Industrial Operations Research Articles

Stackelberg game analysis of the energy performance contracting considering altruistic preferences

The Stackelberg game analysis of an Energy Performance Contracting(EPC) is established to correctly analyze the impact of altruistic preference behaviors on the decision-makers, energy service companies(ESCOs) and energy-consuming units(EUs). Its motivation comes from the fact that, to achieve a win-win situation, EUs usually engage in altruistic behaviors, but the implementation results of the EPC are always contrary to their expectations. Using nonlinear optimization theory, analyzed the decisions of the Stackelberg models under different altruistic preference attributes, and then compared the impacts of altruistic preferences on decisions and profits. Results reveal: (1)compared to a situation lacking altruistic preference, when the leader, the EU, has an altruistic preference, the EU only needs to transfer a small part of its own interests to significantly increase the ESCO's profit and ultimately improve the overall EPC profit; (2) the altruistic preference of the ESCO functions akin to a "poison," engrossing the company in the satisfaction derived from the increased revenue of the energy-consuming entity and gradually eroding its own profits; and (3)the altruistic preference behavior of both parties is a kind of “benefit transfer behavior,” which is conducive to stable operation and long-term development of EPC industry. The research results can provide a theoretical reference to enhance the development of energy-saving service industry.

Mixed convection flow and heat-mass transfer of cross liquid near a stagnation region with Dufour, Soret effects and chemical reaction

Heat-mass transfer in a liquid flow over a stretching surface at a stagnation regime is significant in optimal manufacturing and quality assurance depending on the rheological behavior of viscoelastic fluids in process machinery. Stretching sheet flow at a stagnation point is a standard procedure used in the metallurgical industries for manufacturing of different equipment and cooling systems. Due to its various applications in industrial operations, the current study aims to explore the heat-mass transfer of buoyancy-driven flow of cross liquid over a stretching sheet at a stagnation point. The heat and mass transfer under the framework of Dufour and Soret effects, radiant heat, and chemical reactions are assessed. Furthermore, the analysis also takes into account the linear and nonlinear convection-diffusion phenomena. The model findings are obtained by merging the Bvp4c in MATLAB using a local similarity technique up to the fourth truncation threshold. The findings illustrate that the action of magnetic field can be used to control the flow across the sheet. Temperature increases with thermal radiation, Biot number and Dufour number. The action of buoyancy ratio parameter result in improvement in fluid flow. With increasing trend of magnetic and Weissenberg number, the flow field and skin friction coefficient drops.

Data-Driven Techniques and Data Analytics in Water Treatment Facilities: Innovative Safety Protocols and Optimization

Aim: To examine harnessing of data-driven techniques and data analytics in water treatment facilities considering innovative safety protocols and process optimization. Problem Statement: The most significant part of human existence and industrial operations has been linked to water which often becomes vulnerable to hazardous contaminants which are brought on through natural processes and human activity. Significance of Study: The use of water treatment facilities in order to make water a sustainable natural resource for the whole world is vital. To achieve this, it is imperative to monitor and classify water quality. Methodology: Recent literature materials in form of books, journals and relevant published articles in the area of data-driven techniques and data analytics in water treatment facilities were consulted. Discussion: This review article has critically examined harnessing of data-driven and data analytics in water treatment facilities with consideration also given to innovative safety protocols and process optimization. The basic fundamental principle of water treatment facilities were discussed alongside the application of data-driven techniques. Safety protocols in water treatment facilities were discussed. Data driven techniques in water treatment facilities and applications of machine learning and artificial intelligence techniques to water treatment were explained. It was noticed that AI-Driven solutions transforms real-time water quality analysis and offers a proactive approach to water treatment. The integration of machine learning and AI algorithms capabilities in water treatment facilities can now respond quickly to fluctuations and continuously monitor key parameters fluctuations in water quality. Consideration was given to artificial intelligence as data analytics optimization tool for water treatment facilities. Future prospects, limitations and challenges of the study were stated. Conclusion: AI algorithms and historical data can be harnessed into water treatment facilities in order to assess performance patterns, predict equipment malfunctions and proactively plan the maintenance activities.

