Maintenance Of Machinery Research Articles

Remaining useful life prediction of rolling bearings using a residual attention network with multi-scale feature extraction and temporal dependency enhancement

ABSTRACT Predicting the remaining useful life (RUL) of rolling bearings is crucial for industrial machinery maintenance, non-destructive testing and evaluation (NDT). To address the challenges posed by noise interference and redundant information, this paper proposes a novel approach utilising residual attention networks and multi-scale feature extraction. The method enhances feature extraction by combining shallow and deep convolutional layers while employing bidirectional LSTM to capture both short-term and long-term dependencies in time series data. The incorporation of a residual attention fusion module further enhances the model’s ability to focus on important features, ensuring more stable training and better prediction performance. After validation on PHM2012, IMS, and laboratory self-constructed datasets, the proposed model demonstrates superior performance compared to existing methods, significantly reducing prediction errors. The practical application of this work lies in its potential integration into industrial predictive maintenance systems, providing a solid foundation for expanding predictive maintenance strategies in complex real-world industrial environments.

OPTIMIZATION OF USED ENGINE OIL AS ADMIXTURE IN CONCRETE USING RESPONSE SURFACE METHODOLOGY

Maintenance of industrial machinery and automobile engines produces a large amount of Used Engine Oil (UEO) as waste. This waste oil is harmful to the environment, pollutes the land and kills the aquatic habitats. Used engine oil poured into household drains or directly onto the field will pollute fresh water by entering rivers and groundwater. Efforts are shifted to conversion of Used Engine Oil into useful material for concrete production as a replacement for SP430 superplasticizer. This research deals with optimizing Used Engine Oil in M20 grade concrete using response surface methodology. The oxide composition of UEO revealed 37% of SO3, 15.5% of CaO, 17.7% OF ZnO, 9% of P2O5. The research revealed that the workability of the concrete mixes were between medium and high. The control mix slump value was 50mm but when UEO and superplasticizer were used, the slump obtained were 54mm and 52mm respectively. Concrete samples were produced and strength properties were tested at 7, 14 and 28 days of curing by water immersion. The test result showed that water absorption decreased with increase in UEO. Compressive strength increased with increase in dosage and curing days. Also, there was gradual increase in flexural and splitting tensile tests with the increase in UEO. Model equations and optimal values of UEO concrete were generated by numerical optimization. The optimal value was 11.4ml of UEO at 28 days which gave compressive strength result of 26.7 N/mm2 , flexural strength 8.3N/mm2 , tensile strength 18.9 N/mm2 and water absorption of 0.7%. Therefore, 11.4ml of UEO is recommended for use as superplasticizer in M20 grade concrete.

Using a 3D system for control of parts during repairs

The development of scientifi c hypotheses and approaches to improving the strategy for maintenance and repair of agricultural machinery is one of the current areas in the agro-industrial complex, as it is of paramount importance in the technical support of the machine and tractor fl eet throughout the entire life cycle of machines. Research of methods and means of monitoring parts of automotive and tractor internal combustion engines during defect detection and repair with the development of an automated interactive system with software for monitoring these parameters, using the example of a connecting rod of the K-744 tractor engine.

Three-stage location-routing programming for heavy-duty railway maintenance machinery base based on multi-factor combined weights

Abstract An imperative shift towards a scientific and methodical approach has become increasingly essential when deliberating the placement of maintenance bases for robust railway machinery dedicated to heavy-duty tasks. This article addresses the need for a systematic approach to placing maintenance bases for heavy-duty railway machinery. It introduces a meticulously crafted three-stage location-routing paradigm that factors in both qualitative and quantitative determinants. This approach optimizes routing strategies for the machinery while constructing an evaluative framework for base placement. An evaluative index is created by merging relevant qualitative and quantitative elements, using an enhanced game theory methodology combined with the evidential analytic hierarchy process and the entropy weight method to balance subjective and objective considerations. The model’s complexity is handled with an improved genetic algorithm and a hybrid ant colony algorithm, effectively addressing location and routing interactions. Practical implementation and case studies validate the model’s and algorithm’s efficacy, confirming their applicability in real-world scenarios.

