used to learn the dependency relationship between adjacent labels to obtain the optimal sentence level label sequence. Experimental results demonstrate that the proposed model has good performance on industrial equipment fault named entity recognition tasks, with an average F1 score of 92.403%. [ABSTRACT FROM AUTHOR]

Predictive maintenance has gained importance due to industrialization. Harnessing advanced technologies like sensors and data analytics enables proactive interventions, preventing unplanned downtime, reducing costs, and enhancing workplace safety. They play a crucial role in optimizing industrial operations, ensuring the efficiency, reliability, and longevity of equipment, which have become increasingly vital in the context of industrialization. The analysis of time series' stationarity is a powerful and agnostic approach to studying variations and trends that may indicate imminent failures in equipment, thus contributing to the effectiveness of predictive maintenance in industrial environments. The present paper explores the use of the Augmented Dickey–Fuller p-value temporal variation as a possible method for determining trends in sensor time series and thus anticipating possible failures of a wood chip pump in the paper industry. [ABSTRACT FROM AUTHOR]

As an important piece of equipment in industrial production, the health of an air compressor directly impacts the success of production. Therefore, researching fault diagnosis methods for air compressors is of significant importance in improving the continuity, reliability, and safety of production. Traditional fault diagnosis methods, however, struggle to obtain accurate fault features. The measurement of feature distribution differences between various working conditions lacks sufficient domain adaptability, making it challenging to achieve high recognition accuracy. Additionally, the operation of air compressors generates background noise, which can introduce interference and impact the accuracy in fault detection. In order to overcome these limitations, a fault diagnosis method for air compressors based on feature fusion is proposed. Firstly, the Mel-frequency cepstral coefficients (MFCC) features and wavelet transform features of the air compressor are extracted separately. Then, late fusion is applied at the decision level to combine confidence scores and predicted bounding boxes. The best network model is determined based on evaluation metrics to complete the classification. Based on the experimental results analysis, the feature fusion method demonstrated superior recognition performance. [ABSTRACT FROM AUTHOR]

As an important component of mechanical transmission systems, internal splines are widely used in aerospace, industrial equipment, and other fields. However, internal splines are prone to deformation and shrinkage after heat treatment. At present, most internal splines with a pitch circle diameter greater than φ60 mm can be processed and shaped by ordinary corundum grinding wheels, but there is no effective processing method for the shaping of small- and medium-sized internal splines. This paper establishes a single abrasive material removal model; uses Abaqus to simulate three-body free grinding; and analyzes the effects of abrasive rotation angle, rotation speed, and grinding depth on material removal under different conditions. By comparing the tooth lead deviation and tooth direction deviation before and after internal spline grinding, the experimental results show that after ultrasonic vibration grinding, the internal spline tooth profile deviation is reduced by 41.9%, and the tooth direction deviation is reduced by 44.1%, which provides a new processing method for the deformation recovery of internal splines after heat treatment. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office 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.)

Predictive maintenance (PM) is a data-driven approach to performing proactive maintenance by analyzing the condition of the equipment in any industrial setting. The high-precision sensors are widely adapted to meticulously analyze critical maintenance conditions using such a datadriven approach. In a similar context, a fiber brag grating (FBG) sensor is a passive and high-precision sensor that is widely used in industries where conventional sensors are not preferred. Broadly, this article presents four sub elements of the proposed integrated system such as the design of the sensor element, signal processing scheme (SPS), machine learning (ML) model for predicting anomalies, and decision support system (DSS) to suggest maintenance actions. Also, this article highlights an experimental case study on vibration monitoring and analysis of real-time signals for making proactive maintenance decisions. An FBG vibration sensor of center wavelength 1,550 nm is designed and utilized to acquire real-time vibration signatures of a rotating machine under test. A piezoelectric vibration sensor is used with the FBG sensor to compare the vibration response obtained during the test. Pre-processing of raw signals is performed using a moving average filter (MAV) followed by a low pass filter to nullify the effect of noise. To obtain proactive maintenance decisions, a DSS model is prepared by considering the processed vibration signatures. Various maintenance conditions are tested during the experimental analysis and detailed results analysis are presented. [ABSTRACT FROM AUTHOR]

