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Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. The majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance is the current trend of smart maintenance, followed by most maintenance engineers. The challenging task in predictive maintenance is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, a data-driven approach for predictive maintenance is taking a new flight towards smart manufacturing. Several researchers have published work related to fault diagnosis in rotating machines, mainly exploring a single type of fault. However, a consolidated review of literature that focuses more on the "multi-fault diagnosis" aspect of rotating machines is lacking. There is a need for a study that would systematically cover all the aspects right from sensor selection, data acquisition, feature extraction, multi-sensor data fusion to the systematic review of AI techniques employed in multiple fault diagnosis. In this regard, this paper attempts to achieve the same by implementing a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines using the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) method. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. This paper identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines. The paper also identifies the major challenges, research gap. It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field. [ABSTRACT FROM AUTHOR]

Human motion analysis plays a crucial role in industry 4.0 and, more recently, in industry 5.0 where human-centered applications are becoming increasingly important, demonstrating its potential for enhancing safety, ergonomics and productivity. Considering this opportunity, an increasing number of studies are proposing works on the analysis of human motion in an industrial context, taking advantage of the rise of artificial intelligence technologies and sensor technologies. The objective of this work is to provide a review of recent studies exploring these technologies in the analysis of human movement while specifically considering industrial context. First, a taxonomy of key human motion analysis applications is proposed, presenting statistical insights to reveal trends and highlighting lacks in current research. Furthermore, this work identifies benchmark datasets acquired in various industrial case studies and associated sensors. Many recommendations for selecting optimal sensors and valuable benchmarks are proposed. Then, the paper outlines the current trend of utilizing hybrid deep learning methodologies in human movement analysis while underscoring the performance and limitations of these proposed methods, considering industrial constraints such as real-time recognition and frugality. Finally, challenges and future works are highlighted, focusing on the opportunities to address problems related to the complex industrial environment in order to achieve reliable performances. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup. This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets' specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, a hydraulic system harnesses liquid fluid power to create powerful machines. Under the trend of Industry 4.0, the predictive maintenance of hydraulic systems is transforming to more intelligent and automated approaches that leverage the strong power of artificial intelligence and data science technologies. However, due to the knowledge-intensive and heterogeneous nature of the manufacturing domain, the data and information required for predictive maintenance are normally collected from ubiquitous sensing networks. This leads to the gap between massive heterogeneous data/information resources in hydraulic system components and the limited cognitive ability of system users. Moreover, how to capture and structure useful domain knowledge (in a machine-readable way) for solving domain-specific tasks remains an open challenge for the predictive maintenance of hydraulic systems. To address these challenges, in this paper we propose a virtual knowledge graph-based approach for the digital modeling and intelligent predictive analytics of hydraulic systems. We evaluate the functionalities and effectiveness of the proposed approach on a predictive maintenance task under real-world industrial contexts. Results show that our proposed approach is capable and feasible to be implemented for digital modeling, data access, data integration, and predictive analytics. • A domain ontology HSO is built to formalise domain knowledge about hydraulic systems. • A virtual knowledge graph system is explored to facilitate data integration. • An intelligent predictive maintenance approach is proposed to predict degradation. • The effectiveness of the proposed approach is evaluated on a real-world case study. [ABSTRACT FROM AUTHOR]

To build intelligent manufacturing systems that react flexibly in case of failures or unexpected circumstances, manufacturing capabilities of production systems must be utilized as much as possible. Artificial Intelligence (AI) and, in particular, automated planning can contribute to this by enabling flexible production processes. To efficiently leverage automated planning, an almost complete planning domain description of the real-world is necessary. However, creating such planning descriptions is a demanding and error-prone task that requires high manual efforts even for domain experts. In addition, maintaining the encoded knowledge is laborious and, thus, can lead to outdated domain descriptions. To reduce the high efforts, already existing knowledge can be reused and transformed automatically into planning descriptions to benefit from organization-wide knowledge engineering activities. This paper presents a novel approach that reduces the described efforts by reusing existing knowledge for planning and scheduling in Industry 4.0 (I4.0). For this purpose, requirements for developing a converter that transforms existing knowledge are derived from literature. Based on these requirements, the SWS2PDDL converter is developed that transforms the knowledge into formal Planning Domain Definition Language (PDDL) descriptions. The approach's usefulness is verified by a practical evaluation with a near real-world application scenario by generating failures in a physical smart factory and evaluating the generated re-planned production processes. When comparing the resulting plan quality to those achieved by using a manually modeled planning domain by a domain expert, the automatic transformation by SWS2PDDL leads to comparable or even better results without requiring the otherwise high manual modeling efforts. • Literature-based requirements for transforming semantic models into planning domains. • Literature-based requirements for using AI planning in business process management. • Applying design science for developing a converter for reusing semantic models. • Using AI planners to manually created and automatically converted planning domains. • Evaluation on a smart factory for verifying the suitability of the converter. [ABSTRACT FROM AUTHOR]

The recognition of human actions based on artificial intelligence methods to enable Human–Robot Collaboration (HRC) inside working environments remains a challenge, especially because of the necessary huge training datasets needed. Meanwhile, Digital Twins (DTs) of human centered productions are increasingly developed and used in the design and operation phases. As instance, DTs are already helping industries to design, visualize, monitor, manage, and maintain their assets more effectively. However, few works are dealing with using DTs as a dataset generator tool. Therefore, this paper explores the use of a DT of a real industrial workstation involving assembly tasks with a robotic arm interfaced with Virtual Reality (VR) to extract a digital human model. The DT simulates assembly operations performed by humans aiming to generate self-labeled data. Thereby, a Human Action Recognition dataset named InHARD-DT was created to validate a real use case in which we use the acquired auto-labeled DT data of the virtual representation of the InHARD dataset to train a Spatial–Temporal Graph Convolutional Neural Network with skeletal data on one hand. On the other hand, the Physical Twin (PT) data of the InHARD dataset was used for testing. Obtained results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]