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Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection. [ABSTRACT FROM AUTHOR]

SMEs play a vital role in driving economic growth, creating jobs, and fostering innovation. However, unlike larger businesses, SMEs often struggle to adopt Industry 4.0 technologies due to limited resources. Addressing these challenges is essential to help SMEs leverage advanced technologies, enhance competitiveness, and support economic development. This paper presents a framework for SMEs to adopt Industry 4.0 technologies in maintenance operations. The framework leverages Reliability, Availability, Maintainability, Safety, and Sustainability (RAMS2) benefits and offers optimization opportunities to enhance production efficiency, reduce costs, and improve product quality. Based on the comprehensive literature review the gaps are identified and technical components of the proposed framework are matched with RAMS2 objectives. A case study illustrates its practical application, including a mathematical model to balance cost and reliability in maintenance. The proposed framework, compared to traditional systems, provides SMEs with a competitive edge by achieving operational and financial objectives. [ABSTRACT FROM AUTHOR]

Refactoring is a technique for changing internal attributes without affecting external ones in an optimized manner. Bad smells in the source code can cause various issues, increasing the need for refactoring. In this study, prioritization of classes is initially performed using a newly proposed metric called the Quality Decline Factor (QDF), which considers an appropriate ratio of software metrics along with eleven detected types of bad smells. Next, these bad smells are addressed by applying refactoring techniques, and changes in the metrics are observed. Subsequently, machine learning algorithms are used to assign weights to each metric, leading to the proposal of another new metric, the Total Refactoring Index (TRI). TRI combines the assigned weights and the effects of metric changes to determine the optimal refactoring sequence. The results indicate that the Decision Tree Forest algorithm is the most suitable for determining the refactoring sequence. After applying this technique, a 94.9% reduction in effort was observed. This study would benefit software maintainers by providing predefined sequences, allowing them to focus only on the code sections with the highest concentration of bad smells, thus completing projects within real-time constraints. [ABSTRACT FROM AUTHOR]

The presented studies made it possible to clarify the calculation model of the diesel engine clutch with packages of sleeve springs. The stressed state of the spring packets is considered, while it is proposed to place the point of application of the radial force, which bends the spring package, in the zone of its greatest flexibility. Calculations performed according to the obtained expressions showed a coincidence with the results of experimental studies by other authors within 4% and with simulations conducted using automated design systems.

The aim of the present study is to investigate reliability, availability, maintainability, and dependability (RAMD) of crystallization system of a sugar production plant. Previous studies attentive on the reliability and availability analysis of sugar plants specially its subsystems like evaporation units. This study is focus on the RAMD analysis of the crystallization system of sugar plant having four subsystems with different number of components. Failure and repair rates of all subsystems are taken as exponentially distributed. The transition diagram and ChapmanKolmogorov differential equations for each subsystem are derived by using Markov birth-death process. For all four subsystems, reliability, availability, mean time between failure (MTBF), mean time to repair (MTTR), and dependability ratio are computed using simple probabilistic concepts. The effect of change in failure rates of subsystem in system performance is also observed. It is shown that the crystallization subsystem found to be more sensitive among four subsystems from reliability point of view. This study can be helpful to system designer for further modeling/designing of reliable systems and enhancement in system’s performance through planning efficient maintenance strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the reliability, availability and maintainability (RAM) characteristics of a in different systems of the process industries. Critical mechanical subsystems with respect to failure frequency, reliability and maintainability are identified for taking necessary measures for enhancing availability of the respective industries. As complexity of the systems increasing across the various sectors so performance evaluation becomes necessary for the smooth functioning of all the systems of respective industry. The study explores the evolution of RAM approaches over time, highlighting their significance in ensuring the efficient operation of intricate systems. It provides an overview of the historical development and current state of RAM practices in the complex system of the industries. A comprehensive review of academic literature from the past two decades, including books, journals, and scholarly articles, is conducted to expand the analysis, mainly focus on the evaluating RAM methodology in diverse industrial contexts, different complex system and other process industrie [ABSTRACT FROM AUTHOR]