Chromatic confocal technology is widely used for precise, steady, and efficient displacement measurement in many industrial fields. It employs the confocal and dispersion principles to encode axial positions with the wavelengths of the reflected broad spectrum. The typical chromatic confocal sensor includes a light source, a dispersion objective, conjugate pinholes, and a spectral detection device. This study offers an overview of the current research on chromatic confocal technology. Because of its good performance in displacement detection, chromatic confocal technology has been widely used in contour measurement, biomedical imaging, and thickness measurements, as part of global and professional research. Due to its structural flexibility, it is also easily integrated into industrial equipment for in-machine and online profile measurements. It holds significant potential for future applications in industrial manufacturing and scientific research. However, there are also some challenges to be explored in terms of the broadband light source, dispersive optics design, and the balance between speed and accuracy in signal processing. [ABSTRACT FROM AUTHOR]

Shell–tube heat exchangers are commonly used equipment in large-scale industrial systems of wastewater heat exchange to reclaim the thermal energy generated during industrial processes. However, the internal surfaces of the heat exchanger tubes often accumulate fouling, which subsequently reduces their heat transfer efficiency. Therefore, regular cleaning is essential. We aim to detect circle holes on the end surface of the heat exchange tubes to further achieve automated positioning and cleaning tubes. Notably, these holes exhibit a regular distribution. To this end, we propose a circle-permutation-aware object detector for heat exchanger cleaning to sufficiently exploit prior information of the original inputs. Specifically, the interval prior to the extraction module extracts interval information among circle holes based on prior statistics, yielding prior interval context. The following interval prior fusion module slices original images into circle domain and background domain maps according to the prior interval context. For the circle domain map, prior-guided sparse attention using prior a circle–hole diameter as the step divides the circle domain map into patches and performs patch-wise self-attention. The background domain map is multiplied by a hyperparameter weak coefficient matrix. In this way, our method fully leverages prior information to selectively weigh the original inputs to achieve more effective hole detection. In addition, to adapt the hole shape, we adopt the circle representation instead of the rectangle one. Extensive experiments demonstrate that our method achieves state-of-the-art performance and significantly boosts the YOLOv8 baseline by 5.24% mAP50 and 5.25% mAP50:95. [ABSTRACT FROM AUTHOR]

This work presents a novel compact CMOS potentiostat-designed circuit for an electrochemical cell. The proposed topology functions as a circuit interface, controlling the polarization of voltage signals at the sensor electrodes and facilitating current measurement during the oxidation–reduction process of an analyzed solution. The potentiostat, designed for CMOS technology, comprises a two-stage amplifier, two current mirror blocks coupled to this amplifier, and a CMOS push–pull output stage. The electrochemical method of cyclic voltammetry is employed, operating within a voltage range of ±0.8 V and scan rates of 10 mV/s, 25 mV/s, 100 mV/s, and 250 mV/s. The circuit is capable of reading currents ranging from 10 µA to 500 µA. Experimental results were obtained using a potassium ferrocyanide K3[Fe(CN)6] redox solution with concentrations of 10, 15, and 20 mmol/L, and their corresponding voltammograms were evaluated. The experimental results from a discrete circuit demonstrate that the proposed potentiostat topology produces outcomes consistent with those of classical topologies presented in the literature and industrial equipment. [ABSTRACT FROM AUTHOR]

The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent manufacturing technology, the structure of industrial equipment has become more sophisticated, resulting in frequent equipment failures. However, traditional anomaly diagnosis methods suffer the issues of insufficient accuracy and are always unable to identify anomalies in time. To solve the above problems, a deep learning-based equipment anomaly diagnosis method in industrial production scenarios is proposed in this paper. Specifically, a combination model based on Selective Kernel (SK) convolution blocks is designed to improve the accuracy of anomaly identification; a lightweight model based on Depthwise Separable Convolution (DSC) and Attention Mechanism (AM) is proposed to improve the timeliness of anomaly identification; an anomaly analysis and intelligent diagnosis framework is established and implemented to automatically complete anomaly identification tasks for different equipment and practical problems. Finally, we conduct extensive experiments to validate the effectiveness of our methods. The experimental results show that the accuracy of our anomaly identification method is as high as 99.07%, and the calculation amount of the lightweight model is reduced by 84.95% compared to the baseline model. [ABSTRACT FROM AUTHOR]

