Search Results (12)

Data Centers (DCs) are critical infrastructures that support the digital world, requiring fast and reliable information transmission for sustainability. Ensuring their reliability and efficiency is essential for minimizing risks and maintaining operations. This study presents a novel availability-driven approach to optimizing maintenance costs in DC Uninterruptible Power Supply (UPS) systems configured in a parallel k-out-of-n arrangement. The model integrates reliability and availability metrics into a dynamic optimization framework, determining the optimal number of components needed to achieve the desired availability while minimizing maintenance costs. Through simulations and a case study by utilizing variable failure rates and monthly maintenance costs, the model achieves a combined system availability of 99.991%, which exceeds the Tier 1 DC requirement of 99.671%. A sensitivity analysis, incorporating ±10% variations in Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), and maintenance costs, was conducted to demonstrate the model’s robustness and adaptability across diverse operational conditions. The analysis also evaluates how different k-out-of-n UPS system configurations influence overall availability and maintenance costs. Additionally, feasible k-out-of-n configurations that achieve the required system availability while balancing operational costs were examined. Furthermore, the optimal number of UPS components and their associated minimum costs were compared across different DC tiers, highlighting the impact of varying availability requirements on maintenance strategies. These results showcase the model’s effectiveness in supporting critical maintenance planning, providing DC managers with a robust tool for balancing operational expenses and uptime. Full article

This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations. Full article

The focus on maintenance actions in the early design phases has been a trend in recent years. The main sources of information during the design of the maintainability process include operator reports, maintainer experience, failure history, and manufacturer recommendations. During this process, an important aspect is related to the configuration of maintenance tasks and interventions, such as their main phases, activities, and durations. The allocation or estimation of maintainability involves identifying and/or estimating the mean time to repair (MTTR) for each component or system. The time of the maintenance tasks or the repair time are fundamental for companies, as the availability of equipment directly depends on this parameter. In this study, a new method for evaluating maintainability during the design phases is proposed. The method is based on the integration of the maintenance task analysis (MTA) principles and the UNE 151001 maintainability evaluation standard. A data structure is proposed that serves the application of the UNE151001 procedures, obtaining a data-based maintainability evaluation. As a validation procedure, an application of the proposed approach is presented using two overhead cranes. Comparisons and recommendations are made regarding the maintainability of both pieces of equipment. Finally, some managerial and engineering insights are presented. Full article

This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to production. However, when a machine or component gets deteriorated, the mini-term also suffers deterioration, allowing it to be a multifunctional indicator for the prediction of machine failures as well as measurement of production. Currently, in Industry 4.0, one of the handicaps is Big Data and Data Analysis. However, in the case of predictive maintenance, the need to install sensors in the machines means that when the proposed scientific solutions reach the industry, they cannot be carried out massively due to the high cost this entails. The advantage introduced by the mini-term is that it can be implemented in an easy and simple way in pre-installed systems since you only need to program a timer in the PLC or PC that controls the line/machine in the production line, allowing, according to the authors’ knowledge, to build industrial Big Data on predictive maintenance for the first time, which is called Miniterm 4.0. This article shows evidence of the important improvements generated by the use of Miniterm 4.0 in a factory. At the end of the paper we show the evolution of TAV (Technical availability), Mean Time To Repair (MTTR), EM (Number of Work order (Emergency Orders/line Stop)) and OM (Labour hours in EM) showing a very important improvement as the number of mini-terms was increased and the Miniterm 4.0 system became more reliable. In particular, TAV is increased by 15%, OM is reduced in 5000 orders, MTTR is reduced in 2 h and there are produced 3000 orders less than when mini-terms did not exist. At the end of the article we discuss the benefits and limitations of the mini-terms and we show the conclusions and future works. Full article

