Industrial Equipment Research Articles

Optimalization of strategic management to enhancing CEO’s transformational leadership

Globalization poses a challenge to the international community due to its infinite scope. The fourth industrial revolution, often known as Industrial Revolution 4.0, is either already underway or is coming quickly. In this revolution, previously unheard-of levels of connectedness and communication are made possible by the integration of industrial equipment with the internet and information and communication technology (ICT). Building a cohesive team was a key priority for transformational leaders to effect positive change. They would encourage improvements for both their staff and the system. Transformational leaders support employees' performance improvement to attain a professional level by rewarding them, whereas transactional leaders reward the company's profits. The purpose of this research was to determine how best to use strategic management to support the transformational leadership of the CEO. This study combined a literature study methodology with a qualitative approach. The study's findings indicated that, in order for a leader to practice transformational leadership, they must build a stronger bond with their followers. Through an applicable strategic management process, leaders must enable followers to see their interests as being more significant than their own to further the goals and objectives of the group or organization. best possible.

Design of hydraulic lift system of technical equipment working in amusement industry

This paper examines the design and application of a telescopic hydraulic system for amusement industry equipment that experiences cyclic passenger loads. The system comprises three hydraulic cylinders, each with different diameters, which activate at various points as the device moves. To achieve static balance and lift the main beam from a horizontal to a vertical position, the forces exerted by the cylinders were calculated using static equilibrium equations. The system's design, including the length and angle of each cylinder, was based on the technical specifications of the equipment. Analytical and numerical methods were used to ensure the correct sizing of the cylinders and the system's overall effectiveness, resulting in a structure that operates safely and efficiently.

Permutation Entropy: An Ordinal Pattern-Based Resilience Indicator for Industrial Equipment

In a highly dynamic and complex environment where risks and uncertainties are inevitable, the ability of a system to quickly recover from disturbances and maintain optimal performance is crucial for ensuring operational continuity and efficiency. In this context, resilience has become an increasingly important topic in the field of engineering and the management of productive systems. However, there is no single quantitative indicator of resilience that allows for the measurement of this characteristic in a productive system. This study proposes the use of permutation entropy of ordinal patterns in time series as an indicator of resilience in industrial equipment and systems. Based on the definition of resilience, the developed method enables precise and efficient assessment of a system’s ability to withstand and recover from disturbances. The methodology includes the identification of ordinal patterns and their analysis through the calculation of a permutation entropy indicator to characterize the dynamics of industrial systems. Case studies are presented and the results are compared with other resilience models existing in the literature, aiming to demonstrate the effectiveness of the proposed approach. The results are promising and highlight a highly applicable and simple indicator for resilience in industrial systems.

Calculation model of enjoyment of dust particles by swirking flow in gas flowes

An analysis of the reasons for the formation of deposits in the flue ducts of aspiration systems was carried out. Problems have been noted in removing deposits with compressed air. The purpose of the work is to solve the problems of aspiration systems and reduce the formation of dust deposits in gas ducts. This is possible due to the swirling of the compressed air flow when purging the gas duct. Despite significant pressure losses when organizing flow swirl, compressed air consumption is reduced by 20–40%. A computational model of the entrainment of dust particles by a swirling flow in gas ducts is presented. To describe the physical model of the movement of a dust particle in a swirling gas flow flowing in a gas duct with a circular cross-section, polar coordinates are used. Dependencies have been deter-mined to determine the value of the average axial velocity of the swirling flow required to move a particle along the gas duct. A diagram of the coordinate axes and forces acting on the particle is presented. The forces acting on the dust particle and its connections with the inner surface of the flue are replaced by the reaction of the flue wall. By decomposing the wall reaction into tangential and normal components, differential equations were obtained that describe the motion of the particle. The dust particle is significantly influenced by the forces of resistance to flow around the gas flow Fμ, the friction force on the wall of the gas duct Ffr, the weight of the particle P, the normal reaction of the wall of the gas duct N. Analysis of the fractional composition of various dusts contained in gases aspirated from the equipment of the metallurgical industry and the production of electrode materials showed that For these dusts, the optimal flow is a weak swirl of intensity W*= 0.5–0.8. This swirling intensity can be achieved by introducing a flow of compressed air when blowing at an angle of 36–48°.