Robot Calibration Sampling Data Optimization Method Based on Improved Robot Observability Metrics and Binary Simulated Annealing Algorithm

As the societal demand for precision in industrial robot operations increases, calibration can enhance the end-effector positioning accuracy of robots. Sampling data optimization plays an important role in improving the calibration effect. In this study, a robot calibration sampling point optimization method based on improved robot observability metrics and a Binary Simulated Annealing Algorithm is proposed. Initially, a robot kinematic model based on the Product of Exponentials (POE) model and a generalized error model is established. By utilizing the least squares method, the ideal pose transformation relationship between the robot’s base coordinate system and the laser tracker measurement coordinate system is derived, resulting in an error calibration model based on spatial single points. An improved robot observability metric combined with the Binary Simulated Annealing Algorithm (BSAA) is introduced to optimize the selection of calibration sampling data. Finally, the robot’s parameter errors are obtained using a nonlinear least squares method. Experimental results demonstrate that the average end-effector positioning error of the robot after calibration can be reduced from 0.356 mm to 0.254 mm using this method.

Assessing bioavailability risks of heavy metals in polymetallic mining regions: a comprehensive analysis of soils with varied land uses.

To accurately assess the bioavailability risk of heavy metals (HMs) in a representative polymetallic mining region, we undertook an exhaustive analysis of Cu, Pb, Ni, Co, Cd, Zn, Mn, and Cr in soils from diverse land-use types, encompassing agricultural, forest, residential, and mining areas. We employed speciation analysis methods and a modified risk assessment approach to ascertain potential ecological threats posed by the HMs. Our findings reveal that both the total potential ecological risk and the modified bioavailability risks are most pronounced in the soil of the mining area. The modified bioavailability threats are primarily caused by Pb, Ni, Cd, and Co. Although the total potential ecological risk of Cu is high in the local soil, the predominance of its stable forms reduces its mobility, thereby mitigating its detrimental impact on the ecosystem. Additionally, medium modified bioavailability risks were identified in the peripheries of agricultural and forest areas, potentially attributable to geological processes and agricultural activities. Within the urban district, medium risks were observed in residential and mining areas, likely resulting from mining, metallurgy, industrial operations, and traffic-related activities. This study provides critical insights that can assist governmental authorities in devising targeted policies to alleviate health hazards associated with soils in polymetallic mining regions.

Employing a multi-resonance microwave sensor for in-line moisture monitoring of fluidized bed agglomeration

Amorphous substances are relatively common in food industry and their characteristics depend highly on the moisture content. Process parameters are adjusted based on online measurements to control the product quality of the agglomerates. This leads to two major challenges: precise moisture content measurements of amorphous particles at (i) different temperatures and for (ii) a wide moisture content range.In this study two workflows (inline fluidized bed and conditioned fixed bed) calibration of microwave sensors are compared and it is shown that for fluidized beds inline calibration is necessary to achieve accurate moisture measurements. The application of the novel microwave sensor MW 4200 working at resonance frequencies of 2.3 and 5.6 GHz is described. The temperature influence on the measurement is investigated with measurements of a fixed particle bed. Rapid, reliable in-process moisture control at a wide range of moisture contents and temperatures for agglomeration in fluidized beds, with a mean relative accuracy of 82 %, is achieved. This technology improves productivity and precision in industrial operations, minimizes waste and optimizes energy usage.

Anomaly Detection Method for Industrial Control System Operation Data Based on Time-Frequency Fusion Feature Attention Encoding.

