An overview of Industry 4.0 Applications for Advanced Maintenance Services

Manufacturing today has experienced dynamic changes imposed by social-technical systems that address economic, social and sustainable requirements. Furthermore, the technologies in Industry 4.0 have also brought many smart applications to advance manufacturing to the next level of development. In a focused sense, maintenance plays a key role in manufacturing to create value propositions—extension of equipment useful life and enhancement in overall equipment effectiveness—for stakeholders towards economics and sustainability. In this context, the maintenance services that create the value propositions are not only delivered as after-sales maintenance services but developed to advanced maintenance services embedded into Industry 4.0 applications. To provide a clearer picture of the development, this work aims to review and categorize the maintenance services in three groups—basic services, intermediate services, advanced services—associated with technologies in Industry 4.0 across the life-cycle maintenance service

XRepo - Towards an information system for prognostics and health management analysis

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A Feasible Framework for Maintenance Digitalization

The entire industry is changing as a result of new developments in digital technology, and maintenance management is a crucial procedure that may take advantage of the opportunities brought about by industrial digitalization. To support digital innovation in maintenance management, this study intends to meet the cutting-edge necessity of addressing a transformation strategy in industrial contexts. Setting up a customized pathway with adequate methodologies, digitalization tools, and collaboration between the several stakeholders involved in the maintenance environment is the first step in this process. The results of a previous conference contribution, which revealed important digitalization variables in maintenance management, served as the foundation for the research approach herein suggested. We lead a thorough assessment of the literature to categorize the potential benefits and challenges in maintenance digitalization to be assessed in conjunction with the important digitalization aspects previously stated. As a starting point for maintenance management transformation, we offer a feasible framework for maintenance digitalization that businesses operating in a variety of industries can use. As industrial processes and machines have become more sophisticated and complex and as there is a growing desire for more secure, dependable, and safe systems, we see that this transition needs to be tailored to the specific application context

Maturity level of predictive maintenance application in small and medium-sized industries: Case of Morocco

In order to remain competitive in the long term and to push the company's efficiency to its limits, entrepreneurs are more and more open to the idea of integrating into Industry 4.0 aiming mainly at filling the important downtimes and the associated productivity losses by implementing predictive maintenance. This concept, common in developed countries, is much less widespread in Morocco and even less in small and medium-sized Moroccan companies. The objective of this article is to study the maturity level of predictive maintenance in Moroccan small and medium-sized enterprises, through a questionnaire validated by experts and made available to several companies. Valid data from 115 companies throughout the kingdom operating in different sectors were collected and processed by descriptive and factorial analysis under SPSS software. The results obtained show that only 33% of our sample were able to implement predictive maintenance, and that the expected benefits of this approach are the minimization of downtime at 96.5% and the increase in productivity at 94.8%, The main challenges observed are the lack of team motivation and a corporate culture unsuited to digitalization, which represents 42.277% of the total variance, lack of financial resources at 12.916% of the total variance and lack of data protection at 11.644% of the total variance. This analysis indicates that the level of maturity regarding the application of predictive maintenance in Moroccan small and medium-sized companies is low, these rates can be used to improve the root causes

A Prescriptive Maintenance Aligned Production Planning and Control Reference Process

Digital innovations can improve various business processes, such as production planning and control (PPC). In the last years, prescriptive maintenance (PxM) emerged as a strategy to increase overall production performance, but an alignment of the PPC process with PxM has not been examined yet. To tackle this problem, a PxM-aligned PPC process is designed and evaluated in this study using a reference model development methodology, including a narrative literature review, a multivocal literature review, and eight expert interviews. The reference model shows where process elements benefit from PxM alignment, how alignment can be achieved from a process and output, data, function, and organization view, and where fits and gaps between theory and practice are

A Type-2 Fuzzy Based Explainable AI System for Predictive Maintenance within the Water Pumping Industry