It is a typical occurrence to replace some industrial equipment or components, such as electronic chips, bulbs, etc. It deals with the ideas of dependability theory, in which the likelihood of an equipment malfunctioning instantly is calculated assuming that it has operated normally for a given amount of time, 't'. In reliability, it's known as the hazard rate. The rate of hazard may be rising, falling, or staying the same. However, replacement tactics are employed to maintain output. Basically, two distinct approaches are employed. 1. Replacing a broken item; 2. Replacing items on a regular basis. Since it is a preventive measure to maintain production, the effective administration of maintaining system functionality depends on the application of both reliability concepts in addition to replacement theory. One may envision a similar issue with the personnel system as well. To determine the best manpower policies in this situation, replacement methods and dependability theory can also be coupled. This theory's application to labor systems is examined, and appropriate methods for employee replacement and advancement are covered in order to ensure the system's successful upkeep. In order to obtain the best fit, manpower planning is a dynamic process that controls the movement of workers into, through, and out of the organization. In order to determine appropriate manpower replacement policies for promotion and replacement of personnel for the successful maintenance of the system, this study discusses the application of dependability theory and the renewal process. [ABSTRACT FROM AUTHOR]

Non-equilibrium condensation (NQC) induced heat transfer in the supersonic nozzle (SSN) results in entropy production and alters the flow structure. The analysis of entropy production offers valuable insights for enhancing the design of industrial equipment by pinpointing the origins of energy losses. The interplay between frictional entropy, thermal entropy, and NQC is a fascinating but relatively unexplored subject in the field. This study aims to examine the impact of the superheat degree on NQC, frictional entropy, and thermal entropy. The goal is to enhance our understanding of the interconnectedness among these three parameters and their relationship. The findings revealed that within the SSN, the generation of frictional entropy surpasses that of thermal entropy generation. Upon analyzing the variations in entropy production with an increase in the degree of superheat, a general trend of ascending–descending can be observed for thermal, frictional, and total entropy productions. Furthermore, as the degree of superheat increases, both the droplet diameter and liquid mass fraction within the nozzle decrease. Optimization techniques were employed to determine the optimal degree of superheat for the given scenario. After the optimization process, the range of 70–90 was identified as the optimal degree of superheat. At a superheat degree of 70, the parameters of production entropy, input flow rate, condensation loss, and energy kinetics undergo changes of 19.3%, 9.8%, 99.9%, and 14.3%, respectively. [ABSTRACT FROM AUTHOR]

Marine cranes are one of the most important industrial equipment in the maritime field. The base of a marine crane is dynamically moving as the motion of the ship's six degrees of freedom that is affected by offshore environmental loads. There is a coupling between the crane and the ship, which means the crane operation and the ship motion affect each other. In this paper, co-simulation technology is employed to construct the virtual marine operation system which is composed of diverse Functional Mock-Up Units (FMUs) exported using the Functional Mock-Up Interface (FMI) standard and System Structure and Parameterization (SSP) standard to define the structure and parameters based on the co-simulation platform Vico. A path planning case for the Palfinger crane is implemented using the A* algorithm. The physical three-dimensional working space of the crane is discretized into a finite number of nodes in joint space. The cost is defined by the variable of the ship motion to optimize the marine operation. The obtained discrete nodes are smoothed to get the velocity of the actuators as control signals. Simulation of the crane operation is carried out in the virtual operating system following the planned path. [ABSTRACT FROM AUTHOR]

The sliding window method is commonly used for non-intrusive load disaggregation. However, it is difficult to choose the appropriate window size, and the disaggregation effect is poor in low-frequency industrial environments. To better handle low-frequency industrial load data, in this paper, we propose a vertical non-intrusive load disaggregation model that is different from the sliding window method. By training multiple electrical parameters at a single point on the bus end with the corresponding load data at the branch end, the proposed method, called multiple electrical parameters to point (Mep2point), takes the electrical parameter data sampled at a single point on the bus end as its input and outputs the load data of the target device sampled at the corresponding point. First, the electrical parameters of the bus end are processed, and each item is normalized to the range from 0–1. Then, the electrical parameters are vertically arranged by their time point, and a convolutional neural network (CNN) is used to train the model. The proposed method is analyzed on low-frequency industrial user data sampled at a frequency of 1/120 Hz in the real world. We compare our method with three advanced sliding window methods, achieving an average improvement ranging from 9.23% to 22.51% in evaluation metrics, while showing substantial superiority in the actual decomposed images. Compared with three classical machine learning algorithms, our model, using the same amount of data, significantly outperforms these methods. Finally, we also compared our method with the multi-channel low window sequence-to-point (MLSP) method, which also selects multiple electrical parameters. Our model's complexity is much less than that of the MLSP model, and its performance remains high. The superiority of our model, as presented in this paper, is fully verified by experimental analysis, which can produce better actual load decomposition results from each branch and contribute to the analysis and monitoring of loads in industrial environments. [ABSTRACT FROM AUTHOR]