Anomaly detection in industrial control system (ICS) data is one of the key technologies for ensuring the security monitoring of ICSs. ICS data are characterized as complex, multi-dimensional, and long-sequence time-series data that embody ICS business logic. Due to its complex and varying periodic characteristics, as well as the presence of long-distance and misaligned temporal associations among features, current anomaly detection methods in ICS are insufficient for feature extraction. This paper proposes an anomaly detection method named TFANet, based on time-frequency fusion feature attention encoding. Considering that periodic variations are more concentrated in the frequency domain, this method first transforms the time-domain data into the frequency domain, obtaining both amplitude and phase data. Then, these data, together with the original time-series data, are used to extract features from two perspectives: long-term temporal changes and long-distance associations. Finally, the six features learned from both the time and frequency domains are fused, and the feature weights are calculated using an attention mechanism to complete the anomaly classification. In multi-classification tasks on three ICS datasets, the proposed method outperforms three popular time-series models-iTransformer, Crossformer, and TimesNet-across five metrics: accuracy, precision, recall, F1 score, and AUC-ROC, with average improvements of approximately 19%, 37%, 31%, 35%, and 22%, respectively.

Porous Organic Cage as an Efficient Platform for Industrial Radioactive Iodine Capture

AbstractHerein, we firstly develop porous organic cage (POC) as an efficient platform for highly effective radioactive iodine capture under industrial operating conditions (typically ≥150 °C), ≤150 ppmv of I2). Due to the highly dispersed and readily accessible binding sites as well as sufficient accommodating space, the constructed NKPOC‐DT‐(I−) (NKPOC=Nankai porous organic cage) demonstrates a record‐high I2 uptake capacity of 48.35 wt % and extraordinary adsorption capacity of unit ionic site (~1.62) at 150 °C and 150 ppmv of I2. The I2 capacity is 3.5, 1.6, and 1.3 times higher than industrial silver‐based adsorbents Ag@MOR and benchmark materials of TGDM and 4F‐iCOF‐TpBpy‐I− under the same conditions. Furthermore, NKPOC‐DT‐(I−)Me exhibits remarkable adsorption kinetics (k1=0.013 min−1), which is 1.2 and 1.6 times higher than TGDM and 4F‐iCOF‐TpBpy‐I− under the identical conditions. NKPOC‐DT‐(I−)Me thus sets a new benchmark for industrial radioactive I2 adsorbents. This work not only provides a new insight for effectively enhancing the adsorption capacity of unit functional sites, but also advances POC as an efficient platform for radioiodine capture in industry.

Piperazine pyrophosphate-functionalized Ni-MOF metal framework: Fabrication and synergistic explosion suppression mechanisms

Dust explosion suppression is crucial for ensuring safe industrial operations. To explore composite explosion suppression powders with higher performance, piperazine pyrophosphate (PAPP)-functionalized nickel-based metal–organic framework (PAPP@Ni-MOF) synergistic suppression materials were designed and synthesized. The effectiveness of this suppressant in inhibiting the explosion flame and intensity of polypropylene (PP) dust was investigated using explosion flame propagation and explosion pressure test apparatus. The results indicate that the combination of N/P elements and Ni-MOF exhibits excellent catalytic carbonization performance. Adding 50 wt% PAPP@Ni-MOF can completely inhibit the propagation of PP explosion flames and the rise in explosion pressure. The maximum propagation distance of the explosion flame was reduced by 78.07 %, the overall flame propagation speed decreased by 84.54 %, and the maximum explosion pressure was reduced by 76.87 %. Through characterization experiments, the products before and after the explosion were analyzed. Combined with molecular dynamics (MD) simulation results, the efficient synergistic explosion suppression mechanism of PAPP@Ni-MOF for PP dust was revealed from the perspectives of thermal conduction isolation, key radical recombination, and volatile gas adsorption.

