Industrial Equipment Research Articles

General Industrial Process Optimization Method to Leverage Machine Learning Applied to Injection Moulding

ABSTRACTThe development of machine learning (ML) technologies is driving changes across many sectors. In industrial settings, this is called the fourth industrial revolution and encompasses several technologies pushing the boundaries of industrial automation. In this study, a general industrial process optimization (GIPO) methodology is formulated in the context of Industry 4.0 and tested on an industrial injection moulding machine (IMM). GIPO aims to encourage the practical inclusion of industrial artificial intelligence at all levels of the manufacturing process, while enabling industrial equipment to adapt to a changing processing environment. Special attention is given to the generality of the methodology so that it can be extended to other applications. In the example case study presented here, GIPO combines K‐nearest neighbours classification and nearest neighbours optimization methods to optimize an injection moulding process effectively. Practical implementation conducted on the IMM demonstrates a novel methodology to leverage data mining and ML methods in a real‐world setting to improve production quality, production time and energy cost.

A fault detection method for automated industrial equipment based on multi attribute decision fusion in knowledge graph

A fault detection method for automated industrial equipment based on multi attribute decision fusion in knowledge graph

Fuzzy flexible job shop dynamic scheduling considering machine remaining processing capacity

Dynamic fuzzy flexible job shop scheduling (DFFJSP) is significant in the medical equipment industry. Generally the dynamic flexible job shop scheduling problem considers a single dynamic factor or ignores resources such as machine itself also plays a huge role in dealing dynamic issues. Thus fuzzy processing time and machine breakdowns are considered in this paper. In order to copy with two problems mentioned above, the model of DFFJSP containing two objectives of makespan and energy consumption is established. To solve this problem, the remaining processing capacity of broken machine is applied to weaken the impacts of machine breakdowns and a multi-objective evolutionary algorithm (NG-WOA) is proposed, which integrating NSGA-II and whale optimal algorithm (WOA). Furthermore, the joining of variable neighborhood search makes NG-WOA skip the local optimum solution in time. Through experiments, the validity of remaining processing capacity is verified in rescheduling scheme. Then NG-WOA algorithm is proven can find better solutions when compared with other two algorithms according to the distribution of pareto forefront solutions in benchmark examples. The results of this paper show that the remaining processing capacity has greater advantages in guaranteeing the stability of dynamic scheduling problems, because the machine’s processing capacity can be utilized fully compared to previous strategies for fault disturbances.

Trajectory-based synthesis of a slider-crank mechanism for applications in inertial vibration exciters

Slider-crank mechanisms are widely used in various industrial and technological machines. This paper considers a generalized diagram of a slider-crank mechanism, on the connecting rod of which an imbalanced mass can be fixed. In such a case, the slider-crank mechanism can be employed as an inertial vibration exciter. The aim of this research is to justify the geometric parameters of the mechanism to ensure a predetermined elliptical trajectory of the imbalanced mass motion. The research methodology involves the analytical derivation of the motion equations for a connecting rod point and solving the problem of synthesizing the geometric parameters of the mechanism based on the given trajectory of this point. The obtained results are presented in the form of displacements and trajectories for the connecting rod point of a specific slider-crank mechanism. The major novelty of this research lies in the further development of the theory of slider-crank mechanism synthesis for use in inertial vibration exciters. The derived analytical dependencies can be utilized by designers and engineers in the development of new types of vibration exciters for various industrial and technological vibratory equipment.

Simulation and Experimental Study of Ultrasonic Vibratory Grinding of Internal Splines

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.

Oxyanions Enhancing Crystallinity of Reconstructed Phase for Oxygen Evolution Reaction.

The catalysts were always undergoing continuous amorphization and dissolution of active structure in operating condition, hindering the compatibility between stability and activity for oxygen evolution reaction (OER). Herein, we propose the selective adsorption of leached NO3- to strengthen the crystallinity and activity of surface reconstructed layer with amorphous and crystalline (a-c) heterojunction. Taking a-c Ni doped Fe2O(OH)3NO3·H2O (Ni-FeNH) as a model precatalyst, we uncover that the leached NO3- are readily adsorbs on the crystalline phase in the formed a-c Fe(Ni)OOH during OER, lowering the disorder degree and further activating Ni and Fe ion of the crystalline Fe(Ni)OOH on a-c heterojunctions. Accordingly, Ni-FeNH deliver a low overpotential of 303 mV and high durability of 500 hours at 500 mA cm-2 for OER. Particularly, constructing industrial water electrolysis equipment exhibits high stability of 100 hours under a high operating current of 8000 mA.