Diagnostics-LLaVA

The recent advancements in the area of Large language models (LLMs) has opened horizons for conversational assistant-based intelligent models capable of interpreting images, and providing textual response, also known as Visual language models (VLMs). These models can assist equipment operators and maintenance technicians in the complex Prognostics and Health Management (PHM) tasks such as diagnostics of faults, root cause analysis and repair recommendation. Significant open source contributions in the area of VLMs have been made. However, models trained on general data fail to perform well in complex tasks in specialized domains such as diagnostics and repair of industrial equipment. Therefore, in this paper we discuss our work on development of Diagnostics-LLaVA, a VLM suitable for interpreting images of specific industrial equipment and provide better response than existing open source models in PHM tasks such as fault diagnostics and repair recommendation. We introduce Diagnostics-LLaVA based on the architecture of LLaVA, and created one instance of the Diagnostics-LLaVA for the automotive repair domain, referred to as Automotive-LLaVA. We demonstrate that our proposed Automotive-LLaVA model performs better than the state-of-the-art open source visual language models such as mPlugOWL and LLaVA in both qualitative and quantitative experiments.

Intelligent Fault Diagnosis Method Based on Neural Network Compression for Rolling Bearings

Rolling bearings are often exposed to high speeds and pressures, leading to the symmetry in their rotating structure being disrupted, which can lead to serious failures. Intelligent rolling bearing fault diagnosis is a critical part of ensuring operation of machinery, and it has been facilitated by the growing popularity of convolutional neural networks (CNNs). The outstanding performance of fault diagnosis CNNs results from complex and redundant network structures and parameters, resulting in huge storage and computational requirements, which makes it challenging to implement these models in resource-limited industrial devices. This study aims to address this problem by proposing a comprehensive compression method for CNNs that is applied to intelligent fault diagnosis. It involves several different compression methods, including tensor train decomposition, parameter quantization, and knowledge distillation for deep network compression. This results in a significant decrease in redundancy and speeding up the training of CNN models. Firstly, tensor train decomposition is applied to reduce redundant connections in both convolutional and fully connected layers. The next step is to perform parameter quantization to minimize the bits needed for parameter representation and storage. Finally, knowledge distillation is used to restore accuracy to the compressed model. The effectiveness of the proposed approach is confirmed by an experiment and ablation study with different models on several datasets. The results show that it can significantly reduce redundant information and floating-point operations with little degradation in accuracy. Notably, on the CWRU dataset, with about 60% parameter reduction, there is no degradation in our model’s accuracy. The proposed approach is a new attempt at the intelligent fault diagnosis of rolling bearings in industrial equipment.

Experimental study on flow boiling heat transfer of high-pressure water in a mini-tube

With the miniaturization of industrial equipment, cooling methods in microchannels has been more important in recent years. However, studies focusing on high-pressure-water flow boiling in mini-channels under are rare. Flow boiling of high-pressure water has a high cooling potential due to the properties of water. In this paper, flow boiling heat transfer experiments were carried out in a tube of 1 mm diameter. The maximum heat transfer coefficient is as high as ∼350 kW/(m2·K). Heat transfer coefficient decreases with mass flux, and increases with heat flux and inlet pressure, indicating the heat transfer mechanism is nucleate boiling. Phenomenological models in the literature were utilized to analyze the flow pattern. Slug flow and churn flow, in which bubbles are present in the liquid phase, appear in this paper. A new correlation was proposed, which has better accuracy when predicting the experimental results.

