Equipment Downtime Research Articles

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.

Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

A Hybrid Method for Identifying Critical Failure Modes in a Ball Mill

The reliability of a ball mill is crucial for the seamless operation of ore processing and other industrial plants, where unexpected equipment failure or downtime can result in significant financial losses and reduced production efficiency. Thus, this paper examined a hybrid method for identifying critical failure modes in a ball mill using the Failure Modes, Effects, and Criticality Analysis combined with the Complex Proportional Assessment with Grey Numbers (COPRAS-G). The COPRAS-G method was employed to assess the criticality of various failure modes by determining the weights of significant failure modes in the bearing, gearbox, motor, and shaft components of a ball mill. It was observed that gear seal failure is the most critical failure with percentage contribution (Ni) of 100%, while bearing wear is the least critical failure with percentage contribution (Ni) of 62%. The result suggests that failure modes of gear seal failure, gear pitting, motor bearing fatigue, and shaft fracture are regarded as the main contributors to failure with percentage contribution (Ni) of 100%, 97%, 87% and 83% respectively. Current ball mill maintenance includes corrective, preventive and predictive maintenance. For failure modes with high criticality, predictive maintenance was advised, while for moderate and low criticality, corrective and preventive maintenance were advised.

Combined ResNet Attention Multi-Head Net (CRAMNet): A Novel Approach to Fault Diagnosis of Rolling Bearings Using Acoustic Radiation Signals and Advanced Deep Learning Techniques

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.

Integrating artificial intelligence into ERP systems: advantages, disadvantages and prospects

Objective: to identify the key benefits and potential risks associated with the use of artificial intelligence in ERP systems to improve decision-making processes, management efficiency and operational performance of various sectors, including commercial and non-profit organizations. Methods: systematic literature review, empirical data analysis, analytical and experimental research methods. Results: the key directions of artificial intelligence implementation in ERP-systems are reflected, providing improvement of operational efficiency, customer relations, as well as optimization of business processes, data management, supply chain and personnel management, automation of operations related to finance, optimization of customer relations; implementation of artificial intelligence in ERP-systems reduces inventory management costs, improves the accuracy of forecasting andinventory optimization, accelerates financial analysis and increases the accuracy of budgeting, resulting in reduced budget planning time; it also increases productivity by optimizing necessary production processes and reducing equipment downtime. However, there are also risks of confidential data leakage, unauthorized access to data; job losses due to automation of tasks; and vulnerability to cyberattacks. Scientific novelty: the little-studied directions of artificial intelligence integration in ERP-systems are analyzed; an integrative approach to the application of artificial intelligence in ERP-systems is proposed, which combines methods of machine learning, natural language processing and predictive analytics and provides a comprehensive assessment of the complex impact on the business processes’ efficiency. Practical significance: the formulated directions for solving the identified problems of artificial intelligence integration in ERP-systems can be implemented in practice, as they will enable to better take into account local requirements and laws.

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC − heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2 %, recall of 89.7 %, precision of 88.5 % and F1-score of 89.2 %. These results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction

Abstract In industrial production, rolling bearings are widely used as key mechanical components in all types of rotating machinery. Fault diagnosis is essential for predicting bearing damage in advance, avoiding sudden equipment downtime and reducing economic losses. However, rolling element fault diagnosis of rolling bearings continues to be a challenge, especially with multi-rolling element faults. In view of the characteristics of randomness, weakness, and coupling in the vibration signal generated by multi-rolling element faults in rolling bearings, a multi-rolling element fault detection method is proposed by combination time-frequency (TF) analysis (TFA) with multi-curves extraction methods. The pre-processing method combined autoregressive model with maximum correlated kurtosis deconvolution is employed to enhance the weak periodic fault impulses in the raw vibration signals of the rolling bearing. Then an improved dynamic path multi-curves extraction method is proposed to extract multiple TF curves from the TF spectrogram (TFS) constructed via short-time Fourier transform. According to the proposed classification criteria, the TF curves are classified as homologous faults. The TF masking (TFM) method is employed to keep TF information closely associated with the fault impulse. Finally, the fault signals are reconstructed sequentially based on the TFS processed by TFM, and precise identification of multi-rolling element faults is achieved by envelope analysis. Experimental results demonstrate the effectiveness of the proposed method in extracting the weak fault features of multi-rolling elements and accomplishing fault separation and diagnosis.