Rotating machinery is critical functional part of industrial mechanical equipment, and health status of rotating machinery is closely related to equipment stability, reliability and safety. Vibration signals for health prediction are often collected under operating conditions with variable loads and speeds, which makes health prediction more challenging. STFT-based time-frequency representation methods are widely used for the health prediction of rotating machinery. However, these methods lack specific learning mechanisms to effectively distinguish the time-frequency representations at different time points and frequency bands and highlight important feature information. To vanquish the weakness, this paper develops a novel dynamic adaptive time-frequency attention residual network (TFARNet) for rotating machinery intelligent health prediction. Firstly, a new adaptive STFT time-frequency attention (TFA) unit is developed to recalibrate time-frequency features, thereby enhancing important information and suppressing redundant information. Secondly, the TFA unit is inserted into the residual network, by stacking multiple residual blocks and TFA units to establish TFARNet and efficiently learn more discriminative features. Thirdly, label smoothing regularization and dynamic learning rate are employed to accelerate model convergence and optimize the model training process. Finally, three cases are carried out to evaluate the developed method. Compared with the other seven health prediction methods, the developed method can achieve higher prediction accuracy. [ABSTRACT FROM AUTHOR]

As an important part of industrial equipment, the safe and stable operation of rolling bearings is an important guarantee for the performance of mechanical equipment. Aiming at the problem that it is difficult to characterize the running state of rolling bearings, this paper mainly analyzes and processes the vibration signals of rolling bearings, extracts and fuses multi-information entropy, and monitors the running state of rolling bearings and predicts the remaining useful life prediction (RUL) through test verification. Firstly, in view of the difficulty in characterizing the bearings running state characteristics, a rolling bearings running state monitoring method based on multi-information entropy fusion and denoising autoencoder (DAE) was proposed to extract the multi-entropy index features of vibration signals to improve the accuracy of feature extraction, and to solve the problem of not obvious information representation of a single feature indicator and missing information in the feature screening process. Secondly, in view of the problems of low prediction accuracy and poor robustness and generalization in traditional RUL models, a rolling bearings RUL model combining convolutional autoencoder (CAE) and bidirectional long short-term memory network (BiLSTM) was proposed. The introduction of convolution operation made CAE have the feature of weight sharing, reducing the complexity of the model. Finally, the XJTU-SY data set was used to verify the constructed model. The results show that the condition monitoring model established in this paper can accurately evaluate the running state of the rolling bearing and accurately locate the failure time. At the same time, the residual life prediction model can realize the residual life prediction of most data sets, and has good accuracy and robustness. [ABSTRACT FROM AUTHOR]

This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor's health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper intends to provide key insights to the manufacturing industrial park designers for selecting the typical days of electric load and planning the resources for energy-producing infrastructure. First, a hybrid time-series model of energy-consuming equipment based on the autoregressive integral moving average model (ARIMA) and temporal convolutional network (TCN) is generated. According to this model, the energy consumption (EC) curve of large equipment in the industrial park can be depicted. Moreover, the present study designed a TLSM-IPML (typical load stratification method for industrial parks with manufacturing load) algorithm based on the typical day-selected method. The data clustering method is utilized to analyze the energy usage characteristics. Furthermore, an energy usage-based planning model is proposed, network constraints are considered, and a multi-optional method is designed to solve the problem. Finally, case studies validate the superior performance of TLSM-IPML in analyzing the characteristics of energy consumption and planning the model in reducing MES (manufacturing industrial factory integrated energy system) economic costs. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) is critical to ensure optimal operating efficiency and minimize costly failures of industrial machinery. The PdM leverages a machine learning (ML) method to predict remaining useful life (RUL) for implementing minimal-cost and reliable maintenance. RUL prediction involves multiple steps, such as data collection, data pre-processing, and RUL estimation, each of which incorporates various methods. This study conducted a critical review of RUL estimation and data pre-processing specifically for turbofan engines and bearings, categorizing existing models to offer a high-level perspective on RUL prediction. This review demonstrates that the indirect mapping method exhibits outstanding prediction accuracy compared to the direct mapping method. Moreover, it highlights that autoencoder techniques and their variants demonstrate commendable performance in extracting features from turbofan engines and bearing datasets. Furthermore, the paper proposes potential areas for future research to improve RUL prediction in this domain. [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]