A IMPORTÂNCIA DA MANUTENÇÃO PREDITIVA NA INDÚSTRIA 4.0

In the era of Industry 4.0, technological evolution has significantly transformed the management and maintenance practices of industrial equipment. The integration of advanced technologies has enabled a more sophisticated and efficient approach to machine and facility maintenance. In this context, it becomes crucial to investigate how these innovations impact the operational efficiency of industries. How can the adoption of predictive maintenance techniques affect operational efficiency in the Industry 4.0 era? This study aimed to review the existing literature on predictive maintenance, exploring its main techniques, associated technologies, benefits, challenges, and practical applications. The investigation was conducted through databases such as thesis and dissertation repositories, Google Scholar, and Emerald Insight, analyzing 29 relevant pieces of evidence. The review was organized into three chapters, each addressing different aspects of predictive maintenance. The conclusion indicated that predictive maintenance, by allowing continuous monitoring and forecasting of failures before they occur, significantly enhances operational efficiency in industries. Its main benefits include reduced operational costs, increased production efficiency, improved safety, and optimized spare parts inventory. However, its implementation presents challenges such as initial investment in technologies and the need for specialized skills. Therefore, predictive maintenance is crucial for the competitiveness and sustainability of industrial operations in the current technological era.

Development of a model for solving the functional task «Formation of surgical operations schedule» in a medical institution

The object of the research is the process of forming the schedule of surgical operations of the Sytenko Institute of Spine and Joint Pathology National Academy of Medical Sciences of Ukraine. Modern research in the field of modeling work on the formation of the work schedule of medical institutions and surgical departments is focused on the development of models for the formation of schedules for the requirements of specific institutions. The use of these models to automate the formation of the schedule in other medical institutions is impossible without theoretical and applied modification of existing solutions taking into account the requirements and features of a particular institution. Therefore, conducting research to solve this problem is relevant from a theoretical and applied point of view. It was proposed to use a complex of mathematical models of target programming as the basis of the schedule formation model, which made it possible to form a schedule under conditions of limited resources. The main features of the Sytenko Institute of Spine and Joint Pathology National Academy of Medical Sciences of Ukraine as an object of automation. Based on these features, a modification of the proposed models was developed, which allows automating the process of forming a schedule that would have minimal deviations from the submitted applications for operations. Based on the results of the modification of the schedule formation model, the choice of the method of solving the problem was justified and it was proposed to adapt the modification of the method of branches and boundaries to solve the problem of forming the schedule of surgical operations, it is proposed to adapt this modification to the specifics of the specified problem. The peculiarities of implementation of algorithmic, informational and software tools of the developed functional task are considered. Experimental verification of the obtained scientific results was carried out. For such verification, real data of applications for operations at the Sytenko Institute of Spine and Joint Pathology National Academy of Medical Sciences of Ukraine The obtained results of the experimental test allow to make a decision about the possibility of applying the results of solving the functional problem in the industrial operation of the medical institution.

Thermal Efficiency Analysis of a 1 kW ORC System with a Solar Collection Stage and R-245fa Working Fluid: A Case Study

A thermal efficiency analysis of an organic Rankine cycle (ORC) system enables its performance to be evaluated; for this purpose, critical system components, including the turbine and the boiler, must be scrutinized. ORC plants can operate under various regimes, such as simple, regeneration, and reheat work modes. Organic fluids such as R-245fa integrate low-temperature sources such as solar radiation. However, a literature review revealed limited research on the impact of a solar collection system on the overall thermal efficiency of an ORC system during the regeneration stage. In this study, we examined the thermal efficiency behavior of an ORC plant with a 1 kW generator operating in simple and regeneration modes with a solar collection stage. The results show that the thermal efficiency in simple mode was 35.27%, while in regeneration mode with solar collection it reached 51.30%. Improving the thermal efficiency of a thermodynamic cycle system can reduce CO2 emissions. The operating temperature ranges facilitate the development of a methodology for industries to implement ORC systems in their manufacturing processes, thereby utilizing waste heat from industrial operations.

