Maintenance 4.0 Technologies for Sustainable Manufacturing

1. Introduction

Manufacturing companies are navigating two pivotal trends that significantly impact their operations: sustainability and digitalization [1]. These trends present significant challenges, with sustainability becoming a global concern due to climate change and resource depletion [2]. In the sustainable development environment, manufacturing companies have been pressured to think beyond traditional economic measures and evaluate their business’s environmental and social effects. They need not only to offer a return on investment but also to reduce the impact on the environment [3]. They must also constitute an attractive workplace for people and meet the requirements of stakeholders who can affect or be affected by the company. The second major trend, digitalization, is a cornerstone of the Industry 4.0 era, as described in the production literature [4]. In this era, manufacturing systems are empowered to monitor physical processes and make intelligent decisions through real-time communication and collaboration with humans, machines, sensors, and other elements. This evolution toward Industry 4.0 is pervasive, impacting all enterprise levels, including maintenance [5,6].

According to the authors of [7], maintenance can be defined as “the systematic execution of monitoring, repair, and replacement tasks designed to preserve or reinstate the desired functionality of a machine”. Today, maintenance management is a very complex function involving technical and managerial skills and the flexibility to cope with the enormous dynamics of the business environment [8]. Maintenance management is key in modern production systems and requires proper attention [9]. Maintenance management should be treated as long-term strategic planning, integrating all stages of the product life cycle. This strategic planning must also anticipate changes in future social, economic, and environmental trends and incorporate innovative technologies into operations [10]. Moreover, according to [11,12,13], maintenance management is commonly considered the first step in an Industry 4.0 environment to have technical and economic advantages. Industry 4.0 technologies offer new possibilities for maintenance managers and support to improve maintenance strategies [14]. Moreover, according to the authors of [15], integrating I4.0 technology with maintenance can support the company in meeting the economic, environmental, and social challenges of sustainable manufacturing.

If technologies such as the Industrial Internet of Things (IIoT), Augmented Reality (AR), Virtual Reality (VR), Big Data Analytics (BDA), and AI are the main drivers of Industry 4.0, Maintenance 4.0 is the realization of these technologies. The ultimate objective of Maintenance 4.0 is to enhance and advance maintenance processes.

Over the last decade, several initiatives and approaches have been set up to support maintenance processes in adopting the principles and technologies of Industry 4.0 [16,17,18]. Modern technologies enabling the acquisition, integration, and analysis of numerous industrial data provide new possibilities for supporting maintenance processes. Predictive maintenance is a particularly interesting and wide-ranging field of application [19,20]. According to [21], “Predictive maintenance is an industrial science that utilizes condition-based monitoring technology to observe the health of machines, thereby enabling early detection of its deterioration via anomalies or faults; and provides a mechanism to plan and schedule maintenance actions to maximize its remaining useful-life”. Many predictive maintenance methods have been developed with the development of big data methods and IoT technology [22,23]. In data-driven predictive maintenance strategies, machine learning (ML) can predict the system’s operational status and remaining useful life (RUL) [24,25,26]. Additionally, ML methods support the planning of maintenance activities via connected IoT technology to reduce downtime and maintenance costs and increase machine availability [27]. PdM can also be implemented using digital twins [28,29,30]. A “digital twin” is a dynamic, digital replica of a technical object, such as a physical system, device, machine, or production process. This solution is an integral part of Industry 4.0 and is particularly effective in sustainable production and maintenance [31,32]. Research on the application of DT in maintenance has been conducted by, among others, [33,34].

As predictive maintenance advances and is more widely adopted by companies, augmented reality can more effectively support machine inspection [35,36]. AR represents effective maintenance support, guiding operators through diagnostics, inspection, and training [37,38,39,40,41].

A literature analysis shows that implementing I4.0 technologies has many advantages [42,43,44]. However, despite the potential benefits, the implementation of I4.0 technologies in maintenance requires not only overcoming the technological challenges related to the integration with Industry 4.0. Other challenges, such as sustainability, must also be considered, including social, economic, and environmental challenges, to ensure sustainable manufacturing, which is greatly influenced by maintenance [45,46]. According to the authors of [47,48], the ability to anticipate, avoid, and quickly solve problems by applying Industry 4.0 technologies to maintenance practices is an invaluable tool for a company in achieving its sustainability goals

The objective of the following Special Issue (SI) is to present the latest advances and developments in new methods, techniques, systems, and tools dedicated to applying Maintenance 4.0 technologies to the economic, environmental, and social challenges of sustainable manufacturing. The present Special Issue captures the diversity of research focusing on the issues of Maintenance 4.0 and sustainability. The SI contains 13 articles, which are briefly described in the following chapter. The following Editorial encourages the reader to familiarize themselves with the articles and further develop the still topical issues of maintenance, Industry 4.0 technologies, and sustainable manufacturing.

