Production Equipment Maintenance Research Articles

Predictive Maintenance with Linguistic Text Mining

The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency.

Air quality impacts from the development of unconventional oil and gas well pads: Air toxics and other volatile organic compounds

Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production. These operations offer an especially useful case to study as several management practices were implemented to reduce emissions (e.g., electrified, grid-powered drill rigs and closed loop fluid handling systems to reduce truck traffic and limit fluid handling on the pad). With simultaneous measurements of methane and 50 VOCs from October 2018 to December 2022 at as many as 19 sites near well pads, in adjacent neighborhoods, and at a more distant reference location, we identify impacts from each phase of well development and production. Use of weekly, time-integrated canisters, a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous photoionization detectors (PID) to trigger canister collection upon detection of VOC-rich plumes, and an instrumented vehicle, provided a powerful suite of measurements to characterize both transient plumes and longer-term changes in air quality. Prior to the start of well development, VOC gradients were small across Broomfield. Once drilling commenced, concentrations of oil and gas (O&G) related VOCs, including alkanes and aromatics, increased around active well pads. Concentration increases were clearly apparent during certain operations, including drilling, coil tubing/millout operations, and production tubing installation. Emissions of C8–C10 n-alkanes during drilling operations highlighted the importance of VOC emissions from synthetic drilling mud chosen to reduce odor impacts. More than 90 samples were collected of transient plumes. Using composition measurements, meteorological data, and information about well pad activities, these plumes were connected with specific UOGD operations including drilling, flowback, and production equipment maintenance. The chemical signatures of these plumes differed by operation type (e.g., C8–C10 n-alkanes constituted a larger fraction of measured VOCs in drilling-related plumes). Concentrations of individual, oil and gas-related VOCs in these plumes were often several orders of magnitude higher than in background air, with maximum ethane and benzene concentrations of 79,600 and 819 ppbv, respectively. Because these plumes typically impact a monitoring site for just several minutes, they are easily missed by slower-responding instruments. Study measurements highlight future emission mitigation opportunities during UOGD operations, including better control of emissions from shakers that separate drill cuttings from drilling mud, production separator maintenance operations, and periodic emptying of sand cans during flowback operations.

An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair

Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time and the appropriate selection of the scope of maintenance activities are necessary not only for the operation of industrial equipment but also for effective planning of the demand for own maintenance resources (spare parts, people, finances). A number of studies have been conducted in the last decade and many attempts have been made to use artificial intelligence (AI) techniques to model and manage maintenance. The aim of the article is to discuss the possibility of using AI methods and techniques to anticipate possible failures and respond to them in advance by carrying out maintenance activities in an appropriate and timely manner. The indirect aim of these studies is to achieve more effective management of maintenance activities. The main method applied is computational analysis and simulation based on the real industrial data set. The main results show that the effective use of preventive maintenance requires large amounts of reliable annotated sensor data and well-trained machine-learning algorithms. Scientific and technical development of the above-mentioned group of solutions should be implemented in such a way that they can be used by companies of equal size and with different production profiles. Even relatively simple solutions as presented in the article can be helpful here, offering high efficiency at low implementation costs.

Text mining techniques for the management of predictive maintenance

The advent of Industry 4.0 provides new opportunities to improve the maintenance of production equipment from both the technical and managerial perspective. In this paper, we propose a contribution in the direction of predictive maintenance of machine tools based on the integration of a text mining algorithm with the cyber-physical system of a manufacturing industry. The system performs its analysis starting from data stored in log files maintained by a machine tool returning an alert about a future potential machine failure. Log files, produced by part programs running on the machine control system, record the status of execution parameters taken by key sensors or derived by the control system during the part program execution. Historical data are collected by means of Digital Twin technologies and then analyzed using computational linguistic techniques so that we can predict a machine failure in the imminent future starting from data collected in the past. The paper first describes a new scheme for the classification of maintenance approaches. Then, starting from the proposed cyber-physical system model, an algorithm for predictive maintenance based on text mining technology is integrated in it. The implemented tool supports the maintenance manager in making the most appropriate decisions about the scheduling of maintenance activities when there is an alert about a possible machine failure.