Butterfly valve erosion prediction based on LSTM network

When valves and pipelines are used, erosion wear is a major concern. Erosion wear can lead to equipment downtime, material replacement, and other issues, as well as seal surface failure. The research delves into the phenomenon of erosion in butterfly valves, crucial components utilized across diverse industrial sectors. Erosion primarily affects the valve's disc and seat regions, leading to premature wear and compromised performance. To address this issue, the research employs computational fluid dynamics (CFD) and machine learning techniques, including long short-term memory (LSTM) and BP neural networks, to predict erosion rates under various conditions (different valve openings and particle diameters). Results indicate that erosion escalates with time and larger valve openings, highlighting the need for proactive maintenance strategies. The LSTM model demonstrates superior predictive capabilities compared to the BP neural network, offering valuable insights for improving butterfly valve design and operational efficiency in industrial settings. Furthermore, to seek a more efficient network configuration, the intelligent search capabilities of the particle swarm optimization (PSO) algorithm have been utilized to systematically explore the optimal network structure parameters. The prediction results highlight the advantages of LSTM in handling time series data, particularly in predicting erosion rates with complex dynamic characteristics.

Applications of augmented reality (AR) in chemical engineering education: Virtual laboratory work demonstration to digital twin development

With advances in information technologies, virtual technologies have become a viable option for chemical engineering educators. Although traditional teaching methods remain effective, they have many limitations when presenting complex phenomena. Virtual technologies like Augmented Reality (AR) have shown great potential as an addition to traditional teaching methods. This study explored the practicality and potential applications of AR technologies in chemical engineering education. In this work, traditional 2D representations of CFD simulation results were replaced by AR experiences to visualise the flow pattern and assist the understanding of the working mechanism behind different devices. Other than CFD result integration, animations were also included in AR experiences for personalised training and inclusive laboratory work experience. AR-assisted training has shown good potential in reducing financial loss and equipment downtime due to improper handling. Besides delivering teaching content, the development process of a digital twin with an AR interface was used to demonstrate how virtual technologies can be involved in digitalisation in Industry 4.0. Beyond the technical contents, important design philosophies were also taught as part of the courses.

Анализ изнашивания элементов опорно-поворотного устройства стреловых самоходных кранов производства LIEBHERR

The failure of the slewing support leads to significant economic damage associated with long downtime of equipment due to the impossibility of replacing it with a new one as a result of the imposition of sanctions by unfriendly countries. If earlier, before the introduction of restrictive sanctions (measures), the failure of the was less painful and took from 15 days to one month maximum, now it stretches to 3… 5 months. One of the reasons for the failure of equipment is the wear of the teeth of the wheel and gear of the rotary bearing, which leads to a change in the stress-strain state of the cassettes with rolling elements. There is uneven wear of the wheel in height, with the upper part of the wheel wearing out to a greater extent than the lower part, which leads to slippage between the gear teeth in the upper part to a greater extent than in the lower part. Wear of the teeth of the gear and the turntable leads to uneven and increased wear of the cassettes and cages of the slewing support and, accordingly, to the loss of rolling elements. The established unevenness of wheel wear in its various segments makes it possible to make proposals for improving the wheel design. The studies of the wear of cassettes and rollers made it possible to establish changes in the stress-strain state in the contact zone of the cassette and the roller, which in the future will improve the method of their calculation and make a proposal to improve the connection between the cassette and the roller.

Markov Chain Method in Decision Implementation of Preventive Maintenance Scheduling to Reduce Equipment Downtime in PT. ADF Indonesia: Case Study

PT. ADF is a company operating in the shoe manufacturing industry located in Indonesia. PT. ADF has the problem of machine downtime which is currently still relatively high and requires quite a long repair time. The aim of this research is to reduce the high downtime of equipment or machines with the Markov chain method approach used in selecting the next strategy to produce a better probability of implementing scheduled preventive maintenance (PM) compared to Corrective Maintenance (CM), so that implementing a maintenance schedule is chosen to reduce downtime. equipment/machines. This strategy can reduce obstacles or disruption to production schedules. Based on the Pareto diagram, it is known that the largest downtime is caused by damage to the Rotary injection molding machine and molding machine at 80%. After implementing the preventive maintenance scheduling system as intended, the effectiveness of implementing preventive maintenance can be determined by knowing the results of calculating downtime data on Rotary printing machines and printing machines which were previously 2180 minutes/3 months to 729 minutes/3 months or the equivalent of a cost reduction of IDR. 54,735,613.00 to IDR. 18,303,790.00 or the company obtains savings of 66.56%.