An enhanced deep intelligent model with feature fusion and ensemble learning for the fault diagnosis of rotating machinery

Vibration signals, serving as critical sources of information for monitoring the status of rotating machinery, demand effective extraction and rational utilization of its features to significantly enhance the accuracy and reliability of fault diagnosis. However, vibration signal features typically manifest as nonlinear and nonstationary, posing a significant challenge in industrial settings. To tackle this challenge, this article proposes an enhanced deep intelligent model based on feature fusion and ensemble learning for practical fault diagnosis of rotating machinery. First, a parallel network structure is introduced to comprehensively and accurately explore the fault characteristics of rotating machinery. This network comprises two branches: the first branch designs an improved one-dimensional convolutional neural network to extract locally robust features from raw signals; the second branch adopts variational mode decomposition to decompose raw signals into a set of intrinsic mode functions and extract comprehensive statistical features in both the time and frequency domains, significantly enhancing the signal representation capability. Subsequently, a deep neural network is used to extract more stable feature information. The features from the two branches are then fused, and the final network output is generated through a softmax regression function. Finally, ensemble learning uses a majority voting scheme to obtain more stable final outputs. To confirm the effectiveness of the proposed method, experiments are conducted on two laboratory cases and one industrial case. The experimental results demonstrate that the proposed method significantly improves fault diagnosis accuracy and reliability in controlled laboratory environments and real-world industrial applications, making it highly applicable for real-time monitoring and predictive maintenance of industrial machinery. These improvements can reduce maintenance costs and downtime, thus enhancing operational efficiency in various industrial settings.

Studying the Effectiveness of Using Intelligent Mining Machinery Systems on Health, Safety, and Environmental Parameters and Preventive Maintenance

Introduction: The goal of this study is to investigate how the development of technology has affected the industry (especially the mining industry). For this purpose, this paper examines the impact of intelligent mining machinery systems, including tire pressure monitoring systems (TPMS), dispatching systems, and vehicle health monitoring systems (VHMS), on health, safety, and environmental parameters and preventative maintenance. Material and Methods: This study is descriptive-analytical research that was conducted between time intervals before and after employing the intelligent mining machinery systems. Initially, parameters were identified using the Delphi method. These parameters include human accidents, equipment accidents, environmental incidents, warnings and fines in the domains of health, safety, and the environment, tire usage parameters, the shelf life of the tire, oil overfill, fuel consumption, failure rate, mean time between failures, and preventive maintenance compliance schedules in the domain of preventative maintenance. The effectiveness of using these systems was then assessed by comparing the state of the specified parameters before and after the introduction of the intelligent mining machinery systems. Results: The findings of this research indicate that using intelligent mining machinery systems will decrease equipment accidents by 33.3%, extend the useful life of tires by 7.1%, reduce fuel consumption by 14.6%, cut the mean time required to repair by 25.5%, and enhance preventive maintenance compliance schedules by 5.7%. The findings showed the effectiveness of the use of intelligent systems of mining machines was obtained as follows: reduction of equipment accidents by 33.3%, increasing the useful life of tires by 7.1%, reducing fuel consumption by 14.6%, reducing the average downtime of the car for repair by 25.5% and increasing compliance with the maintenance program by 5.7%. Conclusion: Utilizing intelligent mining machinery systems might have a positive impact on the safety of machines, reduce negative environmental effects like fuel consumption, and improve the maintenance of heavy machinery, which would lead to better mining conditions and lower costs.

High-Efficiency Enrichment of Metallic Particles in Lubricating Oil Based on Filter-Free Acoustic Manipulation Chip.