The dual-spectrum triocular camera system composed of a binocular visible light camera and mid-infrared camera is used for high-precision thermal fault detection and thermal field reconstruction of industrial equipment. The realization of its function depends on the high-precision camera parameter calibration. The difficulty lies in how to realize the infrared camera calibration quickly and improve the parameter accuracy of multi-lens camera. In this paper, according to the characteristics of the dual-spectral triocular camera system, the theoretical model is constructed, and the circular asymmetric calibration plate under infrared supplementary light is selected as the calibration object through experiments. A method for combining infrared image adaptive histogram enhancement and binarization processing based on the Sauvola algorithm is proposed to effectively calibrate the infrared camera. A global parameter optimization method based on traditional passive vision binocular stereo calibration is proposed. The optimization of experimental parameters finds the reprojection error value is 0.16, which meets the demand of high-precision calibration and solves the problem of difficult-to-calibrate weak image texture of infrared camera and the calibration accuracy of the camera under different imaging capabilities. [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]

The mining and metallurgical industry is a fundamental sector of China's industry, consisting of various subdivisions such as mining, mineral processing, smelting, etc. Mining and metallurgical equipment are characterized by harsh operating conditions, complex operation and maintenance, high energy consumption, significant pollution, substantial loss, and a high accident rate, resulting in an urgent need to leverage new infrastructure like 5G and the Industrial Internet to provide cloud-based digital and intelligent services for the "deaf, dumb, and dull" equipment in this industry. Therefore, constructing an industrial internet platform for cloud-based mining and metallurgical equipment is one of the crucial means to achieve resource aggregation and optimal allocation, enhance equipment efficiency and safety levels, and is also an inevitable choice for realizing the industry's digital transformation and upgrading. Based on the background and current situation of the construction of the cloud industrial Internet platform on equipment and the needs of the mining and metallurgy industry, the cloud industry Internet platform on mining and metallurgy industry equipment is studied. The construction contents of the device layer, edge layer, IaaS layer, PaaS layer, application layer, and security protection system are provided, respectively. Through constructing the three major functional systems: network, platform, and security, key industrial equipment assets, production and manufacturing systems, management systems, and resource applications have been fully integrated. The specific construction steps and effects of the platform are analyzed through a practical case of a mining and metallurgical enterprise. The proposed construction scheme can meet the needs of constructing a cloud industrial Internet platform for equipment in the mining and metallurgy industry and solve the common problems of cloud industrial equipment in the industry, so as to provide a theoretical and practical reference for mining and metallurgy enterprises to carry out similar platform construction, support fine equipment management, reduce equipment operation and maintenance costs and accident rates, promote the public service ecological construction of cloud industrial equipment solutions, and help the collaborative development of upstream and downstream of the industrial chain. [ABSTRACT FROM AUTHOR]

Analyzing the vibration of blower motors in industrial sites to detect and predict anomalies is important for increasing operational efficiency and improving predictive maintenance. Existing methods risk malfunctions due to over-sensitivity, and deep learning approaches, in particular, require large datasets for training and are difficult to maintain. This research is divided into two phases: diagnostic analysis and predictive analysis. The first phase, diagnostic analysis, utilizes the Pruned Exact Linear Time (PELT) algorithm and Statistical Process Control (SPC) techniques to identify vibration data points with abrupt pattern changes and outside the normal operating range. The second step, predictive analysis, utilizes the Extreme Gradient Boosting (XGBoost) Regression algorithm to identify patterns in the vibration data to predict the occurrence and timing of potential failures. The algorithm used in the study provides computational efficiency and high prediction accuracy, which can compensate for the shortcomings of existing methods. The study also presents a methodology that can effectively detect and predict anomalies in blower motors and similar mechanical equipment in industrial environments. [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]

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]

In this study, the focus is on investigating prevalent issues of rupture and wrinkling that occur during the extra-deep drawing process. These defects are very common in a local sanitary equipment industrial company, mainly in the manufacture of bathtubs, which increases scrap and leads to loss of time and costs in production. To analyze these defects, a numerical simulation of the bathtub extra-deep drawing process was performed with industrial parameters. The originality lies in controlling non-uniform blank holder pressures generated from six actuators in order to control the flow of the blank between the blank holder and the die and ensure the production of defect-free bathtubs. 3D and ultrasonic thickness measurements were performed on a bathtub manufactured without defects. Numerical and experimental plots of the thickness reduction show that the two approaches are in good agreement. The numerical results demonstrate that there are no rupture or wrinkling defects in the bathtub final shape, which exactly matches the actual case manufactured by the company. The numerical analysis was also performed on different cases that can cause rupture and wrinkling defects, namely: the influence of the blank holder pressure, the blank initial shape, and the die design using draw beads. [ABSTRACT FROM AUTHOR]