In this paper, renewable resources, namely photovoltaic panels (PV), are placed in a specific configuration to obtain the maximum reliability and availability of a microgrid and study the subcomponent-level reliability and availability. The reliability of components can be increased by trying different configurations of the components. We identify the preferred configuration used for the PV panels as bridged linked. The overall reliability of the microgrid is increased when component-wise reliability is considered. Even components are further divided into subcomponents, and the multiple faults of each component are considered. The method used for the reliability evaluation and availability study is Markov state transition modeling. The microgrid’s reliability and availability are plotted concerning time using Matlab. The optimization of reliability and availability is conducted through optimization techniques such as the genetic algorithm (GA) and artificial neural networks (ANN). The results are compared and validated for the optimal values of mean time to failure (MTTF) and mean time to repair (MTTR). Using a genetic algorithm, there is a 96% of improvement in the reliability, and after applying the neural networks, a significant improvement of 97% along with quick results is achieved. Full article

Good marine practice and proper operation of navigation, radio navigation, and radio communication systems are nowadays of key importance for marine navigation safety. This applies to merchant vessels and navy ships and effectively monitoring unmanned vehicles along a set route. Technological progress has contributed to developing equipment for ships and unmanned vehicles, which are fitted out with devices of the automatic identification system (AIS). One of the issues in AIS operation is limited-service availability, which manifests itself in the presence of incomplete data for the navigation parameters sent by radio, compressed in dynamic data messages. This results in the unusability of the system information for ships equipped with an AIS transponder. This paper aims to develop an AIS service availability model based on the mean time of incomplete navigation parameter occurrence in AIS data messages and to present the test results in the time and frequency domain using a mathematical method—Fast Fourier Transform. The study results refer to five basic navigation parameters and are indicative of a high service availability index—over 0.99 for three out of the five navigation parameters tested. Data recorded by a ship’s system receiver were the key source of practical knowledge concerning the limitations of AIS service availability. The experiment revealed interruptions in regular data inflow from navigation devices. In effect, a description was provided of a functional relationship based on a spectral analysis of the frequencies of the times occurring between service repair (time to repair—TTR), and the use of the model to analyze other variables was proposed. Full article

Due to technological enhancements, traditional, qualitative decision-making methods are usually replaced by data-driven decision-making even in smaller companies. Process simulation is one of these solutions, which can help companies avoid costly failures as well as evaluate positive or negative effects. The reason for this paper is twofold: first, authors conducted a Quality Function Deployment analysis to find the most vital reliability indicators in the field of production scheduling. The importance was acquired from the meta-analysis of papers published in major journals. The authors found 3 indicators to be the most important: mean time between failure (MTBF), mean repair time and mean downtime. The second part of the research is for the implementation of these indicators to the stochastic environment: possible means of application are proposed, confirming the finding with a case study in which 100 products must be produced. The database created from the simulation is analyzed in terms of major production KPIs, such as production quantity, total process time and efficiency of the production. The results of the study show that calculating with reliability issues in production during the negotiation of a production deadline supports business excellence. Full article

Plastic packaging of food products has a significant impact on the sustainability of the food industry and trade. The article presents selected problems surrounding the production of plastic packaging for food storage and distribution that might cause disruptions in the implementation of sustainable production. An important question regards the extent to which the industry that produces this packaging complies with the sustainability requirements. The present work consists in an investigation of the problems observed in a plastic packaging manufacturing company located in Poland, which is part of a global corporation. Plastic waste management was analyzed and compared with the requirements of a closed loop economy. The quantities of raw material processed and the quantities of waste in the defined period were analyzed. During the analyzed period, 0.05% of the monthly production was non-recyclable waste. The quality of raw material seems to be responsible for the majority of wastes. Therefore, the important role of SAP (Systems Applications and Products) in the use of lower quality batches of raw material is indicated. On the other hand, a possibility of converting the wastes into liquid or gaseous fuels is suggested. In addition, the paper investigates the efficiency of machine use in a film bag production line in a three-shift system. Process losses were analyzed and reliability indicators such as overall equipment efficiency (OEE) and its components, mean time between failures (MTBF), and mean failure repair time (MTTR) were determined. The monthly OEE values for each change individually exceeded 80%. Full article