Software bug prediction is a software maintenance technique used to predict the occurrences of bugs in the early stages of the software development process. Early prediction of bugs can reduce the overall cost of software and increase its reliability. Machine learning approaches have recently offered several prediction methods to improve software quality. This paper empirically investigates eight well-known machine learning and deep learning algorithms for software bug prediction. We compare the created models using different evaluation metrics and a well-accepted dataset to make the study results more reliable. This study uses a large dataset collected from five publicly available bug datasets that includes about 60 software metrics. The source-code metrics of internal class quality, including cohesion, coupling, complexity, documentation inheritance, and size metrics, were used as features to predict buggy and non-buggy classes. Four performance metrics, namely accuracy, macro F1 score, weighted F1 score, and binary F1 score, are considered to quantitatively evaluate and compare the performance of the constructed bug prediction models. The results demonstrate that the deep learning model (LSTM) outperforms all other models across these metrics, achieving an accuracy of 0.87. [ABSTRACT FROM AUTHOR]

Copyright of Modern Urban Transit is the property of Modern Urban Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimating the reliability and maintainability (R & M) parameters is crucial in various industrial applications. It serves purposes such as evaluating system performance and safety, minimising the risk and cost of potential failures, and designing efficient maintenance strategies. This task becomes challenging for complex repairable systems, where failures can occur due to different causes and performance may be affected by various covariates (such as material, environment, and labour). Another challenge in R & M studies arises from the presence of censorship in failure times. Existing methodologies often fail to account for all the aforementioned aspects of system‐related data in R & M analysis. By incorporating valuable information from covariates and utilising data from censored failure times alongside complete failure data, the accuracy of R & M parameter estimation can be significantly improved. This paper develops reliability models for repairable systems with multiple failure causes in the presence of covariates. The system can also be subject to imperfect maintenance. The R & M parameters are then estimated by applying the Kijima Type I and II model's virtual age concept. The proposed technique is illustrated using two case studies on gas pipelines and aero‐engine systems. Through these case studies, we show that the proposed method not only provides more efficient estimates of the R & M parameters compared to the alternative approach, but it is also easier to apply and yields more straightforward interpretations. [ABSTRACT FROM AUTHOR]

Inherent uncertainties of biomass-driven systems including seasonality, supply chain problems, and energy conversion limitations put reliability and availability of such systems under question. The optimization of the energy systems taken into account the reliability, availability and maintainability (denoted by RAM), parameters, and constraints can dramatically change the system design, configuration, and operation. An enviro-economic optimization of biomass-powered energy systems, considering the impact of the reliability and maintainability parameters in the final optimal cost of the energy generation and after-commissioning operation, is pinpointed in this study. The objective function was developed as an explicit function to provide the system performance parameters such as rated capacities and utilization times and reliability elements such as maintenance intervals and mean time to failure (denoted by MTTF) as independent parameters for the multivariable nonlinear optimization problem. Such parameters are then used for deriving maintainability and availability parameters such as mean time to repair (denoted by MTTR) to assure the required availability levels. Developing a methodology to be used for performing the same analysis for other configurations using distinguished energy systems, storage or biomass fuel is another problem that was considered in this research. The results showed that integrating RAM parameters to optimization analysis still keeps the biomass-fueled systems competitive economically with other energy systems. The study showed that a biomass-powered system is more sensitive to electrical module performance parameters than to thermal module and biomass types. Furthermore, thermal module requires more frequent maintenance activities in comparison with electrical module in order to retain a system reliability level above the thresholds. Moreover, reliability can be integrated as a nonlinear constraint into the above-mentioned optimization problem, resulting in optimal rated capacities closer to maximum nominal capacities in case of electrical module. RAM integration to optimization changes the performance parameters of an enviro-economic optimization analysis. The sensitivity to parameters and approaches could be high, and other fuels, technologies, or system configurations shall be considered to deliver more confident results. [ABSTRACT FROM AUTHOR]

Software maintenance is a time-consuming and costly endeavor. As a part of maintenance, refactoring is aimed at enhancing quality. Due to project deadlines and limited resources, developers need to prioritize refactoring activities. In this paper, we present a livestock management-inspired approach for identifying and prioritizing classes to refactor within an object-oriented program. This approach empowers developers to enhance the time/quality ratio. The novelty of our approach lies in utilizing established metrics for detecting code defects to prioritize each class. To validate its effectiveness, the approach was tested on four distinct Pharo-based open source programs. The results demonstrate the approach's efficacy in improving software quality, reducing development time, and enhancing team productivity

This paper presents a method of equipment demonstration AoA method considering RMS factors. It puts forward the equipment demonstration AoA framework, and elaborates equipment demonstration AoA measure system. It analyses the RMS and cost factors, and establishes their quantitative models. An AoA model based on Data Envelopment Analysis (DEA) and an application example are given to illustrate the feasibility and effectiveness of the method.

Reliability optimization is a process aimed at improving the effectiveness and efficiency of maintenance activities within an organization. This optimization can concern various aspects, including failure prevention, resource management, cost reduction, and improving the operational availability of equipment. In this work, our study aims to implement a comprehensive maintenance strategy for the 1600T press at Algal + company with the primary objectives of preventing breakdowns, minimizing unplanned downtime, optimizing productivity, extending the lifespan of equipment, ensuring safety, and improving the competitiveness and profitability of the company. The problem of reliability and availability of the 1600T press at the Algal + company can be addressed by a systematic approach to industrial maintenance. By adopting a holistic approach to industrial maintenance, Algal + company can improve the reliability and availability of its equipment, thereby reducing unplanned downtime and optimizing overall productivity. By systematically applying FMECA to the 1600T press, the optimization efforts become data-driven and focused on addressing the most critical risks. This, in turn, significantly contributes to optimizing the reliability and availability of the equipment, reducing unplanned downtime, and enhancing overall operational efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reliable and resilient power transmission networks serve as vital for sustainable development and uninterrupted electricity supply. Effective maintenance programs are necessary to comply with reliability and sustainability requirements in the power sector. To that end, RAM (reliability, availability, and maintainability) assessments can provide efficient maintenance services that minimize adverse consequences and increase productivity at the lowest possible cost. We employ a statistical framework to evaluate RAM principles, including data acquisition, homogenization, trend hypothesis validation, and parameter estimation. The RAM evaluation of power transmission networks identifies primary bottlenecks in subsystems based on failure and repair behavior trends, which should be prioritized. To find the optimal maintenance policies for each subsystem, we adapt a Multi-Attribute Utility Theory (MAUT) approach, taking costs, availability, and dependability into account. The results of this approach can help improve the operational performance and sustainability of power transmission networks. [ABSTRACT FROM AUTHOR]

As amine-based post-combustion capture is adopted in numerous projects, aligning projected equipment performance with the operational requirements of Carbon Capture and Storage (CCS) facilities is imperative. This study highlights the methodology in using data from the operation of an amine-based CCS facility to provide essential Reliability, Availability and Maintainability (RAM) parameters for some key equipment including a Booster Fan, Compressor, and Lean–rich heat exchanger. The performance of the equipment was evaluated using key indicators such as the mean time between failures and mean time to repair. The analysis shows the need to combine different data sources within the plant to identify opportunistic maintenance and critical failure points to assess equipment reliability. These findings can be used as a tool for maintaining operational efficiency in future CCS plants, empowering decision-makers to identify bottlenecks and make informed equipment redundancy decisions. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to develop a framework to enhance building maintainability through facilitating early suppliers' involvement (ESI) in the design process. Design/methodology/approach: A research methodology consisting of literature review, case studies and survey questionnaire was designed to achieve the above-mentioned aim. Firstly, literature review was used to examine the concepts of building maintenance, maintainability, the design process and ESI. Secondly, three case studies were presented and analysed to investigate the role of ESI towards enhancing building maintainability during the design process. Thirdly, a survey questionnaire was carried out with a representative sample of architectural design firms (ADFs) in Egypt to investigate their perception and application of ESI towards enhancing building maintainability during the design process. Finally, the research developed a framework to facilitate ESI in the design process in ADFs in Egypt. Findings: Through literature review, the research highlighted the relationship between ESI and enhancing building maintainability and identified the roles, benefits, challenges and that encounter ESI in ADFs, factors for suppliers selection and levels of involvement. Results of the data analysis showed that "Difficulty of trusting external parties and sharing information with transparency" was ranked the highest challenge of ESI in ADFs in Egypt, followed by "Legal competitive advantage restrictions". Moreover, "Better estimation for operation and maintenance costs" was ranked the highest contributions of ESI towards enhancing building maintainability, followed by "Reduce the number of operation and maintenance problems or reworks". Finally, respondents stated that "Innovation, technical expertise, and competence" was ranked the highest supplier's selection criteria, while "paying consultation fees for offering advice and recommendations to the design team" was ranked the highest form of supplier's remuneration. Research limitations/implications: The proposed framework contributes to the body of knowledge through proposing five functions that aimed to facilitate ESI in the design process, a research area that received scant attention in construction research. In addition, because of the conceptual nature of the framework, it has to be validated to ensure its capability to overcome the challenges of ESI as an approach for enhancing building maintainability during the design process. Practical implications: This research presents a practical solution that bridges the gap between theory and practice through overcoming the challenges that obstruct suppliers from being involved in the design process as an approach for enhancing building maintainability. Originality/value: This research discussed the relationship between ESI and enhancing building maintainability as well as the roles, benefits and challenges that encounter ESI in ADFs. In addition, the research investigated the levels of suppliers' involvement, selection criteria and forms of payment. Moreover, it investigated the perception and application of ESI in ADFs in Egypt towards improving building maintainability. The research proposed a framework to facilitate the integration of suppliers in the early stages of the project life cycle. It represents a synthesis that is novel and creative in thought and adds value to the knowledge in a manner that has not previously occurred. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This research project aims to improve a power plant maintenance program by using the theory of RAM (Reliability Availability and Maintainability). A North Bangkok Combined Cycle Power Plant, especially a steam turbines power plant, is selected to conduct research from 2021 to 2022. The steps of this research can be separated into 2 phases as follows; the first phase involves root cause analysis from failure notifications stored in the CMMS-KKS code database and risk prioritization. Unit C10 and the compressed air unit system in steam turbines operation are the critical systems subsequently, the RAM approach is applied to improve a preventive maintenance program of unit C10 by estimating an operation time before failure, system availability, and the ability to repair. RAM information is brought to reschedule a PM program, for example, MTTF, MTTR, failure rate, and repair rate, etc. It is found that the percentage of unit C10 system reliability, R(t), and availability, A(t), are higher than 90%. Furthermore, the trade-off between the cost of maintenance and failure for unit C10 is decided by running unit C10 at a percentage of reliability of 88% which can schedule a maintenance interval every 300 hours

The mechatronic industry is currently subject to huge changes challenging it to offer products matching individual customer requirements at competitive prices. The design of such products calls for sophisticated and complex components integration following different technologies. Since we are on the cusp of the Fourth Industrial Revolution, in which the world of mechatronic production, network connectivity, the Internet of Things, and cyber-physical systems are correlated, the complexity of these systems increases exponentially, and we are talking about advanced mechatronic systems. To assist these changes, various methods, sweeping all project phases, are used by business houses. Predictive dependability assessment in the earlier design stage is considered a powerful metric used to evaluate the performances of different kinds of mechatronic products before the production phase. Altogether, dependability analysis ties the design directly to the desired functionality, operability, and integrity of the system. This paper explores an approach to assessing the dependability attributes, reliability, availability, and maintainability (RAM), of repairable mechatronic systems based on timed colored Petri nets and a Monte Carlo simulation, integrating simultaneously diverse components technologies: mechanical, electronic, and software. The proposed approach is tested taking the case of a regenerative braking system. The methodology appears to be efficient for evaluating predictive RAM indicators (MTTFF, MTTR, MTBF...) for the whole system and for each individual component separately. [ABSTRACT FROM AUTHOR]

This paper takes the architecture features and functions of GSM-R Macro Site equipment in the existing network as the research object, and demonstrates, through RAMS analysis and engineering tests on the operating network, that the reliability, availability and maintainability of a single set of Base Transceiver Station (BTS) currently in use can be improved by enhancing the redundancy of key modules, so as to reduce the probability of the failures and out-of-service of base stations in the network. Reducing the impact of single-module failures of GSM-R base stations on railway services including CTCS-3, and lowering the maintenance timeliness requirements for BTS module failures along the line, both lay the foundation for the continuous, long-term and stable operation of GSM-R Macro Sites in the network, and provide a solid base for safe and stable railway operation. [ABSTRACT FROM AUTHOR]

The operation of liquefied natural gas (LNG) marine loading arms plays a pivotal role in the efficient transfer of LNG from maritime vessels to downstream facilities, underpinning the global LNG supply chain. Despite their criticality, these systems frequently encounter operational challenges, notably slow coupling speeds and increased downtimes driven by maintenance demands. Addressing these challenges, Physical Asset Management principles advocate for maximizing process availability by minimizing both planned and unplanned outages. Recognizing maintainability as a key equipment attribute, this document proposes a procedure that extends the use of the UNE 151001 standard to evaluate the maintainability of physical assets. This proposal incorporates into traditional RCM a step for the selection of maintenance levels proposed in the standard, as well as the use of the AHP technique for selecting the weights used during the analysis process. Finally, an aggregated maintainability indicator is presented, which will allow for better evaluation, comparison, and monitoring of this characteristic in one or more industrial assets. To demonstrate its feasibility and utility, the proposed procedure is applied to a set of LNG marine unloading arms. This study identifies pivotal areas for improvement and devises strategic action plans aimed at enhancing asset's maintainability. The outcomes of this analysis not only provide a roadmap for augmenting operational efficiency but also furnish empirical justification for the requisite investments in maintainability enhancements, thereby contributing to the resilience and sustainability of LNG logistics infrastructure. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Preservation and conservation endeavors aimed at historical structures hold significant importance in transmitting cultural and historical legacies to future generations, functioning as custodians of societal memory. This research meticulously assesses the current condition of a traditional mansion situated in the Çatalca district of Istanbul, outlining restoration strategies to restore its original character. Despite the burgeoning residential development in Çatalca, the rapid onset of structural decay poses a tangible threat to the preservation of its historical heritage, necessitating a focused examination of relevant structures in the area. Thorough documentation, meticulous surveys, archival research, and on-site inspections have been conducted, leading to the formulation of comprehensive restoration proposals and intervention strategies. Recognizing the distinctive attributes of the mansion, it becomes apparent that its restoration should prioritize maintaining its original function rather than exploring alternative uses. Furthermore, the examination uncovers numerous alterations and misguided interventions over time, emphasizing the urgent need for concerted preservation endeavors. In essence, this study holds paramount importance in revealing the intrinsic qualities of a historical structure, safeguarding its significance in the collective urban consciousness, and perpetuating the sustainable continuation of cultural nuances. [ABSTRACT FROM AUTHOR]

When studying Markov chain models and semi-Markov chain models, it is useful to know which state vectors n , where each component n i represents the number of entities in the state S i , can be maintained or attained. This question leads to the definitions of maintainability and attainability for (time-homogeneous) Markov chain models. Recently, the definition of maintainability was extended to the concept of state reunion maintainability ( S R -maintainability) for semi-Markov chains. Within the framework of semi-Markov chains, the states are subdivided further into seniority-based states. State reunion maintainability assesses the maintainability of the distribution across states. Following this idea, we introduce the concept of state reunion attainability, which encompasses the potential of a system to attain a specific distribution across the states after uniting the seniority-based states into the underlying states. In this paper, we start by extending the concept of attainability for constant-sized Markov chain models to systems that are subject to growth or contraction. Afterwards, we introduce the concepts of attainability and state reunion attainability for semi-Markov chain models, using S R -maintainability as a starting point. The attainable region, as well as the state reunion attainable region, are described as the convex hull of their respective vertices, and properties of these regions are investigated. [ABSTRACT FROM AUTHOR]