ИНФОРМАЦИОННО-ДИАГНОСТИЧЕСКИЕ СРЕДСТВА ДЛЯ СЕЛЬСКОХОЗЯЙСТВЕННОЙ ТЕХНИКИ

The issues of assessing the improvement of the efficiency of agricultural domestic machinery according to the criteria of adaptability to maintenance and diagnostics and the development of measures to improve it are relevant, as they have a significant impact on reliability and total cost of ownership and resource savings during operation. The costs of maintenance and repair and losses from equipment downtime depend on the level of equipment adaptability to maintenance and diagnostics. (Research purpose) The research purpose is substantiating the development of additional information and diagnostic tools to improve the efficiency of agricultural machinery. (Materials and methods) Conducted an analysis and description of the developed tools for diagnosing agricultural machinery. We have studied similar control and diagnostic equipment manufactured by third-party manufacturers for agricultural machinery. We examined the distinctive features of our own developments and reflected the need to diagnose specific parameters. The possibilities of analyzing the received information using artificial intelligence algorithms were described. (Results and discussion) Were designed in special engineering software and the first prototypes of these technical means were assembled. Conducted 3D modeling of these devices. We performed tests that proved the possibility of developing such digital diagnostic devices for agricultural machinery, justified their effectiveness based on the data obtained. Proposed modernization of a number of systems. (Conclusions) Analyzed the results of the work of information and diagnostic devices. We collected materials on modern methods of diagnosing equipment. We have determined the further development of the direction of work for better diagnostics of components and assemblies of agricultural machinery. One of the proposed directions is the work on the adaptation of artificial intelligence algorithms for the diagnosis of agricultural machinery.

Prediction maintenance based on vibration analysis and deep learning — A case study of a drying press supported on a Hidden Markov Model

The main objective of this paper is to describe a methodology that was developed to support maintenance decision-making methods based on equipment condition. Condition-Based Maintenance allows to increase equipment availability and maximize investments. This is mainly due to the prevention of unexpected equipment downtime. By avoiding turning on/off industrial equipment, production flows are more efficient, allowing manufacturers to improve the quality of the end-product. The industry aims more and more to correspond satisfying customer expectations. We argue that the methodology developed in this paper adds value to the existing literature, namely because the fact that it is possible to anticipate the state of an equipment without a large amount of support data. In other words, although one could find information gaps regarding the occurrence of failures, it was possible to accurately assess the state of the equipment. This approach is robust, as it can be used in distinct equipment with different sensors, making this methodology generalizable for Condition-Based Maintenance. The paper presents the validation of the preceding through a case study on drying presses in the paper industry. To do so, three states were adopted, namely: “Proper function”; “Alert state”; and “Equipment failure”. The methodology follows a series of steps, going through the collection of values from vibration sensors, imputation of values using Deep Artificial Neural Networks through on-line sensors, until reaching the last stage of classification carried out by the Hidden Markov Model. Through optimized observations from the previous steps, it was possible to define the hidden states through the Viterbi algorithm, which corresponds to the health states of the equipment. Additionally, it was possible to demonstrate that the proposed methodology can accurately characterize the condition states of the equipment based on the data obtained and can be generalized to other types of equipment.

UTILIZING MACHINE LEARNING TO ASSESS DATA COLLECTION METHODS IN MANUFACTURING AND MECHANICAL ENGINEERING

This study explores the significant impact of machine learning (ML) on data collection methods within the manufacturing and mechanical engineering sectors, emphasizing its superiority over traditional techniques. By analyzing data from 20 case studies and 15 industry reports, the research highlights how ML models such as neural networks and support vector machines enhance accuracy, efficiency, and reliability. The findings reveal that ML-based methods excel in handling large datasets, automating processes, and reducing human error, thereby improving data quality and operational performance. Applications in predictive maintenance and quality control demonstrate substantial reductions in equipment downtime and defect detection errors, alongside streamlined workflows and cost savings. Additionally, the study shows that ML can optimize process parameters and identify bottlenecks more effectively, leading to enhanced overall efficiency in industrial operations. These results underscore the transformative potential of ML in optimizing data collection practices, marking a significant advancement in industrial operations and paving the way for more innovative and efficient practices across the sector.