Enhancing Productivity of CNC Machines by Total Productive Maintenance (TPM) implementation. A Case Study

Lean manufacturing is a business system that contributes to the growth of production efficiency and the company profit, by identifying and eliminating waste. TPM is one of the techniques used to eliminate production waste, being focused on the maintenance of production equipment, so as to increase their operation and efficiency, while ensuring the quality of production due to preventing the causes that lead to waste. In the present paper for the continuous improvement of the manufacturing process within the work cells equipped with CNC processing centers, TPM is implemented, which contributes to the optimization of predictive, preventive and corrective maintenance activities of production equipment, thus improving the efficiency of machine tools. TPM ensures maximum equipment efficiency due to the elimination of unforeseen equipment downtime, respectively increasing the speed and availability of the machine. The research is based on a case study from automotive industry. Monitoring the daily activities of the equipment is done through the TPM component, namely Overall Equipment Effectiveness (OEE) which presents the situation in the manufacturing process. The waste related to technological equipment were identified, such as: incorrect operation of the work equipment, unplanned shutdowns, respectively damages that occurred in the manufacturing process.

Innovative Approach to Preventive Maintenance of Production Equipment Based on a Modified TPM Methodology for Industry 4.0

Preventive maintenance (PM) in the production industry is one of the most essential measures to eliminate accidental machinery failures by replacing/repairing worn out machines or parts. The decision of when and where to perform preventive maintenance is non-trivial due to the complex and stochastic nature of the industry where PM is implemented. This article deals with the theoretical and practical implementation of preventive maintenance based on a unique modification of the total productive maintenance (TPM) methodology. The innovative approach of preventive maintenance management was implemented in the real production hall of ITT (Czech Republic) and has been verified. Within preventive maintenance, the new concept brings in an innovative method of managing the maintenance process as a whole, from abstract methodical conception to practical usage. The whole new approach has been verified and implemented on industrial equipment. A challenging task while implementing Industry 4.0 technologies is the issue of how to fully gather and analyse operational data from various items of equipment and users under various conditions, which would result in innovative services of equipment maintenance for clients. The solution to this problem is based on an innovative approach to preventive maintenance of complex equipment and could help many industrial companies to increase production and maintenance efficiency.

A predictive maintenance system for multi-granularity faults based on AdaBelief-BP neural network and fuzzy decision making

Predictive maintenance of production equipment is a prerequisite to ensure safe and reliable manufacturing operations. To avoid unexpected shutdown and even casualties caused by faults during production, it is paramount to design an effective predictive maintenance decision system for production equipment. Most of the related research works concentrate on early warn of specific faults but neglect the differentiations of the fault severity. To address the issue, this paper presents an intelligent predictive maintenance system for multi-granularity faults of production equipment based on the AdaBelief-BP (back propagation) neural network and the fuzzy decision making. The characteristics of the system presented in this paper include: (1) The proposed system implements a two-stage framework, integrating the functions of fault type prediction and fault degree prediction, which can provide comprehensive fault information throughout production lifecycles; (2) On the maintenance solution identification stage, fuzzy logic-based decision making is carried out to determine appropriate maintenance solutions based on the practical vague boundary of fault severity. In the system, the design of the AdaBelief-BP neural network can achieve a higher convergence rate and a better generalization capability as well. Meanwhile, to the best of our knowledge, in this research, it is the first time to use the migration of the fuzzy membership degree as the indicator of the changing condition of fault severity to facilitate more accurate maintenance solution identification. To verify the effectiveness of the system, comparison experiments with some popular algorithms are conducted. Benchmarking results show that the developed system can achieve higher prediction accuracy as well as higher efficiency than the comparative methods.

Perencanaan Strategi Preventive Maintenance Pada Mesin Shot Blasting di PT. ABC dengan Klasifikasi ISMO