2. Overview of Published Articles

Industry 4.0 is expected to revolutionize maintenance practices by reaching new predictive and prescriptive maintenance analytics levels. However, according to Nordal and El-Thalji (Contribution 1), justifying these new maintenance paradigms (predictive and prescriptive) is often difficult due to their multiple inherent trade-offs and hidden systems causalities. The prediction models in the literature can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. The literature also omits modeling deterioration based on loading or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The authors propose a novel simulation model that enables estimation of the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as a mixed maintenance strategy and predictive maintenance (PdM) is leveraged into opportunistic intervals

In the paper by Giacotto et al. (Contribution 2), the authors discuss how the maintenance technologies applicable to various machines need to be appropriately supported by a production environment, called an “ecosystem”, that facilitates their integration within the process and their synergy with the operators. The authors tested the existing concepts of the Smart Prescriptive Maintenance Framework (SPMF) for introducing a prescriptive maintenance policy in an aviation assembly line.

Scheffer et al. (Contribution 3) propose an adaptive architectural framework aimed at shaping and structuring the process that provides operators with tailored support when using an augmented reality (AR) tool. It was found that the framework ensures that self-explanatory AR systems can capture the operator’s knowledge, support the operator during maintenance activities, conduct failure analysis, provide problem-solving strategies, and improve learning capabilities. In the fourth article included in this Special Issue, Borro et al. (Contribution 4) present an example of the application of AR technology and wearable devices for the maintenance of bus fleets. The solution aims to improve the maintenance process by verifying the task checklist. The main contribution of the paper focuses on implementing prototypes at company facilities in an operational environment with real users and addresses the difficulties inherent in transferring the technology to a real work environment, such as a mechanical workshop.

The research from Nentwich and Reinhart (Contribution 5) and Xie et al. (Contribution 6) concerns an industrial robot (IR) monitoring system. Industrial robots are used in almost every industry, and their reliability is crucial to minimizing downtime and maximizing production. An IR includes various components (e.g., robot arms, body, arm, actuators, sensors, end effectors, switches, gears, and connections). Due to its complex nature, many faults can occur in a robotic system. Therefore, monitoring their condition is very important. This applies to both individual components, e.g., gears (Contribution 5) and the architecture of the monitoring system (Contribution 6).

Yin et al. (Contribution 7) propose a novel prediction scheme for the life prediction of equipment under multiple operating conditions based on morphological patterns and the symbolic aggregate approximation-based similarity measurement method (MP-SAX) and STM. The analysis and verification of public datasets of the turbofan engine from the NASA Ames Prognostics Data Repository proves that the proposed method can achieve life prediction only using original monitoring data without extracting the degradation trend of said data. In addition, the prediction result of the STM can be effectively improved by improving the STM’s similarity measurement accuracy.

Kowalski and Waszkowski (Contribution 8) propose an innovative idea of taking environmental aspects into account when selecting loaders and haul trucks for excavated material transport tasks so that the amount of pollutants emitted by them in exhaust gases, e.g., the sum of hydrocarbons and nitrogen oxides (HC+NOx), is also taken into consideration when assigning the means of transport for particular tasks. Environmental aspects were also the subject of the paper by Cárcel-Carrasco et al. (Contribution 9). In this paper, the authors indicate that refrigeration production accounts for a significant proportion of electricity consumption in the main branches of the food industry. The authors state that regulating the power compressors’ efficiency is a suitable way to save energy.

Research by Cárcel-Carrasco and Cárcel-Carrasco (Contribution 10) highlights the importance of knowledge in maintenance activities. The main objective of their study was to define the relationship of knowledge management within maintenance activities from the perspective of the technicians who work in these departments and extract the fundamental barriers and facilitators that these technicians consider for the creation, transmission, and use of this strategic knowledge.

The topic of the next two papers (Contribution 11 and Contribution 12) is the digital twin. The purpose of the paper by Rojek et al. (Contribution 11) was to present the results of research on the development of digital twins of technical objects, which involved data acquisition and their conversion into knowledge, the use of physical models to simulate tasks and processes, and the use of simulation models to improve the physical tasks and processes. The main goal of the paper by Pawlewski et al. (Contribution 12) was to demonstrate the research implications of a new trend in computer simulations using digital twin technologies to optimize intralogistics processes.