Implementation of Internet of Things for Condition-Based Maintenance in Elevator Systems: An Analytical Case Study

The aim of this paper is to show how IOT technology can allow highly distributed elevator equipment servicing by using remote-monitoring technology to ease a move from tradi- tional corrective maintenance (CM) and time-based maintenance (TBM) to more prophetic, condition-based maintenance (CBM) in evidence to accomplish different advantages.The Literature review can indicate that CBM has merit over conventional CM and TBM from a theoretical outlook, but it turns on constant monitoring enhancement via advanced IOT technology. A detailed case history was implement to bring practical proof that IOT allows elevator adamant to attain CBM.Through, a theoretical context, the CBM of elevators put together exper- tise.The venture lies in data collection, data analysis and decision making in real-world context of business. The key outcome of this research recommends that in the case of elevators the IOT can be commercialized and would improve the welfare and accuracy of equipment. It might, so, be understandable from technological, process and economic aspect. Our long term physical world case study reveal an actual way of building the CBM of elevators worldwide. Incorporating Internet of things and other modern technology will improve the safety and authenticity of elevator equipment,longer service life, minimize disruption or difficulty and business interference as a result of equipment downtime and to eliminate major repairs, thus result in low maintenance cost. A key contribution in this study stand in the empiric confirmation of the advantages and difficulties of CBM through Internet of things comparative to conventional CM and TBM in the instance of elevators. The writer had faith that this study is well timed and will be worth to company working on the same research or business strategies.These are form on condition based maintenance, IOT, Time-based maintenance and Corrective maintenance.

ANALYSIS OF THE PROPERTIES OF COATINGS USED TO PROTECT THE PUMP AND COMPRESSOR PIPE

In this article considers basic properties of protective coatings applied to the inner wall of tubing in order to extend pump and compressor pipe (PCP) service life PCP and minimize operational risks. The main complicating factors, such as scale deposits, corrosion aggressiveness, mechanical impurities, etc., arising during PCP operation are specified, groups of test media and test parameters for grading different types of coatings according to their resistance to complicating factors are singled out. Influence of various aggressive media on PCP, such as distilled water, petroleum product simulator, etc., coatings is described, and test media and test parameters for protective coatings are reviewed. The basic methods for determining the physical and mechanical characteristics, such as determining adhesion, thickness and dielectric continuity, of coatings are given, and brief informative and descriptive references for each of them are given. Recommendations are given for testing protective coatings on the inner surface of PCP for compliance with technical requirements, which ultimately allows selecting materials most resistant to aggressive field environments. The relevance of studying the influence of aggressive media on the protective coatings of the inner wall of tubing is due to the need to increase the level of reliability and durability of technical systems in operating conditions. The work is of interest to specialists in the field of materials science, technical expertise and tubing design, as well as researchers involved in the development of new materials and methods for surface protection in operating conditions. The results obtained will optimize the choice of protective coatings for specific operating conditions, which helps to increase the service life of tubing and reduce maintenance costs. This is of direct importance for reducing the risks of accidents and the environmental consequences of oil and gas production processes. In addition, the development of innovative materials and methods for protecting the surfaces of the inner wall of the tubing contributes to improving the efficiency of the operation of oil and gas wells, as well as helps to reduce equipment downtime.