Oxyanions Enhancing Crystallinity of Reconstructed Phase for Oxygen Evolution Reaction

The catalysts were always undergoing continuous amorphization and dissolution of active structure in operating condition, hindering the compatibility between stability and activity for oxygen evolution reaction (OER). Herein, we propose the selective adsorption of leached NO3‐ to strengthen the crystallinity and activity of surface reconstructed layer with amorphous and crystalline (a‐c) heterojunction. Taking a‐c Ni doped Fe2O(OH)3NO3·H2O (Ni‐FeNH) as a model precatalyst, we uncover that the leached NO3‐ are readily adsorbs on the crystalline phase in the formed a‐c Fe(Ni)OOH during OER, lowering the disorder degree and further activating Ni and Fe ion of the crystalline Fe(Ni)OOH on a‐c heterojunctions. Accordingly, Ni‐FeNH deliver a low overpotential of 303 mV and high durability of 500 hours at 500 mA cm‐2 for OER. Particularly, constructing industrial water electrolysis equipment exhibits high stability of 100 hours under a high operating current of 8000 mA.

Efficient Fault Warning Model Using Improved Red Deer Algorithm and Attention-Enhanced Bidirectional Long Short-Term Memory Network

The rapid advancement of industrial processes makes ensuring the stability of industrial equipment a critical factor in improving production efficiency and safeguarding operational safety. Fault warning systems, as a key technological means to enhance equipment stability, are increasingly gaining attention across industries. However, as equipment structures and functions become increasingly complex, traditional fault warning methods face challenges such as limited prediction accuracy and difficulties in meeting real-time requirements. To address these challenges, this paper proposes an innovative hybrid fault warning method. The proposed approach integrates a multi-strategy improved red deer optimization algorithm (MIRDA), attention mechanism, and bidirectional long short-term memory network (BiLSTM). Firstly, the red deer optimization algorithm (RDA) is enhanced through improvements in population initialization strategy, adaptive optimal guidance strategy, chaos regulation factor, and double-sided mirror reflection theory, thereby enhancing its optimization performance. Subsequently, the MIRDA is employed to optimize the hyperparameters of the BiLSTM model incorporating an attention mechanism. A predictive model is then constructed based on the optimized Attention-BiLSTM, which, combined with a sliding window approach, provides robust support for fault threshold identification. The proposed algorithm’s efficacy is demonstrated through its application to real-world gas-fired power plant equipment fault cases. Comparative analyses with other advanced algorithms reveal its superior robustness and accuracy in efficiently issuing fault warnings. This research not only provides a more reliable safeguard for the stable operation of industrial equipment but also pioneers a new avenue for the application of metaheuristic algorithms.

Modernizing industrial equipment: a case study of retrofitting a vertical lathe

Modernizing industrial equipment: a case study of retrofitting a vertical lathe

A fault diagnosis method for bearings and gears in rotating machinery based on data fusion and transfer learning

Abstract Rotating machinery is a crucial component of industrial equipment, and the fault diagnosis of bearings and gears, as vital elements of rotating machinery, is essential since they often fail under harsh working conditions, leading to significant property losses and serious personal safety problems. However, fault data for gears and bearings are often sparse in actual condition, and it is a challenge to ensure the reliability and stability of fault diagnosis results by extracting the features of a single data. To solve the above problems, this paper proposes a fault diagnosis method that combines Transfer Learning and data fusion techniques. Firstly, in this method, two kinds of fault signals are transformed into Gramian Angular Difference Fields and Recurrence Plot. Next, a U-shaped feature fusion dual discriminator generative adversarial network is used to fuse two-dimensional images from multiple sensor data. Its feature fusion module deeply integrates the features of the two images, thereby solving the impact of single data on the reliability and stability of fault diagnosis. Moreover, open-source datasets are used for Transfer Learning training to tackle the small sample problem. Finally, a decision-level information fusion classifier, the Dual-Branch Dempster-Shafer Classifier (DB-DSC), classifies the fused images. This classifier incorporates an improved soft threshold function and D-S evidence theory to achieve adaptive gradient changes and improve the robustness and accuracy of classification results. The experimental results show the effectiveness and stability of the proposed method, and the generated images get high score in several metrics. The average classification accuracy of the classification network reaches 93% and 92.5% on the two datasets, Therefore, the proposed method exhibits strong fault diagnosis capabilities under the small sample conditions of bearings and gears.