Continuity is one of the metrics that characterize the required navigation performance of global navigation satellite system (GNSS)-based applications. Data outage due to receiver failure is one of the reasons for continuity loss. Although a multi-receiver configuration can maintain position solutions in case a receiver has data outage, the initialization of the receiver will also cause continuous high-precision positioning performance loss. To maintain continuous high-precision positioning performance of real-time precise point positioning (RT-PPP), we proposed a continuity enhancement method for RT-PPP based on zero-baseline constraint of multi-receiver. On the one hand, the mean time to repair (MTTR) of the multi-receiver configuration is improved to maintain continuous position solutions. On the other hand, the zero-baseline constraint of multi-receiver including between-satellite single-differenced (BSSD) ambiguities, zenith troposphere wet delay (ZWD), and their suitable stochastic models are constructed to achieve instantaneous initialization of back-up receiver. Through static and kinematic experiments based on real data, the effectiveness and robustness of proposed method are evaluated comprehensively. The experiment results show that the relationship including BSSD ambiguities and ZWD between receivers can be determined reliably based on zero-baseline constraint, and the instantaneous initialization can be achieved without high-precision positioning continuity loss in the multi-receiver RT-PPP processing. Full article

In conventional reliability analysis, the duration of interruptions relied on the input parameter of mean time to repair (MTTR) values in the network components. For certain criteria without network automation, reconfiguration functionalities and/or energy regulator requirements to protect customers from long excessive duration of interruptions, the use of MTTR input seems reasonable. Since modern distribution networks are shifting towards smart grid, some factors must be considered in the reliability assessment process. For networks that apply reconfiguration functionalities and/or network automation, the duration of interruptions experienced by a customer due to faulty network components should be addressed with an automation switch or manual action time that does not exceed the regulator supply restoration time. Hence, this paper introduces a comprehensive methodology of substituting MTTR with maximum action time required to replace/repair a network component and to restore customer duration of interruption with maximum network reconfiguration time based on energy regulator supply requirements. The Monte Carlo simulation (MCS) technique was applied to medium voltage (MV) suburban networks to estimate system-related reliability indices. In this analysis, the purposed method substitutes all MTTR values with time to supply (TTS), which correspond with the UK Guaranteed Standard of Performance (GSP-UK), by the condition of the MTTR value being higher than TTS value. It is nearly impossible for all components to have a quick repairing time, only components on the main feeder were selected for time substitution. Various scenarios were analysed, and the outcomes reflected the applicability of reconfiguration and the replace/repair time of network component. Theoretically, the network reconfiguration (option 1) and component replacement (option 2) with the same amount of repair time should produce exactly the same outputs. However, in simulation, these two options yield different outputs in terms of number and duration of interruptions. Each scenario has its advantages and disadvantages, in which the distribution network operators (DNOs) were selected based on their operating conditions and requirements. The regulator reliability-based network operation is more applicable than power loss-based network operation in counties that employed energy regulator requirements (e.g., GSP-UK) or areas with many factories that required a reliable continuous supply. Full article

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines. Full article

This paper presents a specific method to improve the reliability of the equipment and the quality of power supplied to the electrical systems with the frequency and voltage control of a thermoelectric plant, to guarantee a more stable system. The method has the novelty of combining Total Productive Maintenance (TPM) using only four pillars, with Electrical Predictive Maintenance based in failure analysis and diagnostic. It prevents voltage drops caused by excessive reactive consumption, thus guaranteeing the company a perfect functioning of its equipment and providing a longer life of them. The Maintenance Management Program (MMP) seeks to prevent failures from causing the equipment to be shut down from the electrical system, which means large financial losses, either by reducing billing or by paying fines to the regulatory agency, in addition to prejudice the reliability of the system. Using management tools, but applying only four TPM pillars, it was possible to achieve innovation in power plants with internal combustion engines. This study aims to provide maintenance with a more reliable process, through the implantation of measurement, control and diagnostic devices, thus allowing the management to reduce breakdown of plant equipment. Some results have been achieved after the implementation, such as reduction of annual maintenance cost, reduction of corrective maintenance, increase of MTBF (Mean Time between Failures) and reduction of MTTR (Mean Time to Repair) in all areas. Probabilistic models able to describe real processes in a more realistic way, and facilitate the optimization at maximum reliability or minimum costs are presented. Such results are reflected in more reliable and continual power generation. Full article