Enrichment of metal particles in lubricating oil is a crucial pretreatment for wear debris analyses in applications of condition-based machinery maintenance. Current techniques using physical filter cleaning and magnetic attachment to enrich metal particles have limitations in terms of efficiency and selectivity. This work presents an innovative acoustic manipulation chip for efficiently enriching metallic particles from lubricating oil. The platform utilizes the hybrid acoustic forces to perform high throughput particle enrichment in microchannels, even in an intensive flow environment. Regarding the viscosity effect of lubricating oil, the temperature dependence upon the particle enrichment is explored, and the figure of merit is employed to quantify the enrichment performance from the captured microscopic images. Experimental results demonstrate the proposed platform shows great nonselectivity for enriching both magnetic and nonmagnetic particles. This method opens a new door for developing automatic filter-free pretreatment tools to perform efficient particle enrichment in lubricating oil, which have great potential in many application scenarios, such as advanced wear debris analyses, oil quality monitoring, etc.

Comparison of Machine Learning Methods for Limited Predictive Maintenance

Predictive maintenance has gained increasing attention recently with the availability of sensors and connectivity of equipment. Yet, it would be difficult to obtain a wide range of data, especially with legacy devices. This paper describes an intelligent method for predicting a near future condition using the past information for an environment in which data are limited to the alarm logs from industrial machinery. Since machine learning methods are proven to be efficient in classification tasks using time series data, three of them are selected to predict an alarm two hours in advance using the past occurrences. These methods are neural networks, random forests, and extreme gradient boosting. The performances of these three methods are compared, and it is aimed to find the optimal configuration among hyper-parameter values. According to the obtained results, extreme gradient boosting gives the highest F1-score of 0.767 with number of trees equal to 500, maximum depth of 128, and an input window of alarm occurrences from the last day. This work consists of a comparative study aiming to identify the best machine learning method for alarm predictions, which potentially provides important insights into the operation and maintenance of machinery, bringing the possibility of considerable cost reductions.

Abstract 4122249: Deletion of macrophage dynamin-related protein 1 exacerbates left ventricular remodeling after myocardial infarction by impairing mitochondrial quality maintenance machinery

Background: Macrophage-mediated inflammation plays an important role in the healing process after myocardial infarction (MI). We have previously reported in macrophages that inhibition of dynamin-related protein 1 (Drp1), which induces mitochondrial fission, restrains skewing toward inflammatory phenotype. It is, however, obscure whether macrophage Drp1 promotes left ventricular (LV) remodeling after MI. Aims: The aim of this study is to elucidate the role of macrophage Drp1 in the mechanisms of LV remodeling after MI. Methods: C57Bl/6J mice specifically deficient Drp1 in Lysozyme-M + macrophages (Drp1KO) were created by Cre/loxP technology and underwent ligation of the left anterior descending coronary artery at 8 to 12 weeks of age. Results: Deletion of macrophage Drp1 decreased LV ejection fraction (38±14 vs. 23±9% at day 28. N=9, p<0.05) and increased LV diameter (LVDd/Ds 5.7±0.4/4.8±0.3 vs. 4.6±0.2/3.7±0.3mm at day 28, N=9, p<0.05) (Figure). Survival rates until 28 days after MI were significantly lower in Drp1KO mice (25 vs. 64%. N=24 and 33, p<0.05). Deletion of Drp1 led to sustained macrophage accumulation and fibrosis of infarcted tissues. TEM revealed that mitophagosomes are decreased in Drp1-deficient macrophages in infarcted hearts. In ex vivo cultured macrophages, siRNA-mediated knockdown of Drp1 impaired mitochondrial fission and LC3-dependent mitophagy. In these macrophages, inhibition of Drp1 by Mdivi-1 for 96 hr, but not for 24 hr, induced mitochondrial DNA (mtDNA) leakage to the cytosol accompanied by increased expression of inflammatory cytokines. Hypoxia and starvation, in addition to Mdivi-1, induced leakage of mtDNA and expression of inflammatory cytokines at 24 hr. These results suggest that impairment of mitochondrial quality maintenance machinery causes leakage of mtDNA that induces inflammation. Conclusions: Macrophage Drp1 protects the heart from sustained inflammation and adverse cardiac remodeling after MI. Drp1-LC3-mediated mitophagy could be a mechanism that maintains mitochondrial quality and prevents excessive inflammation after MI.