In the final paper included in this Special Issue, Bocewicz et al. (Contribution 13) consider the dynamic vehicle routing problem where a fleet of vehicles handles periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries, as well as preventive maintenance requests, are scheduled and implemented according to a cyclically repeating sequence. This is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism, enabling the handling of the fuzzy nature of variables through an algebraic approach. The authors’ computational results show that the proposed solution outperforms commonly used computer simulation methods.

3. Conclusions

The collection of articles on Maintenance 4.0 for sustainable production presented above covers numerous issues. It also points to the challenges companies face, regardless of the industry (e.g., food, rail, and aviation) and type of activity (manufacturing and services). Every company has assets (machines, devices) that require maintenance. Maintenance processes are fundamental to achieving company goals, including those directly related to sustainable development. Industry 4.0 technologies help transform maintenance processes into an intelligent and resilient system, supporting managers in achieving these goals.

Although submissions for the present Special Issue have already been closed, more detailed research is needed on Maintenance 4.0 technologies regarding the challenges of sustainable production. Analyzing the literature and observing business practices, it can be predicted that there will soon be great demand from different companies for new tools and methods offered by Maintenance 4.0 to predict, prevent, and reduce the impact of failure on all aspects of sustainable development.

Acknowledgments

This Special Issue would not have been possible without the contributions of the various talented authors, the hardworking and professional reviewers, and the dedicated editorial team of Applied Sciences. Congratulations to all authors—regardless of the final decisions on the submitted manuscripts, the feedback, comments, and suggestions from the reviewers and editors helped the authors to improve their papers. I would like to take this opportunity to express my sincere gratitude to all reviewers.

Conflicts of Interest

The author declares no conflicts of interest.

List of Contributions

1. Nordal, H.; El-Thalji, I. Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study. Appl. Sci. 2021, 11, 3487.

2. Giacotto, A.; Costa Marques, H.; Pereira Barreto, E.A.; Martinetti, A. The Need for Ecosystem 4.0 to Support Maintenance 4.0: An Aviation Assembly Line Case. Appl. Sci. 2021, 11, 3333.

3. Scheffer, S.; Martinetti, A.; Damgrave, R.; Thiede, S.; van Dongen, L. How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective. Appl. Sci. 2021, 11, 2656

4. Borro, D.; Suescun, Á.; Brazález, A.; González, J.M.; Ortega, E.; González, E. WARM: Wearable AR and Tablet-Based Assistant Systems for Bus Maintenance. Appl. Sci. 2021, 11, 1443

5. Nentwich, C.; Reinhart, G. A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring. Appl. Sci. 2021, 11, 10403

6. Xie, X.;Wu, X.; Hu, Q. Bi-Objective Optimization for Industrial Robotics Workflow Resource Allocation in an Edge–Cloud Environment. Appl. Sci. 2021, 11, 10066.

7. Yin, J.; Li, Y.; Wang, R.; Xu, M. An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions. Appl. Sci. 2021, 11, 10968.

8. Kowalski, A.; Waszkowski, R. Method of Selecting the Means of Transport of the Winning, Taking into Account Environmental Aspects. Appl. Sci. 2021, 11, 5512. 7.

9. Cárcel-Carrasco, J.; Pascual-Guillamón, M.; Salas-Vicente, F. Improve the Energy Efficiency of the Cooling System by Slide Regulating the Capacity of Refrigerator Compressors. Appl. Sci. 2021, 11, 2019.

10. Cárcel-Carrasco, J.; Cárcel-Carrasco, J.-A. Analysis for the Knowledge Management Application in Maintenance Engineering: Perception from Maintenance Technicians. Appl. Sci. 2021, 11, 703.

11. Rojek, I.; Mikołajewski, D.; Dostatni, E. Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance. Appl. Sci. 2021, 11, 31.

12. Pawlewski, P.; Kosacka-Olejnik, M.; Werner-Lewandowska, K. Digital Twin Lean Intralogistics: Research Implications. Appl. Sci. 2021, 11, 1495.13.

13. Bocewicz, G.; Nielsen, P.; Jasiulewicz-Kaczmarek, M.; Banaszak, Z. Dynamic planning of mobile service teams’ mission subject to order uncertainty constraints. Appl. Sci. 2020, 10, 8872.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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