Remaining Useful Life Prediction Method Based on Dual-Path Interaction Network with Multiscale Feature Fusion and Dynamic Weight Adaptation

In fields such as manufacturing and aerospace, remaining useful life (RUL) prediction estimates the failure time of high-value assets like industrial equipment and aircraft engines by analyzing time series data collected from various sensors, enabling more effective predictive maintenance. However, significant temporal diversity and operational complexity during equipment operation make it difficult for traditional single-scale, single-dimensional feature extraction methods to effectively capture complex temporal dependencies and multi-dimensional feature interactions. To address this issue, we propose a Dual-Path Interaction Network, integrating the Multiscale Temporal-Feature Convolution Fusion Module (MTF-CFM) and the Dynamic Weight Adaptation Module (DWAM). This approach adaptively extracts information across different temporal and feature scales, enabling effective interaction of multi-dimensional information. Using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset for comprehensive performance evaluation, our method achieved RMSE values of 0.0969, 0.1316, 0.086, and 0.1148; MAPE values of 9.72%, 14.51%, 8.04%, and 11.27%; and Score results of 59.93, 209.39, 67.56, and 215.35 across four different data categories. Furthermore, the MTF-CFM module demonstrated an average improvement of 7.12%, 10.62%, and 7.21% in RMSE, MAPE, and Score across multiple baseline models. These results validate the effectiveness and potential of the proposed model in improving the accuracy and robustness of RUL prediction.

Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity

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.

Stiffness Hardening Effect of Wire Rope Isolators under Small Cyclic Loads for Vibration Isolation

Wire rope isolator (WRI) devices are widely used in vibration reduction industrial equipment, and stiffness is the key parameter that determines isolation effectiveness. WRI devices show slight nonlinearity under small loads, and the manufacturers generally only provide the initial parameters. To investigate the mechanical behavior changes in the WRI devices under repeated loads, five types of WRI specimens were tested under various amplitudes, loading speeds, and preloads. The test results of large symmetrical compression and tension loads showed that the WRI devices demonstrated stable hysteresis curves under repeated loads, while the hysteresis curves were independent of the loading speed. The test results of small cyclic loads with large preloads show that the stiffness of the WRI specimen follows the logarithmic law, with the cycle number under various loading conditions. Particularly, the stiffness of the specimen increases by about 10–30% after 50 cycles. The initial stiffness Ka decreases linearly with the preloads, while the decrease is quadratic in relation to the cyclic load. The hardening coefficient Ca shows a positive correlation with the loading capacity of the WRI devices, while it shows a negative correlation with the preload and cyclic load amplitudes. It is recommended to consider the stiffness increase in the WRI devices during the evaluation of isolation effectiveness.

An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks

The precise estimation of the operational lifespan of insulated gate bipolar transistors (IGBT) holds paramount significance for ensuring the efficient and uncompromised safety of industrial equipment. However, numerous methodologies and models currently employed for this purpose often fall short of delivering highly accurate predictions. The analytical approach that combines the Pattern Optimization Algorithm (POA) with Successive Variational Mode Decomposition (SVMD) and Bidirectional Long Short-term Memory (BiLSTM) network is introduced. Firstly, SVMD is employed as an unsupervised feature learning method to partition the data into intrinsic modal functions (IMFs), which are used to eliminate noise and preserve the essential signal. Secondly, the BiLSTM network is integrated for supervised learning purposes, enabling the prediction of the decomposed sequence. Additionally, the hyperparameters of BiLSTM and the penalty coefficients of SVMD are optimized utilizing the POA technique. Subsequently, the various modal functions are predicted utilizing the trained prediction model, and the individual mode predictions are subsequently aggregated to yield the model’s definitive final life prediction. Through case studies involving IGBT aging datasets, the optimal prediction model was formulated and its lifespan prediction capability was validated. The superiority of the proposed method is demonstrated by comparing it with benchmark models and other state-of-the-art methods.