Vibration source inversion-based fault diagnosis: approach and application

The gear transmission system of industrial equipment is characterized by a highly integrated structure, non-stationary operating conditions, and large loads. Therefore, the attenuation of fault signal characteristics and the complexity of operating conditions in gear transmission systems significantly affect the effectiveness of vibration signal-based fault diagnosis. This paper proposes a unified diagnostic framework based on vibration source inversion for two typical transmission component fault signal phenomena: meshing frequency modulation sidebands and equally spaced fault characteristic frequency interval harmonic clusters. This approach incorporates the vibration transfer path analysis of passive subsystems and identifies bearing dynamic forces, followed by their decomposition, reconstruction, and sensitive frequency band localization. It enables accurate diagnosis of complex gear transmission systems under non-stationary conditions with limited vibration testing. The effectiveness and advantages of this method over existing mainstream signal processing techniques are validated through actual cases of gear and weak bearing fault diagnosis on a complex planetary gear transmission system test bench.

Identification of incipient cavitation in control valve

Control valves as important elements in hydraulic systems are used to transport liquid and gaseous media, for example, water, water vapor, and hydrogen. They are mainly used in the power engineering, chemical industry, and so forth. Due to their large dimensions, the verification of hydraulic parameters is problematic. In the case of liquid flow, cavitation can occur, which is mainly characterized by noise, vibrations, or changes of hydraulic parameters. During the incipient cavitation in the valve, there are no noticeable changes in the hydraulic characteristics. The article, therefore, deals with the methodology of determining the cavitation by measuring and evaluating the characteristics of the valve, spectral analysis of noise, and vibrations during water flow. Mathematical modeling is also a suitable tool for determining the extent of cavitation in valve design. Newly created modified cavitation model, where the flowing medium is defined as a mixture of incompressible water and compressible gases (vapor and air), better corresponds to physical principles than the two-phase approach (water and vapor) with experimentally modified density and viscosity. Measured noise and vibration frequency characteristics and mathematically modeled pressure frequency characteristics identify cavitation in the (1 to 10) kHz range. The article proves that when identifying cavitation, the method of measuring hydraulic characteristics fails, but the method of measuring noise and vibrations is suitable in practice, i.e., in real industrial equipment, and the modified cavitation model is suitable for identifying cavitation regions in structural designs of the hydraulic elements.

DIAGNOSIS OF ROLLING BEARING DEFECTS BASED ON WAVELET ANALYSIS

Purpose. Development and improvement of a method for analyzing vibration signals from rolling bearings based on wavelet analysis for the detection and identification of equipment defects. Research methods. Wavelet analysis was employed for processing vibration signals from rolling bearings. Threshold wavelet filtering was applied to highlight weak impulse components in the signals, and Morlet wavelet was used to ensure effective filtration. Results. The research results indicate that the proposed method, utilizing wavelet filtering, enhances the speed and reliability of vibration diagnostics for bearings. This allows for the efficient extraction of characteristic frequencies associated with various types of rolling bearing defects. In comparison with other signal analysis methods, the use of the developed method based on continuous wavelet analysis has proven to be particularly effective in extracting characteristic diagnostic frequencies. This method not only allows for the identification of specific types of defects in rolling bearings but also ensures universality, enabling its successful application for analyzing other types of non-stationary signals. Experimental studies have confirmed the high efficiency of the developed method, especially in the early stages of defect development. The application of this method is evident not only in its ability to effectively highlight the characteristic frequencies of bearings but also in its capacity to conduct signal analysis for the identification of equipment defects as a whole. This makes the proposed method a promising and versatile tool for the diagnosis and monitoring of the condition of technical systems. Scientific novelty. Application of the proposed method for processing vibration signals from rolling bearings based on wavelet analysis to improve the effectiveness of defect detection and identification in equipment. Practical value. The developed method can be applied in the industrial sector for the analysis and diagnostics of rolling bearings in equipment. It enables the timely detection of defects, reduces the risk of equipment failure, and lowers operational maintenance costs. Thus, this method has practical value in enhancing the reliability and productivity of industrial equipment.