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Equipment maintenance is a key aspect to maximize its availability. Nowadays, much of the functioning and condition monitoring data from industrial machines is collected through sensors and stored, for off-line analysis. The present work focuses on data analysis of a screw conveyor of a biomass industry. The screw velocity and load were monitored and analysed, in order to detect and predict possible faults. A machine learning approach was used to detect anomalies, where different algorithms were tested with the data available, in order to train an anomaly classifier. The anomaly classifier was able to accurately identify most anomalies, based on historical data, temporal patterns and information of the maintenance interventions performed. The research carried out allowed to conclude that the Extra Trees Classifier algorithm achieved the best performance, among all algorithms tested, with 0.7974 F-score in the test set. The anomaly classifier has been shown to achieve remarkable accuracy in identifying anomalies. This research not only improves understanding of the performance of screw conveyors in biomass industries, but also highlights the practical utility of employing machine learning for proactive fault detection. [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]

This research aims to analyze the importance of inventory control and management, especially regarding the components used in industrial machinery maintenance. The methodology used was based on a quantitative approach, descriptive scope, and non-experimental cross-sectional design. Principal Component Analysis (PCA) and Clustering techniques were employed to evaluate both the quantities consumed and the frequency of usage, to efficiently categorize the components according to their distinctive attributes. This approach is proposed as an objective guidance that will significantly contribute to improving effectiveness and sustainability in the overall management of spare parts inventories. The developed methodology allowed for a detailed description of spare parts behavior; 1,753 references of spare parts used in the maintenance of industrial equipment in a food factory during the period from the second half of 2021 to the first half of 2022. The highest production peaks were recorded during this time, highlighting consumed quantities and usage frequencies, guiding the material management teams in making decisions about spare parts storage. [ABSTRACT FROM AUTHOR]

This research article delves into the vital importance of inventory control and management, specifically in relation to spare parts used in the maintenance of industrial equipment. Multivariate statistical methods are used to evaluate both the quantities consumed and the frequency of use, with the purpose of effectively classifying spare parts according to their distinctive characteristics. This approach is intended to be an objective guide, contributing significantly to the efficiency and sustainability in the integral management of spare parts inventories. [ABSTRACT FROM AUTHOR]

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]

High-strength gears are crucial components in high-end industrial equipment, such as deep-sea submarines, aircraft engines, aerospace vehicles, nuclear power plants, special vehicles, high-speed trains, and high-end machine tools. As these equipment representatives continue to develop towards deeper, higher, faster, and safer directions, the load carrying capacity and service life requirements for gears become increasingly demanding. This study analyzes the service environment and technical challenges of high-strength gears by researching the current state of relevant research. It summarizes the main problems in the design, processing, and performance evaluation of high-strength gears, as well as the technical challenges in transmission design, materials and manufacturing, and design and manufacturing integration. The technical difficulties in design, manufacturing, design-manufacturing integration are analyzed, and the development direction of high-strength gears is emphasized. The research progress in anti-fatigue design, manufacturing, and service performance evaluation of high-strength gears is also elaborated, along with typical applications in the fields of aircraft engines, wind power generation, and engineering machinery. The goal is to promote the advancement of high-strength gear technology and its applications. [ABSTRACT FROM AUTHOR]

The relevance of the subject matter lies in the essential importance of belt conveyors in the technological processes of processing crops and the need to investigate their dynamic characteristics as the most important factor of trouble-free operation. This study aims to explore the design and movement principles of belt conveyors, utilized by agricultural enterprises for grain collection and transportation. The methodological approach is a theoretical study of the general principles of improving the design and operating parameters of a belt conveyor for working with contaminated grain. The results obtained indicate that there is a direct relationship between the belt conveyor's performance parameters on the one hand and the conveyor belt's width, speed of movement, and density of transported grain mass on the other. All the obtained dependencies were presented in the form of mathematical equations. The main types of belt conveyors that are used to work with grain crops were also identified, and a classification of such technical devices was presented. The list of standardised regulatory documents that regulate the technical conditions for the use of belt conveyors is taken into consideration. The main design and operational characteristics of belt conveyors, which are used to work with heavily contaminated grain, were also determined. The prospects for their improvement are determined to optimise technological operations for the collection and transportation of grain crops at enterprises in the Republic of Kazakhstan's agricultural sector. The practical significance of the results obtained lies in the possibility of their use in the development of belt conveyors designed to work with grain crops of a significant degree of contamination. [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]