Research on the Predictive Ability of Economic Policy Uncertainty on Stock Returns of Industries

In recent years, the emergence of economic policy uncertainty has had a certain impact on stock returns. It can be used as a research object to explore whether economic policy uncertainty can significantly predict future stock returns. This paper studies the predictive ability of economic policy uncertainty based on relevant data from major industries in the United States from January 1985 to December 2023, including Food industry (Food), Retail Stores industry (Rtail), Fabricated Products industry (FabPr), Drugs, Soap, Perfumes, Tobacco industry (Cnsum), Construction and Construction Materials industry (Cnstr), Consumer Durables industry (Durbl), Automobiles industry (Cars), Textiles, Apparel & Footwear industry (Clths), Machinery and Business Equipment industry (Machn), Chemicals industry (Chems), and Transportation industry(Trans), Utilities industry (Utils), Steel Works Etc industry (Steel), Mining and Minerals industry(Mines), Oil and Petroleum Products industry (Oil), and Banks, Insurance Companies, and Other Financials industry (Finan). Through both in-sample and out-of-sample examines, it is found that economic policy uncertainty is a reliable indicator for predicting stock returns, and its predictive performance is better in Food industry, Retail Stores industry, Fabricated Products industry, Transportation industry, Construction and Construction Materials industry, Consumer Durables industry, Automobiles industry, Textiles, Apparel & Footwear industry, Machinery and Business Equipment industry, Chemicals industry, and Drugs, Soap, Perfumes, Tobacco industry than in Utilities industry, Steel Works Etc industry, Mining and Minerals industry, Oil and Petroleum Products industry, and Banks, Insurance Companies, and Other Financials industry. Meanwhile, by replacing the measurement indicator of economic policy uncertainty, conducting long-horizon forecasting, and using different evaluation periods for robustness checks, it is found that the research conclusions are robust.

Research on Multi-Sensor Fusion Fault Diagnosis Method Based on Spatiotemporal Attention Mechanism

As industrial systems become increasingly complex, real-time monitoring and intelligent management of industrial equipment have become imperative. However, the limitations in coverage and accuracy of single sensors make it challenging to comprehensively characterize the operational state of equipment, leading to reduced system reliability and increased pressures on data transmission and storage. To address these challenges, this study presents a novel fault diagnosis method based on multi-sensor fusion using a spatio-temporal attention mechanism. Initially, one-dimensional convolutional neural networks (1D-CNN) are employed to extract features from raw signals, effectively capturing local characteristics and ensuring the integrity and validity of fault signals. Subsequently, the spatiotemporal attention mechanism adjusts the feature weights based on the temporal and spatial correlations of different sensors, as well as their respective importance, thereby capturing the spatio-temporal dependencies across multiple sensors and enhancing the efficacy of information fusion. Finally, the proposed method is validated through experiments on a nickel flash smelting furnace system. The results demonstrate that the method achieves a fault diagnosis accuracy exceeding 97.78%, significantly enhancing fault detection and decision-making performance.

Essential oils application as Bacillus cereus antispore agent in food hygiene

Adherence of Bacillus cereus spores to dairy industry equipment is a serious problem, causing food spoilage and, occasionally, outbreaks of foodborne diseases. Antimicrobial essential oils (EOs) have been proposed as an alternative to synthetic disinfectants due to their environmentally friendly properties. The aim of this study was to assess the effect of Cymbopogon winterianus and Juniperus phoenicea EOs on planktonic and adherent B. cereus spores to stainless steel surfaces. Eleven strains of B. cereus were used in this study. The chemical composition of the C. winterianus and J. phoenicea oils was analyzed by gas chromatography (GC)/mass spectrometry (MS). Ninety components were identified for C. winterianus, being geranial (37 %), neral (29 %), beta-myrcene (16 %) and geraniol (4 %) the most abundant constituents. Twelve components were identified for J. phoenicea, being alpha-pinene (65 %) the most abundant constituent. The results of the minimal inhibitory concentration and minimal bactericidal concentration revealed that C. winterianus was the most effective EO (p < 0.05) against all eleven strains. In addition, there was a large log10 reduction (from 4.93 to 2.42; p < 0.05) between spores in planktonic form versus spores adhered to stainless steel surface. C. winterianus can be used for effective detachment of B. cereus spores capable to adhere and form biofilms on stainless steel surfaces, being a valid alternative for the development of natural disinfectant that may reduce negative effects of synthetic chemical disinfectants.