Industrial maintenance has undergone a paradigm shift due to the emergence of artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. Rather than accepting the drawbacks of reactive maintenance, leading firms worldwide are embracing "predict-and-prevent" maintenance. However, opaque box AI models are sophisticated and complex for the average user to comprehend and explain. This limits the AI employment in predictive maintenance, where it is vital to understand and evaluate the model before deployment. In addition, it's also important to comprehend the maintenance system's decisions. This paper presents a type-2 fuzzy-based Explainable AI (XAI) system for predictive maintenance within the water pumping industry. The proposed system is optimised via Big-Bang Big-Crunch (BB-BC), which maximises the model accuracy for predicting faults while maximising model interpretability. We evaluated the proposed system on water pumps using real-time data obtained by our hardware placed at real-world locations around the United Kingdom and compared our model with Type-1 Fuzzy Logic System (T1FLS), a Multi-Layer Perceptron (MLP) Neural Network, an effective deep learning method known as stacked autoencoders (SAEs) and an interpretable model like decision trees (DT). The proposed system predicted water pumping equipment failures with good accuracy (outperforming the T1FLS accuracy by 8.9% and DT by 529.2% while providing comparable results to SAEs and MLPs) and interpretability. The system predictions comprehend why a specific problem may occur, which leads to better and more informed customer visits to reduce equipment failure disturbances. It will be shown that 80.3% of water industry specialists strongly agree with the model's explanation, determining its acceptance

Smart Maintenance - maintenance in digitalised manufacturing

What does digitalised manufacturing entail for maintenance organizations? This is a pressing question for practitioners and scholars within industrial maintenance management who are trying to figure out the best ways for responding to the rapid advancement of digital technologies. As technology moves faster than ever before, this is an urgent matter of uttermost importance. Specifically, in order to secure the success of highly automated, intelligent, connected and responsive production systems, industrial maintenance organizations need to transform to become leading enablers of high performance manufacturing in digitalised environments. In this thesis, this transformation is referred to as “Smart Maintenance”. The purpose of this thesis is to ensure high performance manufacturing in digitalised environments by enabling the adoption of Smart Maintenance. In order to stimulate adoption, a holistic understanding of Smart Maintenance is needed. Therefore, the aim of this thesis is to describe future scenarios for maintenance in digitalised manufacturing as well as to conceptualize and operationalize Smart Maintenance. The holistic understanding has been achieved through a phenomenon-driven research approach consisting of three empirical studies using multiple methods. Potential changes for maintenance organizations in digitalised manufacturing are described in 34 projections for the year 2030. From these projections, eight probable scenarios are developed that describe the most probable future for maintenance organizations. In addition, three wildcard scenarios describe eventualities that are less probable, but which could have large impacts. These scenarios serve as input to the long-term strategic development of maintenance organizations.Smart Maintenance is defined as “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies” and has four core dimensions: data-driven decision-making, human capital resource, internal integration and external integration. Manufacturing plants adopting Smart Maintenance are likely to face implementation issues related to change, investments and interfaces, but the rewards are improved performance along multiple dimensions when internal and external fit have been achieved. Smart Maintenance is operationalized by means of an empirical measurement instrument. The instrument consists of a set of questionnaire items that measure the four dimensions of Smart Maintenance. It can be used by practitioners to assess, benchmark and longitudinally evaluate Smart Maintenance in their organization, and it can be used by researchers to study how Smart Maintenance impacts performance. This thesis has the potential to have a profound impact on the practice of industrial maintenance management. The scenarios described allow managers to see the bigger picture of digitalisation and consider changes that they might otherwise ignore. The rich, understandable, and action-inspiring conceptualization of Smart Maintenance brings clarity to practitioners and policy-makers, supporting them in developing implementation strategies and initiatives to elevate the use of Smart Maintenance. The measurement instrument makes it possible to measure the adoption of Smart Maintenance in manufacturing plants, which serves to develop evidence-based strategies for successful implementation. Taken together, the holistic understanding achieved in this thesis enables the adoption of Smart Maintenance, thereby ensuring high performance manufacturing in digitalised environments

The Future of Maintenance Within Industry 4.0: An Empirical Research in Manufacturing

Part 1: Lean and Green ManufacturingInternational audienceThe recent advances in digital technologies are revolutionizing the industrial landscape. Maintenance is one of the functions that may benefit from the opportunities that emerge with the digital transformation of industrial processes. Nevertheless, until now very few research papers investigated on what digitalized manufacturing entails for maintenance organizations along both technical and social dimensions. The aim of this paper is to investigate the vision of the future of Maintenance within the industry 4.0 and to show empirical evidence on how manufacturing companies are approaching the digital transformation process of maintenance. An empirical investigation was developed through multiple case-study involving nine manufacturing companies in Italy. Findings emerge about the main perceived challenges by companies for the success of digital transformation of maintenance as well as the technological and organizational mechanisms that are used in ongoing innovative Maintenance projects