Simulating the Rising Air Bubbles Stream through Heavy Fuel Oil Residue Bubble Column Reactor using COMSOL

Several industrial operations rely on the rise of air bubbles through heavy fuel oil residue (HFOr), including oil recovery and wastewater treatment. In order to increase efficiency and decrease expenses, it is necessary to understand the peculiarities of this process. With the help of COMSOL Multiphysics, we numerically simulate the ascent of a single air bubble via HFOr in this article. The dimensions of the container used in the simulations were (100) mm in diameter and (200) mm in height, with an air bubble diameter of 4.5 mm. For an air bubble ascending through an HFOr column, the predicted numerical outcomes are contrasted with nine experimental versions. As the bubble rose higher, the region with the greatest number of vortices was located on its side. The remaining vortex is contained inside the bubble. In addition, since drag decreases the flow, a lighter fluid usually exhibits a region of strong vortex and low pressure. Because of the oscillation effect, the results reveal that the rising velocity initially rises and then gradually falls between 0.6 and 0.65 s. The air bubbles' wavy motion is caused by the high fluid density, which becomes worse as the air velocity gets higher. The values of the drag coefficient rise sharply with decreasing air extrusion speed. It turns out that the height of the fluid has an inverse relationship with the pressure.

Machine learning-aided risk-based inspection strategy for hydrogen technologies

Although technically challenging, effective, safe, and economical transport is crucial for enabling a widespread rollout of hydrogen technologies. A promising option to transport large amounts of hydrogen lies in employing retrofitted natural gas pipelines. Nevertheless, H2-rich environments tend to degrade pipeline steels, reducing their load-bearing capability and accelerating crack propagation. Regular inspection and maintenance activities can preserve the pipelines’ integrity and guarantee safe operations. The risk-based inspection (RBI) approach is based on estimating the risk for each component item. It focuses most inspection activities on high-risk components to reduce costs while maximizing the plant’s safety and availability. However, the RBI standards do not consider hydrogen-induced degradations and cannot be adopted for industrial equipment operating in H2 environments. This study proposes a novel ad-hoc methodology for the risk-based inspection planning of hydrogen handling equipment. A machine-learning model to predict the fatigue crack growth in gaseous hydrogen environments is developed and integrated with the conventional RBI approach. The proposed methodology is validated on three pipelines transporting hydrogen and natural gas in different concentrations. The results show how similar operating conditions can determine different degradation rates depending on the environment and highlight how hydrogen-enhanced fatigue can reduce the pipelines’ lifetime.

Monitoring and Diagnosing Faults in Induction Motors’ Three-Phase Systems Using NARX Neural Network

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = 94.2%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%.

Unlocking potential for a circular bioeconomy transition through digital innovation, lean manufacturing and green practices: a review

PurposeToday’s businesses are looking for a circular bioeconomy (CBE) to develop a sustainable manufacturing process as industrial operations result in significant amounts of waste materials and the depletion of natural sources. The industry commonly applies techniques such as lean manufacturing (LM), digital innovations (DI) and green practices (GP) for operational and quality improvement. However, publications explaining how these technologies enable the CBE transition are scarce. This study examines CBE components, common practices of each technology facilitating the CBE transition, problems of solitary technology deployment as well as coupling technologies for the CBE transition.Design/methodology/approachA scoping review was conducted to analyse previous studies in this new field. The data collection is in a quantitative manner, but the data synthesis process follows a similar method of synthesising data in the grounded theory method, which includes familiarisation with the data, open-coding and finalisation of the themes.FindingsCritical components of CBE were identified as biobased goods, industry symbiosis, material resource efficiency, renewable energy, product lifecycle and sharing economy. GP is the most prominent in moderating the CBE transition. We identify each technology has coupled relationships (Lean-4.0, Green-Lean and Green-4.0) technologies facilitated by the circularity concept, which form the core pillars of enablers and advance the CBE paradigm.Research limitations/implicationsThis study demonstrates that combining lean principles with green technology and digital technologies can effectively decrease waste and resource usage in biobased manufacturing processes, therefore endorsing the concept of resource efficiency in circular bioeconomy models.Practical implicationsThe results allow entrepreneurs to strategically incorporate different existing technologies to meet CBE fundamental objectives by initiating it with dual technologies and facilitate industry professionals and regulators to support the improvement of environmental sustainability performance in the manufacturing industry. The management will be able to focus on the common practices across the technologies, which have a dual benefit for both operational and environmental performance.Originality/valueThe paper makes the first attempt to present the synergic impact of the three quality management technologies on a new concept of sustainability, CBE.