Damage to Special-Purpose Ring Wavy Springs under the Influence of Hydrogen Sulfide-Containing Media

Annular wavy springs are widely used in the oil and gas industry and most often serve either as compensators for temperature deformations or for mechanical seals of various joints in equipment transporting oil and gas media. These springs provide high load capacity with a relatively low draft, which ensures a compact design. Most often, such springs are made by stamping from sheet steel. When operating technological equipment that transport hydrogen sulfide-containing oil and gas media, hydrogen sulfide stress corrosion cracking occurs quite often, which can lead to failure of industrial equipment. In particular, damage to annular wavy springs can cause depressurization of elements of industrial equipment and cause significant harm not only to the environment, but also to maintenance personnel. The object of research was annular wavy springs made of steel type X15N7M2YU. The purpose of the study is to determine the resistance of the spring material to stress corrosion cracking. This article discusses the test results of special purpose annular wavy springs in an aggressive hydrogen sulfide-containing medium artificially prepared according to the recommendations of ANSI/NACE TM 0177. The results of hardness studies, strength calculations and metallographic studies of damage to a wavy spring are presented. The chemical composition of the metal of the spring fully corresponds to the steel type X15N7M2YU declared by the enterprise. According to the studies conducted in environments containing hydrogen sulfide, the wavy spring is not recommended for use in oil and gas equipment, since it is not resistant to hydrogen sulfide cracking under stress.

Modern Prospects for the Application of Mechatronics and Robotics Capabilities

This article explores the application of mechatronics and robotics capabilities. In recent years a new quickly developing scientific and technical activity was created. It is called Mechatronics and represents an innovative field of science and equipment where the high-precision mechanics connects to the same exact electronics under control of the most modern computer programs [1],[2]. As a result difficult mechanisms of computer motion control with tremendous functionality are created. Industrial robots are striking examples of such mechanisms. Besides, there are mechanisms of retracting plane flaps and the chassis, many systems of a modern car and "smart houses". Technical and technological progress is impossible without mechatronics [3],[4]. Robotics and industrial automatic equipment in various branches of economy, automation of technological processes, systems of planning and business management, automation of transport, dispatching and other systems falls within the scope of its interests. Nowadays “Mechatronics” is one of the most demanded educational majors in the world. Mechatronics specialists are demanded in different areas: in power engineering, machine-tool design and production of the equipment, automation of technological processes, robotics and automotive industry. Security systems, military equipment, the medical equipment can't be designed without mechatronics.

The Method of Diversity-Based Ensuring the Reliability of the Router in the IIoT System

The architecture of the Industrial Internet of Things (IIoT) system assumes the presence of a router as the main subsystem, which will allow connecting the subsystems into a single network and connecting them to the equipment of the Internet service provider. Failure of the router can lead to downtime of industrial equipment and the whole workshop at the industrial enterprise, and to the failure of the IIoT system as a whole. Control processing units of industrial routers (RCPU) are complex microprocessor systems (MS). Violation of their operability can be caused by both hardware (HW) and software (SW) failures. Therefore, to increase the reliability of RCPU it is reasonable to use such methods of reliability assurance as HW redundancy (duplication of processors with comparison of results), as well as the use of modern means of control and diagnostics. However, the number of HW and SW failures in RCPUs, is still quite high. In this connection, the solution of issues related to the development of methods for ensuring high reliability of RCPU is of great urgency. This paper proposes a method of ensuring the reliability of RCPU using SW diversity on the basis of priority series. The method let to choose the variant of redundancy of HW and the number of versions of SW as diversity in RCPU.