Mesin shot blasting merupakan mesin pembersih produk hasil pengecoran dengan mekanisme shoting bola baja dari gaya sentrifugal yang dihasilkan putaran impeller/blast wheel. Sebagai mesin utama dalam proses akhir kegiatan produksi pengecoran, penurunan kehandalan mesin sangat berdampak negatif pada sistem produksi. Saat ini strategi perawatan peralatan produksi di PT ABC masih menerapkan corrective maintenance yang pelaksanaan perawatnnya tidak terencana dan bersifat reaktif saat kegagalan fungsi mesin terjadi. Hal ini mengakibatkan tingginya production loss dan biaya perawatan meningkat. Oleh karena itu strategi perawatan pada mesin shot blasting diubah menjadi jenis perawatan pencegahan (preventive maintenance). Kajian ini bertujuan untuk merencanakan sistem preventive maintenance (PM) mesin-mesin shot blasting PT. ABC berupa pembuatan jadwal PM, perhitungan waktu downtime dan kebutuhan teknisi pada setiap kegiatan PM_nya. Perencanaan sistem PM dicapai menggunakan metode ISMO (Inspection, Small repair, Medium repair dan Overhaul) dan dilakukan dalam beberapa tahapan yaitu identifikasi obyek mesin, membuat spesifikasi kerja PM, menentukan nilai kerumitan dan siklus pemeliharaan, perhitungan tenaga kerja dan waktu downtime mesin, membuat jadwal tahunan dan bulanan, serta beberapa dokumen perawatan lainnya. Mekanisme penentuan nilai kerumitan, ditentukan berdasarkan kemiripan spesifikasi mesin yang terdapat pada metodologi HP Garg. Perencanaan sistem PM mesin shot blasting telah berhasil dilakukan, beberapa informasi PM yang diperoleh yaitu (i)siklus perawatan mesin adalah C - I1 - I2 - S1 - I3 - I4 - M1 - I5 - I6 - S2 - I7 - I8 - M2 - I9 - I10 - S3 - I11 - I12 – C, (ii) interval waktu antar kegiatan perawatan sebesar 1 bulan, (iii) waktu downtime kegiatan ISMO untuk mesin drum shot, batch shot dan hanger shot, (iv) jumlah dan level teknisi perawatan yang dibutuhkan pada setiap kegiatan ISMO untuk mesin drum shot, batch shot dan hanger shot. Verifikasi perhitungan nilai availability mesin shot blasting pada usulan jadwal PM diperoleh bahwa availability mesin meningkat menjadi lebih dari 80%.

Quality Maintenance of Production Equipment: A Case Study of Bendel Feeds and Mills, Ewu, Edo State

This paper analyses the quality maintenance in Bendel Feeds and Mills, Ewu, Edo State, with a view of designing a robust maintenance process and schedule to enhance production equipment in the company. The specific objectives were to determine the failure rate in the company production equipment, establish the appropriate maintenance process needed to reduce failure rate, investigate whether the current material handling methods and practices permit high maintenance turnover and to proffer useful suggestions and ideas on how to effectively maintain production equipment in the company. Data obtained were used to analyse the materials handling equipment failure rate, MTTR, MTBF, availability, failure mode and work station. The results showed that the failure rate is more in bucket elevator equipment than other materials handling equipment. It was observed that the plant organisational structure is area controlled instead of centre controlled organisation, which is one of the problems of the plant maintenance policy adopted by the company. Maintenance of materials handling equipment of the plant can be better achieved through modified planned preventive maintenance, condition base maintenance (predictive) and equipment inspection. This analysis will be of tremendous help to practicing maintenance engineering.

Production machines maintenance based on digitalization

Introduction.Digital data and analytics transform the role of the production equipment maintenance. Analytical information of sensors placed on the product allows continuous monitoring of the production machines operation and their timely servicing. Thus, defects in technical equipment are identified, the analysis of which enables to develop algorithms for monitoring and forecasting, and to prevent equipment from overshooting the limits of the safe operation.Materials and Methods.Basic digitalization principles and the digital images structure are presented. A mathematical method is used to describe the digital image vector and the control system algorithm.Research Results.The achievements of the known systems of maintenance and digitalization of various machines are summarized. The application of a dynamic digital image made it possible to determine the desired levels of the production facilities maintenance. An optional version of monitoring the equipment state within the framework of the production digitalization concept is shown. It is based on the proposed algorithm for an autonomous control of the process state.Discussion and Conclusions.The construction of machine digital images in accordance with the main stages of its life cycle is described. The task of automated maintenance of machine tools based on digitalization is considered.

DEVELOPMENT OF A MULTI-AGENT COLLISION RESOLUTION SYSTEM AT THE SUPPLY OF SPARE PARTS AND COMPONENTS TO THE PRODUCTION EQUIPMENT OF INDUSTRIAL ENTERPRISES

The approach to the creation of computer facilities for the automation of the technical maintenance of production equipment (TMPE) at industrial enterprises (IE) is outlined. Meaningful and formal statement of the problem of forming solutions for identifying and eliminating collisions that arise when delivering spare parts and components for TMPE are presented. The method of formation of coordinating decisions on maintenance with spare parts and accessories for carrying out TMPE at IE is described. The organization of intellectual support of formation of coordinating decisions by recognition of potential collision in the TMPE process is offered. This procedure involves checking the real existence of the collision and issuing a coordinating decision. In this case, the decision is formed in the event of a disagreement between the need for spare parts and components for the TMPE maintenance, with their availability in the PP warehouse. The ways of software implementation of this method in the environment of multi-agent system are considered. In particular, the description of the multi-agent system developed during the prototype research is given. The prototype is implemented using CORBA technology, in accordance with DSTU ISO/ EC 2382-15:2005. The calculation of the efficiency of the application of the developed computer tools in production is shown. To assess the quality of the system, a sliding control method based on leave-on-out cross-validation (LOOCV) is applied.