Innovative Electrostatic Precipitator Solutions for Efficient Removal of Fine Particulate Matter: Enhancing Performance and Energy Efficiency

The removal of particulate matter (PM) from air streams is essential for advancing environmental technologies and safeguarding public health. This study explores the performance of an electrostatic precipitator (ESP) in eliminating fine and ultra-fine PM under varied experimental conditions. It uniquely examines the influence of PM size and feed rate on ESP removal efficiency. The system’s use of low voltages enhances energy sustainability, while its innovative design improves corona discharge, leading to significant reductions in fine and ultra-fine PM emissions. Plants using electrical devices are increasingly being incorporated into material processing lines to reduce pollution in the surrounding work area, as well as to collect particle emissions in the atmosphere. It is also possible to recycle some raw materials in this way with low energy consumption. This cleaning technology increases the added value of industrial equipment, which affects its competitiveness and its impact on sustainable manufacturing. The experimental results indicate a steady electrostatic field voltage of 15.1 kilovolts, with an airflow maintained at 0.8 m/s through a doser at 2.5 bar, eliminating the need for a fan. The PM feed rate varied between 2 and 20 mm/h, with six trials conducted to ensure the data were consistent. Preliminary studies devoid of ESP intervention demonstrated little PM removal, since buildup on the chamber walls distorted the results. The installation of the ESF markedly enhanced the removal efficiency, achieving up to 95.5%. Further analysis revealed that ESP performance depended on PM concentration in the agglomeration chamber, achieving a clearance rate exceeding 98% under optimal conditions. Fine PM (0.35 to 8.7 µm) was more efficiently removed than ultra-fine PM (0.2 to 0.35 µm). The highest removal efficiency was observed at a feed rate of 0.962 mg/s, while the lowest occurred at 0.385 mg/s. A strong positive correlation between particle concentration and removal efficiency (Pearson value up to 0.829) was observed, particularly at feed rates of 0.128, 0.641, and 1.283 mg/s. The study’s findings confirm that the ESP is highly effective in removing particulate matter, particularly fine and ultra-fine particles, with an optimal feed rate, significantly enhancing the system’s performance.

Parameter-coupled state space models based on quasi-Gaussian fuzzy approximation

The accuracy of a fuzzy system’s approximation is closely tied to the performance of fuzzy control systems design, while this system’s interpretability depends on the description of a mechanical model using human language. This research introduces a quasi-Gaussian membership function characterized by a pair of parameters to achieve the sensitivity of a triangular membership function along with the interpretability of Gaussian membership functions. Consequently, a two-dimensional (2-D) quasi-Gaussian membership function is derived, and a method for establishing quasi-Gaussian fuzzy systems (QGFS) using a rectangular grid is proposed. After validating the approximation properties using the sine function for the one-dimensional (1-D) and 2-D QGFS, the systems are applied to approximate the depyrogenation tunnel, a significant piece of equipment in the pharmaceutical industry with various mechanical designs. Validation results indicate that the 1-D and 2-D QGFS can achieve an approximation error varying within a ± 5% range. Meanwhile, the 1-D and 2-D QGFSs are applied to mechanical models of the depyrogenation tunnel with satisfactory final approximation results. Lastly, the 2-D QGFS is capable of demonstrating an excellent description of models with coupled parameters.

Comparison of Machine Learning Models and Feature Importance Investigation of Intelligent Fault Diagnosis Methods for Robots Based on Datasets Across Various Distributions

Intelligent fault detection is an important component of the industrial and automation fields. However, conventional research on intelligent fault detection mainly focuses on industrial production equipment, while there is little research on intelligent fault detection scenarios for robots. Based on a hexapod robot joints dataset, this paper investigates intelligent fault diagnosis tasks in the field of robotics. Firstly, the dataset is clustered into two new datasets with different distributions through k-means method, which is used to simulate the practicality of imbalanced distribution between healthy and fault data. Afterwards, several multioutput machine learning classification models were established to predict robot joints with faults. In two datasets with different distributions, the larger one is used as the training set and the smaller one is used as the testing set. The article compares the performance of these models with prediction accuracy as the main indicator. And based on the results, the paper selects the model with the highest accuracy for further exploration of feature importance. Finally, the article explains the significance of the results and analyzes possible reasons. The experimental results show that the random forest model better overcomes the problem of data distribution differences and has the highest accuracy. In the random forest model, the importance of position data representing position error is higher than that of slope with respect to the axis data representing angle error. This result may be related to the distribution of feature values and the fact that position data contains more crucial information.

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.