Analysis of methods of hardening and restoration of worn parts

The restoration of worn parts is an important aspect in the field of machine repair and maintenance of machinery. It allows you to significantly reduce repair costs and is a priority issue in the development of a machine maintenance and repair system. The main causes of performance loss products are abrasive wear, fatigue breakdowns, corrosion and other types of destruction. When choosing a recovery method, the type of coupling, types of friction and operating conditions of the machines are important factors. An increase in wear resistance can be achieved by changing the chemical composition of the material, mechanical influences and the application of wear-resistant coatings.

The histone chaperones ASF1 and HIRA are required for telomere length and 45S rDNA copy number homeostasis.

Genome stability is significantly influenced by the precise coordination of chromatin complexes that facilitate the loading and eviction of histones from chromatin during replication, transcription, and DNA repair processes. In this study, we investigate the role of the Arabidopsis H3 histone chaperones ANTI-SILENCING FUNCTION 1 (ASF1) and HISTONE REGULATOR A (HIRA) in the maintenance of telomeres and 45S rDNA loci, genomic sites that are particularly susceptible to changes in the chromatin structure. We find that both ASF1 and HIRA are essential for telomere length regulation, as telomeres are significantly shorter in asf1a1b and hira mutants. However, these shorter telomeres remain localized around the nucleolus and exhibit a comparable relative H3 occupancy to the wild type. In addition to regulating telomere length, ASF1 and HIRA contribute to silencing 45S rRNA genes and affect their copy number. Besides, ASF1 supports global heterochromatin maintenance. Our findings also indicate that ASF1 transiently binds to the TELOMERE REPEAT BINDING 1 protein and the N terminus of telomerase in vivo, suggesting a physical link between the ASF1 histone chaperone and the telomere maintenance machinery.

Selection Dominant Features Using Principal Component Analysis for Predictive Maintenance of Heave Engines

This article aims to identify the dominant features that have a significant impact on the health of a heavy machine that relates to the digital infrastructure of a company. The importance of this research is that the authors define predictive maintenance based on Principal Component Analysis (PCA), which is the novelty of this article. The novel contribution of this research lies in the application of Principal Component Analysis (PCA) for predictive maintenance of heavy machinery, which has not been integrated into the Scheduled Oil Sampling (SOS) procedures. The recorded data are called Scheduled Oil Sampling (SOS) and historical data from an equipment called CoreDataQ, which works for recording many features from heavy machine activities. The data contain two sets data. The method is Principal Component Analysis (PCA). This method leads to obtain a maximum of 20 significant features on data based on SOS. The results have been confirmed and agreed upon by the manager who owned CoreDataQ to consider the selected dominant features for further related maintenance.

Infrared Imaging Analysis as Non-Destructive Maintenance of Food Processing Equipment

The use of infrared imaging is a recently developed non-destructive tool to assess the safety, quality and efficiency of the food industry. This article explores the application areas and benefits of using infrared imaging in the maintenance of machinery used in the food industry. Applications covered include food quality control, packaging inspection, temperature monitoring and control, hygiene and sanitation control, foreign object detection, energy saving through optimization, baking, as well as cooking process monitoring and research. By using infrared cameras to their full potential, it is possible to reveal defects, check the level of cleanliness, optimize the maintenance of machines, and examine the thermal attributes of food. This article also provides an in-depth analysis of the historical development of infrared imaging technology. We also explore the basic ideas behind infrared image analysis. This includes the Stefan-Boltzmann Law, Planck's Law, and Wien's displacement law. Infrared image analysis has many advantages and some disadvantages. Despite these drawbacks, infrared imaging analysis has continued benefits. It saves costs, ensures equipment longevity and increases productivity. This makes it very important for the safety of food industry machinery.