Experimental studies of the generating capabilities of mine hoists in industrial conditions

The article is devoted to a topical subject of distributed generation of electrical energy. The cage and skip mine hoists of the iron ore mining enterprise were considered as generators of electrical energy. The purpose of the study is to compare theoretical and experimental data on electricity generation by mine hoisting units of the Sukha Balka PJSK (Kryvyi Rih, Ukraine). The research methods are theoretical ones – on the transformation of the potential energy of a solid body into electrical energy during its movement through a mine shaft, and experimental ones – involving instrumental measurements of the consumption and generation of electrical energy by cage and skip hoists units during industrial operation. Research schedule: collection and processing of information on the operation of the cage and skip hoists of the mine named after Frunze and similar indicators of the cage and skip hoists units of the Yuvileyna mine for the period from September 1 to December 31, 2021. The novelty of the research lies in the instrumental measurement of the electrical parameters of mine hoists in real Ukrainian operating conditions. The most significant results: it was found that the volumes of electricity generation by mine cage hoists significantly exceed the results of skip hoists. The actual indicators of electricity generation by cage hoists units are much lower than the results of theoretical estimates. There is an imbalance between the generation and consumption of energy resources – 9% for the cage elevation of the mine Frunze; about 2% for the cage hoist of the Yuvileyna mine; less than 1% on skip units. It was found that there is a statistically significant correlative relationship between the generation and consumption of electricity by the cage hoist of the mine named after Frunze and no statistically significant relationship between the generation and consumption of electricity by the cage hoist unit of the Yuvileyna mine. The electricity obtained from the daily operation of two cage and two skip units of the Sukha Balka PJSC (approximately 400 kWh) is enough to power 58 average Ukrainian households.

Design, Building and Deployment of Smart Applications for Anomaly Detection and Failure Prediction in Industrial Use Cases

This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidate the decision-making processes of these black box models and evaluate their behaviour. By analysing the inner workings of the models, we aim at providing insights into the factors influencing failure predictions. Through comprehensive experimentation and analysis on sensor data collected from a water pump, this study contributes to the understanding of deep learning methodologies for anomaly detection and failure prediction and underscores the importance of model interpretability in critical applications such as prognostics and health management. Additionally, we specify the architecture for deploying these models in a real environment using the RAI4.0 metamodel, meant for designing, configuring and automatically deploying distributed stream-based industrial applications. Our findings will offer valuable guidance for practitioners seeking to deploy deep learning techniques effectively in predictive maintenance systems, facilitating informed decision-making and enhancing reliability and efficiency in industrial operations.

Tackling toxins: Case studies of industrial pollutants and implications for climate policy

AbstractAs scholars race to address the climate crisis, they have often treated the problem as sui generis and have only rarely sought to learn from prior efforts to make industrial operations greener. In this paper, we consider what can be learned from other shifts away from polluting substances. Drawing on literatures on corporate regulatory strategies and evolving regulatory interactions, we argue for a focus on configurations of regulatory scrutiny and industrial reform, which we then consider through case studies of several major industrial pollutants. We consider the phaseout of ozone‐depleting substances, which has often been cited as a model for mitigating climate change, plus three other cases: per‐ and polyfluorinated alkyl substances (PFAS), leaded fuel, and mercury. We highlight four configurations of regulatory scrutiny and industrial reform: (1) progressive substitution (of ozone‐depleting substances); (2) regrettable substitution (in the first waves of PFAS regulation); (3) knock‐on substitution (in the phaseout of leaded fuel); and (4) narrow substitution (in the case of mercury). These configurations, and the processes that generated them, provide novel lenses for understanding climate mitigation and confronting obstructionism. They point to the diversity of positions that corporate actors may take in the face of potential or actual public regulation, and the possibility of notable divides across and within given industries, which can facilitate meaningful reforms.

An Integrated Approach: A Hybrid Machine Learning Model for the Classification of Unscheduled Stoppages in a Mining Crushing Line Employing Principal Component Analysis and Artificial Neural Networks.

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations.