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Industry 4.0 is a tsunami that will invade the whole world. The real challenge of the future factories requires a high degree of reliability both in machinery and equipment. Thereupon, shifting the rudder towards new trends is an inevitable obligation in this fourth industrial revolution where the maintenance system has radically changed to a new one called predictive maintenance 4.0 (PdM 4.0). This latter is used to avoid predicted problems of machines and increase their lifespan taking into account that if machines have not any predicted problem, they will never be checked. However, in order to get successful prediction of any kind of problems, minimizing energy and resources consumption along with saving costs, this PdM 4.0 needs many new emerging technologies such as the internet of things infrastructure, collection and distribution of data from different smart sensors, analyzing/interpreting a huge amount of data using machine/deep learning…etc. This paper is devoted to present the industry 4.0 and its specific technologies used to ameliorate the existing predictive maintenance strategy. An example is given via a web platform to get a clear idea of how PdM 4.0 is applied in smart factories.

Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, (4) Maintenance system architecture, and (5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.

Digitalization and sustainability are important topics for manufacturing industries as they are affecting all parts of the production chain. Various initiatives and approaches are set up to help companies adopt the principles of the fourth industrial revolution with respect sustainability. Within these actions the use of modern maintenance approaches such as Maintenance 4.0 is highlighted as one of the prevailing smart & sustainable manufacturing topics. The goal of this paper is to describe the latest trends within the area of maintenance management from the perspective of the challenges of the fourth industrial revolution and the economic, environmental and social challenges of sustainable development. In this work, intelligent and sustainable maintenance was considered in three perspectives. The first perspective is the historical perspective, in relation to which evolution has been presented in the approach to maintenance in accordance with the development of production engineering. The next perspective is the development perspective, which presents historical perspectives on maintenance data and data-driven maintenance technology. The third perspective, presents maintenance in the context of the dimensions of sustainable development and potential opportunities for including data-driven maintenance technology in the implementation of the economic, environmental and social challenges of sustainable production.

Industry 4.0 is the latest technological age, in which recent technological developments are being integrated within industrial systems. Consequently, maintenance management of current industrial manufacturing systems is affected by the emergence of the technologies and features of Industry 4.0. This study aimed to conduct a comprehensive literature review to understand how Industry 4.0 technologies and features affect the various functions of maintenance management systems. The reviewing process was initiated by examining the most recent related literature in three different databases. In total, 54 articles were classified into three research categories. Then, the integration of the main functions and components of the adopted maintenance management model and the Industry 4.0 features and technologies were aligned, focusing on the driving force of predictive maintenance. The analysis focused mainly on the technical aspects of the integration process, including integration concepts and integration-assisting tools, identifying the main applications and highlighting the challenges identified in the analysed literature. The key findings were that the main functions of maintenance management systems are significantly influenced by different Industry 4.0 technologies, mainly artificial intelligence–machine learning, CPS, IoT, big data, augmented reality, and cloud computing, in terms of successful integration. Consequently, the overall system implied tangible improvements through the involvement of different Industry 4.0 features which promote real-time condition monitoring, enable data management and curation, increase coordination between various maintenance tasks, facilitate supervision through remote maintenance applications, and, overall, improve operations and productivity, reduce unplanned shutdowns and, as a result, reduce the associated costs. To provide research directions, examples, and methodologies for integrating the various maintenance management system functions with the cutting-edge Industry 4.0 technologies and features based on real and practical cases present in the reviewed literature, the review’s findings are comprehensively categorised and summarised.

PurposeThe integration of Total Productive Maintenance (TPM) and Industry 4.0 (I4.0) is an emerging model, and the global pressure of various stakeholders raises scepticism of any emerging model towards providing sustainability. Therefore, this research aims to identify and rank the potential significant drivers of an integrated model of I4.0 and TPM to guide manufacturing enterprises towards sustainability.Design/methodology/approachThis research follows a four-phase methodology including literature review and expert opinion to select the sustainability indicators and I4.0-integrated TPM key drivers, followed by employing the analytic hierarchy process approach for weight determination of sustainability indicators. The research then deploys the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritise the I4.0-integrated TPM key drivers based on their effect on various sustainability indicators. Finally, a sensitivity analysis is conducted to check the robustness of the TOPSIS.FindingsThe findings establish the top five most influential key drivers of an I4.0-integrated TPM system, which include top management support, formal I4.0 adoption program, mid-management involvement and support, solid TPM baseline knowledge and high engagement of the production team. These top drives can lead manufacturing firms towards sustainability.Research limitations/implicationsThe digitalisation of shop floor practices, such as TPM, could be adapted by shop floor managers and policymakers of manufacturing companies to deliver sustainability-oriented outcomes. In addition, this research may aid decision-makers in the manufacturing sector in identifying the most important drivers of I4.0 and TPM, which will assist them in more effectively implementing an integrated system of I4.0 and TPM to practice sustainability. The scope of TPM applicability is wide, and the current research is limited to manufacturing companies. Therefore, there is a huge scope for developing and testing the integrated system of I4.0 and TPM in other industrial settings, such as the textile, food and aerospace industries.Originality/valueThis research makes a first-of-its-kind effort to examine how an I4.0-integrated TPM model affects manufacturing companies' sustainability and how such effects might be maximised.

PurposeThe purpose of this paper is to identify and analyze the challenges associated with the implementation of the concept of Maintenance 4.0 in industries.Design/methodology/approachThe challenges in the implementation of Maintenance 4.0 are identified through a literature survey and interaction with professionals from the industry and academia. A structural hierarchy framework that integrates the methodologies of ISM and MICMAC is used for the analysis of Maintenance 4.0 implementation challenges. The framework establishes the interrelationship among challenges and segregates them into driving, linkage, dependent and autonomous groups.FindingsA novel concept of Maintenance 4.0 under the aegis of Industry 4.0 is gaining appreciation worldwide. However, there are challenges in the adaptation of Maintenance 4.0 concepts among industries. The various challenges as well as their impact on the objective of implementation of Maintenance 4.0 are identified.Practical implicationsThe practicing engineers, academicians, researchers and the concerned industries can infer from the results to improve upon the causes of such challenges and promote the implementation of Maintenance 4.0 most efficiently and effectively.Originality/valueThis paper is a novel, unique and first of its kind that addresses the most contemporary challenges in the implementation of Maintenance 4.0 concepts in industries.

In the current context of intense competition, industrial maintenance plays a crucial role in ensuring the performance and resilience of companies. It ensures the continuous availability of equipment, which is essential to avoid unplanned downtime that can lead to significant economic losses. Moreover, maintenance improves production quality by reducing failures and manufacturing defects, and by optimizing the costs associated with maintenance interventions. Predictive maintenance, which is a fundamental part of Industry 4.0, allows for anticipating failures before they occur by leveraging real-time data to predict malfunctions and plan the necessary actions. This not only reduces unplanned downtime but also lowers the overall cost of repairs and equipment replacements. However, data acquisition and processing present major challenges for data science project managers, as they require appropriate frameworks and approaches tailored to each problem and context. This study proposes an innovative solution with a predictive maintenance model developed using the industrial data analysis improvement cycle (IDAIC) approach, specifically designed for industrial maintenance projects. By using a deep learning algorithm, long short-term memory (LSTM), and techniques such as early stopping, the model was applied to the data of a plastic injection molding machine and achieved impressive results. With an R² of 96% and an MSE of 99%, it presents itself as a powerful decision-support tool for industrial maintenance.

PurposeCompetencies are significant predictors of employee outcome. Nowadays, new technologies are changing maintenance processes and workflow. The role of employees and their competencies will therefore undergo decisive changes in the future. Therefore, a well-designed competency model for maintenance departments is important. The purpose of this paper is to develop a maintenance 4.0 competency model applicable to all industrial sectors by adapting it to the specificities of each sector.Design/methodology/approachThe research methods consist of a comprehensive literature review on the main characteristics of the competency model and the individual competencies needed for the maintenance 4.0 employees. Interviews were conducted in order to validate and prioritize the required competencies for maintenance 4.0 employees identified in the literature.FindingsThe maintenance 4.0 competency model combines the required competencies in maintenance 4.0 and crosses the three hierarchical levels: managers, engineers and technicians. These competencies are organized in terms of four categories: technical, personal, social and methodological. In addition, a degree of importance for each competency is assigned as very important, moderately important and slightly important. As a result, this study identified the essential competencies for maintenance 4.0 stakeholders, where 12 competencies are considered very important for maintenance 4.0 technicians, 19 for engineers and 18 for managers.Research limitations/implicationsThis work has some limitations. First, although the articles related to competencies and their classification were selected very carefully, it is difficult to eliminate the probability of overlooking publications. Second, the limitation of the study is based on the difficulty of implementing the model in a case study, given that a minority of industrial companies have implemented maintenance 4.0 technologies in Morocco.Practical implicationsThis work has practical implications for both individuals and institutions (companies and academies) to cope with new competency requirements in maintenance 4.0. Organizations can use the model in the recruitment process and for the identification of training needs. The results of the research will also contribute to identifying the scope of competencies of the maintenance 4.0 actors (engineer, manager and technician), which, in practice, contributes to the creation of requirements for the candidates applying for a job in the maintenance department. Additionally, educational institutions should make the necessary changes to their curricula to suitably prepare students for the required maintenance 4.0 competencies.Social implicationsThe social implications of the article result from the contribution to the development of maintenance competencies. Individuals can use this model for their own personal development. Furthermore, companies can use this model to define job profiles for vacancies in M4.0. Therefore, using the model for training program implementation has a positive effect on employee job satisfaction and employees ’morale.Originality/valueThis research develops a novel maintenance 4.0 competency model by categorizing the maintenance workforce into three hierarchical levels: managers, engineers and technicians. In addition, the competency requirement is prioritized to three degrees: very important, moderately important and slightly important. According to the previous studies conducted on maintenance 4.0 and employees' competencies, this study revealed that no research has developed a competency model for maintenance 4.0. Hence, this model is unique, generic and integrative since it presents the most relevant competencies for the three hierarchical levels. Moreover, this work combines the results of the literature review and the experts' returns. This model can be useful in the recruitment of new maintenance employees, the evaluation of their performance and the identification of training needs to cope with new changes in maintenance competencies.

Hydroelectric power plants are renewable electricity generation assets that require high availability and reliability in their operation and maintenance. To justify improvement actions (modernization and investments), it is necessary to analyze the operation of the plant, the maintenance plan being implemented, and, naturally, the incidents and breakdowns that affect this asset. This paper presents research on hydroelectric power plant maintenance based on the development of a database of incidents and failures of such plants, considering the methodology of failure modes, effects and criticality analysis (FMECA) as well as the reliability-centered maintenance (RCM) methodology of the initial maintenance plan of a standard hydroelectric power plant. Different maintenance standards and analysis standards (IATF criticality of failure modes, UNE 13306, ISO 14224, etc.) were considered. The results reveal different improvement and optimization actions based on the current technological development, which can be applied to hydroelectric generation (Innovation 4.0), as well as actions to optimize the initial maintenance plan based on Maintenance 4.0. The technical justification for such improvements in hydropower generation highlights a key area of development in the expansion of renewable energies worldwide. Hydropower generation assets have contributed renewable energy to the system for many years; however, they now require redesign in their operation and maintenance.

Predictive maintenance (M4.0) allows more targeted and efficient use of resources, reduces unplanned downtime, and increases production and equipment performance compared to classical existing maintenance (M3.0). This paper deals with the development of a new ecosystem that adopts the new technologies of Industry 4.0 to drive real-time monitoring and diagnosis of engine defects. The proposed architecture is based on implementing a process of identifying critical components and extracting related data (speed and acceleration) based on IoT technology. A neural model (ANN) is implemented for monitoring, detecting and diagnosing engine faults with high accuracy compared to existing techniques. The effectiveness and reliability are validated through real-time test bench studies.