Implementing Gaussian process modelling in predictive maintenance of mining machineries

Implementing Gaussian process modelling in predictive maintenance of mining machineries

NO-CONTACT DIAGNOSTICS IN THE FUNCTION OF RISK-BASED MAINTENANCE ON A WIND GENERATOR MODEL

When it comes to risk-based maintenance of technical systems, selecting a diagnostic procedure is the weakest link in the chain of steps that guarantee the system's availability, sustainability, and safe operation while utilizing its working characteristics.The model of a wind generator, which by virtue of its technological structure represents an increasingly widespread system in the geographical and topographical sense, was used to examine diagnostics based on non-contact, remote collection of pertinent information, which is essential for assessing the state of the technical system. In addition, the fact that the system is not physically accessible to the maintenance teams emphasizes the necessity of taking prompt action to rectify work in order to preserve production capacity and the security of the exploitation process.When applied to the wind generator model, proactive maintenance based on risk allows for enhanced interconnected machine efficiency, optimal maintenance process planning, and real-time production process control. These factors contribute to the advancement and refinement of the contemporary 4K industry philosophy.The concept of remote diagnostics in complex technological structures is of interest to the authors. The goal is to process information about the condition through the lens of risk, paying particular attention to the diagnostic method's reliability and improving prediction accuracy in the maintenance of industrial plant machinery.

Instrumental diagnosis of cognitive impairment in workers engaged in the maintenance of agricultural machinery

Workers engaged in the maintenance of agricultural machinery are exposed to a number of production factors in the course of their work, which can be considered as risk factors for the development of cognitive impairment. The purpose of the work: to diagnose cognitive disorders using informative instrumental methods of examination of mental functions, such as cognitive evoked potentials P 300, ultrasound duplex scanning of brachiocephalic arteries in combination with the determination of the atherogenicity index. In order to diagnose the stages of cognitive impairment early, optimal quantitative and qualitative signs in combination with professional experience and age have been determined. Each feature was evaluated in points, which are further summarized. The stages of cognitive impairment are determined by the sum of the points.

Experimental study of sediment erosion in Francis runner using a non-recirculating test rig

Abstract The problem of sediment erosion in hydraulic turbines has posed challenges in design, operation and maintenance of hydraulic machinery. A study was conducted with a 2 kW model Francis runner for investigating the erosion prone locations. The runner and guide vanes were coated with four different coats of colour (red, yellow, green, and blue). The system was then operated in sediment-laden condition with an average concentration of sediment particles of about 24000 ppm. The runner was operated in sediment-laden conditions for 36 hours and the observations were made after each 4 hours of operation. With increasing hours of operation, the colour coating of the runner was eroded at specific locations and a pattern was revealed showing the areas affected by erosion. The erosion was mostly observed at the trailing edge of the pressure side and the junction of the suction side of the blade and hub. Erosion on the hub was observed towards the leading edge of the runner in the area close to the suction side and the region near the trailing edge of blade. Similarly, the guide vanes were eroded at the pressure side of the leading edge and the trailing edge of the suction side. The causes of erosion were identified as high velocity and acceleration of sediment particles, inter-blade vortex, and vortex due to leakage flow.

Integration of Ship Machinery Maintenance and Control Systems in Maritime Education: Enhancing Industry Readiness

Integration of Ship Machinery Maintenance and Control Systems in Maritime Education: Enhancing Industry Readiness

A Study of Quality Management Risks in Construction Projects Based on Social Network Analysis

This research employs a two-dimensional perspective that integrates stakeholders and the entire project lifecycle. It utilizes social network analysis methods and Ucinet6 software to establish a network model of quality management risks in the context of Engineering Inherent Defects Insurance (IDI), Engineering Quality Liability Insurance, and Engineering Quality Assurance Insurance. The study calculates and analyzes the centrality, group density, and cohesive subgroups within the risk network. It identifies key influencing factors and, based on the analysis results, proposes strategies and recommendations. This research provides vital decision support for the future development of the construction industry. The study reveals that three key factors, namely, inadequate construction organization management, unclear construction scope definition, and improper maintenance of construction machinery, are closely related to quality management risk factors. These risks can be effectively mitigated through measures such as strengthening the training of management personnel, implementing relevant regulations, and regular maintenance of construction machinery.

Occupational safety during operation, maintenance and repair of machinery

In article safety of working conditions of workers country, farms is shown at op-eration of agricultural cars and the equipment on various often carried out works in ag-riculture and animal industries, at maintenance service and operating repair of main-tained technics, and also at an emergency, on fi re safety, maintenance with means of in-dividual defense and tool control of working conditions.