Harga Pokok Produksi Dalam Menentukan Harga Jual Melalui Metode Cost Plus Pricing Dengan Pendekatan Full Costing

The purpose of this study is to determine the calculation of the cost of production and selling price setting process at PT. Istiqamah Prima Sejahtera. Calculating the cost of production for the full costing method and setting the selling price using a cost plus pricing. This type of research is quantitative research. Based on the characteristics of the issues raised by the researchers, the study is classified as a quantitative descriptive research. The data used in this study are primary data obtained directly from the company such as data from interviews with the company and the data contains information on the production costs of companies during the month of September 2016. As for the secondary data obtained from books, journals, internet or other media which supported this research. From the analysis of the data, the results showed that the company's calculation of the cost of production is lower than the production cost price calculation using a full costing method. Cost of production is calculated using a method that is equal Rp85.472 company and according to the full costing method that is equal to Rp85.962. This is because in the calculation of factory overhead cost companies do not take into account some costs into the cost of production as the cost of maintenance and maintenance of production equipment, and the cost of depreciation on a shredded fish product. In addition, setting the selling price of the company only to estimate the selling price calculations per kg of shredded fish with a mark-up rate of 40%, amounting to Rp120,000 to set the selling price on the packaging of 100 grams, 250 grams and 500 grams. While using the cost plus pricing method with a mark up of 40% lower than the selling price according to the company in the amount of USD 12 683. (100 grams), Rp 30,488 (250 grams), and USD 60 798 (500 grams). So, setting the selling price should be done precisely because the price is too high will make the product less competitive, while the sale price is too low will lead to losses for the company.

Maintenance Management Systems in the Czech Enterprises of Chemical and Food Industries

Purpose of the article: The aim of this article is to use the results of the qualitative research to describe the current form of maintenance systems used in enterprises of chemical and food industries, and then to discuss and generalize the results. Methodology/methods: The primary qualitative research was conducted in five companies, which can be regarded as typical representatives of the industries. The main objective of the primary research in individual companies was to determine how they perform the strategic and tactical operational planning of maintenance, implementation of these plans and their control. Individual interviews with respondents were used as the research method. The results of the research were processed using the content analysis method. Subsequently, comparison of the findings from individual businesses and subsequent synthesis thereof was performed, which allowed making generalizations. Scientific aim: The scientific aim of the article is to develop knowledge in the field of maintenance management by specifying the form of the maintenance systems utilized in Czech enterprises of the chemical and food industries and identifying the main opportunities for their improvement. Findings: Czech enterprises of the chemical and food industries utilize maintenance management systems. These systems are aimed at prevention, emphasizing the planning of maintenance activities in fixed periodic intervals. Also, they often utilize diagnostic maintenance. However, the maintenance systems currently used cannot be considered fully operational, with regard to the relatively large volume of after-failure repairs. Conclusions: Production equipment maintenance systems are irreplaceable in chemical and food industry enterprises, but there is great potential for improvement. Improvement should be focused on the area of strategic as well as tactical and operational planning of production equipment maintenance. In terms of strategy, the biggest opportunity for a dramatic improvement is the implementation of modern maintenance methods. In the context of the existing systems, improvement can be achieved especially in setting strategic objectives, the system of indicators to measure the performance of maintenance and in implementing an information system. In the tactical and operational level, it would be useful to develop a maintenance planning system linked with suppliers and customers and utilize mathematical methods and models to optimize plans for this business activity