STUDY OF THE RELIABILITY AND OPTIMIZATION OF THE AUTOMATED ROLLING STAMPING COMPLEX

The reliability of technical objects is one of their most important quality characteristics. Automated rolling stamping complexes are widely used in mechanical engineering for the manufacture of parts of complex shape. Reliability of CABs is a key factor that determines their efficiency and cost-effectiveness. The development of a device for automatically changing the equipment of the stamping-rolling complex is due to the need to increase efficiency and automate production processes in industry. Roll stamping is an important technology for the production of metal parts, but the process of changing equipment (for example, tools or dies) can be time- and resource-intensive. The development of an automatic equipment changer can significantly improve the productivity and efficiency of production lines, reduce equipment downtime and manual labor costs. In addition, such a device can increase occupational safety and reduce the risk of injury among workers. Since modern industry is rapidly developing and constantly improving production technologies, the development of a device for automatically changing equipment for a stamping-rolling complex is an urgent and important task. The implementation of such a device can help enterprises maintain a competitive edge in the market and increase their efficiency and profitability. The article deals with the reliability of the automated rolling stamping complex, which is one of the key aspects of modern production. Factors affecting the reliability of such systems and ways to increase them to ensure smooth operation of production lines are analyzed. The article reveals the main problems associated with equipment failures and downtime, which can lead to significant financial losses and loss of reputation of the enterprise. The impact of such factors as the intensity of operation, maintenance, as well as the introduction of the latest management and monitoring technologies on the overall reliability of the system was analyzed. The article proposes a comprehensive approach to increasing the reliability of automated rolling stamping complexes. This approach includes optimization of operating modes, improvement of maintenance and diagnostics systems, as well as the use of advanced management technologies and equipment condition monitoring. The results of the research can be useful for factories that use automated rolling stamping complexes in their activities. They will make it possible to increase the efficiency of the production process, reduce the costs of maintenance and repair, as well as ensure the stable and uninterrupted operation of production lines.

Penentuan Jumlah Persediaan Sparepart Berdasarkan Waktu Maintennance Headtruck Giga di PT. Bima Site Berlian

PT. Berkah Industri Mesin Angkat (PT.BIMA) is a company engaged in the maintenance and supply of port equipment. In the service process there are five types of transport aircraft at PT BIMA. Loading and unloading equipment operates continuously, causing a decrease in the level of equipment reliability and causing frequent breakdowns and high downtime on the machines, especially the Headtruck equipment. To minimize breakdowns and downtime, it is necessary to have a good maintenance scheduling system to prevent machine damage. Mean Time to Failure (MTTF) is one of the company's provisions as a reference for determining an effective maintenance schedule. Therefore, appropriate corrective action is needed to ensure the number of spare parts available so that equipment downtime is not too long which results in decreased/unstable levels of availability and reliability.

An experimental hybrid customized AI and generative AI chatbot human machine interface to improve a factory troubleshooting downtime in the context of Industry 5.0

Throughout industrial revolutions, equipment downtime mitigations have been one of the ultimate goals of most factories. Several tools, such as human machine interface (HMI) alarming systems or predictive maintenance schedules, assist in reducing system downtime but still depend on the operators’ ability to swiftly retrieve, understand, and efficiently act upon reported failures. We propose the design of a hybrid experimental artificial intelligence (AI) and generative AI chatbot HMI that effectively extracts factory equipment conditions that are useful for troubleshooting and predictive maintenance analysis. We achieve these functions by feeding experimental factory-monitored data to the customized chatbot application tool running in its back-end, a Langchain agent linked to the OpenAI GPT-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}3.5 language model (LM) via OpenAI APIs. We design our chatbot front-end with Streamlit, an open-source web app. In the context of I5.0, our chatbot HMI uses personalized natural language, English, to interact with the operator, making the information extraction more understandable. We also integrate the generative AI capability of the GPT 3.5 LM that augments the factory data based on the loaded format to create a larger dataset for additional tasks like machine learning modelling. The experimental results show the accuracy of our customized chatbot HMI when retrieving data based on specific prompts and the advantages of a reduced troubleshooting time compared to operations in traditional factories, which are highly dependent on supervisors’ interventions. Our study provides a valuable example of upgrading standard factory HMIs to I5.0-capable ones by implementing customized AI and generative AI chatbots within operational industrial environments.

Seasonality as Factor of Logistic Costs Formation

The seasonality of demand for goods and services creates vital issues both for the organization of their production, and for implementation of logistic processes. Seasonal fluctuations, as a rule, negatively affect work of the enterprises that is shown in equipment downtimes, incomplete use production and warehouse, in uneven use of human resources, violation of rhythm of activity, to the unreasonable growth of costs. In logistic activity the seasonality influences a set of its components, including creation and maintenance of stocks, existence of warehouse spaces, number and capacities of sales channels and supply. This influence in much defines fluctuations of values of logistic costs. The article proposes an approach to identifying the presence of seasonality, quantitative characteristics in various periods of the annual cycle, the separation of seasonal and trend components of seasonality dynamics, mathematical and statistical modeling of seasonality in the formation of such costs. A forecast assessment of the future dynamics of costs is carried out. Emphasis is made on the highlighting the seasonal component in the indicators of logistics costs that allows to analyze their dynamics more deeply, improve the quality of management decisions associated with them.