Vibroacoustics Analysis of Punching Machine Hydraulic Piping

The reduction of elevated dynamic loads, affecting the elements of the hydraulic drive, is an important task in production equipment maintenance. In case of the hydraulic drive with actuators, remotely located from the power source, the piping defines the working capacity of the whole system. Hence the development of piping vibroacoustic analysis methods appears to be the vital task. The said methods would not only help to define the cause of increased loads, but also permit to develop an adequate design solution without prolonged experimental checks.The article proposes the method of pipeline system vibroacoustics analysis through the example of press-forge unit drain piping. The novelty of this method consists in the use of the fluid pulsation and piping vibration spectra analysis in their correlative comparison with account to the calculated modal parameters of a pipeline.The measurements of working fluid pressure pulsation and pipeline vibration in the vicinity of the breakage locations were carried out. The experimental data spectrum analysis indicated an apparition of intense water-hammer process along with the frequency coalescence of fluid pulsation and pipeline vibration. The resonant vibration amplification, caused by the water-hammer effect, was suggested. The pipeline system mathematical model, allowing to calculate its modal parameters, was derived. The comparison of the modal analysis with the vibroacoustic response confirmed the hypothesis of the pipeline resonant breakdown.The article depicts, that the study of the pipeline system vibroacoustic response, based only on Fourier analysis of the working fluid pulsation and pipeline vibration, is prone to fail the reliable causation of elevated dynamic loads. It is suggested to complement the vibroacoustic response analysis with the modal parameters calculation. The mathematical computation of the modal frequencies and shapes was implemented instead of their experimental observation in order to exclude the undesired signal, introduced with attached mechanical equipment. On top of that, the mathematical computation of the pipeline modal parameters allowed to perform the pipeline modal frequency shift from the water-hammer effect frequency range through cautious relocation of additional supports.

Economic aspects of the Kr-Xe mixture production and enrichment

The present work deals with studying the structure of costs for extracting krypton-xenon mixture, produced as a byproduct of separation of air and krypton-xenon concentrate after its enrichment in individual plants. Reducing the costs of manufacturing products allows to reduce their prime cost and therefore increase the profits of the enterprise.As the object of investigation, oxygen production of large metallurgical enterprise was considered. Comparison of the known methods for calculating the prime cost of air separation byproducts was conducted, the main sources of costs for producing krypton-xenon mixture was analyzed. Components of the prime cost of krypton-xenon concentrate, produced during its enrichment in “Chrome”-type plants were investigated.Calculations have shown that energy costs (70-80 % of total costs) make the main contribution to the prime cost of the krypton-xenon mixture, produced in air separation plants. General production equipment maintenance costs and expenses constitute 20-30%. Contribution of wages fund is from 2 to 5%, depending on the wage level, adopted in the metallurgical industry.In enriching the krypton-xenon primary mixture in “Chrome”-type plants, energy costs (60% of all expenses) are also determining. Among them, 7% accounts for the production krypton-xenon concentrate compression. Wages are about 17% of total costs. Share of original Kr-Xe mixture in the prime cost of concentrate is about 9%.Studies have allowed to define “critical” items of expenses, changing which most affects the prime cost of intermediate mixtures and, finally, krypton and high-purity xenon sales profit margin.

A Conceptual Agent-based Model to Supporting the Production Equipment Technical Service and Repair Organization

Abstract The paper presents a solution of the maintenance and repair organization (MRO) problem based on multi-agent technology. A generalized model of intelligent agent activity in road maintenance and repair techniques developed. An analysis of existing architectures agents has been executed. The hybrid InteRRaP agent architecture with internal simulation subsystem constituting a simulation engine described (it is a complex system dynamic models). The agent simulation apparatus implementation approaches based on the system-dynamic models in the multiagent environment proposed. The structure, basic subsystems and mechanisms of agents’ interaction described. The stages of the continuous organizational process of production equipment maintenance and repair are shown. The proposed system allows obtaining more rational MRO.

Research on Condition Monitoring and Fault Diagnosis of Metallurgical Machinery and Equipment

Using the signal amplitude domain, frequency domain, bearing shock pulse measurement of bearings, gear boxes, motors and other rotating machinery vibration parameters for key components, through the accumulation of historical data and diagnosis, to predict in advance of equipment failure, for maintenance work to help provide a reliable and assurance purposes. The results show that the system does for the routine maintenance of production equipment has played a guiding role.

3422 加工条件の最適化と機械要素の蘇生化再活用技術

"With regard to the maintenance of production equipment, while firms are producing products effectively with appropriate quality utilizing the minimum amount of resources and environmental loads, knowledge and technology for attainable zero-emission/zero loss production are being sought. In this article, an outline of the "Optimization of cutting conditions" and "Revitalizing and re-utilizing technology" for replacement parts for zero loss production will be described."

Minimal repair and readjustment actions for production equipment maintenance

A multicomponent production equipment is analyzed that requires occasional overhauls and readjustments to cut back increasing runtime costs. In addition stochastic failures of components are reset by so—called minimal repair actions. The occurence of failures as well as the initial runtime costs form independent stochastic processes. Using renewal theoretic arguments the behaviour of the equipment is mathematically evaluated. For a generalized loss structure sufficiency criteria are derived to provide for maximum availability, minimum costs, and maximum returns (using a costs dependent policy) in the long run.