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Industry 4.0 presents nine technologies, including the Industrial Internet of Things (IIoT), Big Data and Analytics, Cloud Computing, etc. The progress of Industry 4.0 places a new demand for maintenance. Some of the nine technologies of Industry 4.0, such as IIoT, Big Data and Analytics, Cloud Computing and Augmented Reality (AR), as well as Cyber-Physical System (CPS) and machine learning, play an important in the development of smart maintenance technologies. In smart maintenance research, it is presented how IIoT can be used for machine connection and maintenance data collection, machine learning models for maintenance data analysis and failure prediction, AR for maintenance instructions, etc. Although previous smart maintenance research presents many technologies for smart maintenance, the manufacturing companies still face many implementation challenges when implementing and using to add benefits to maintenance organizations in line with companies main goals. Many manufacturing companies still utilize reactive maintenance and are experiencing too much downtime. In this paper, I have shown how two machine learning models, Isolation Forest and Regression Learner, and the statistical technique, Interquartile range (IQR), can be applied in order to detect anomalies in an unsupervised dataset, consisting of travel time for a linear guide, and temperature, in a drill station, which is part of a Cyber-Physical production system, located at a smart production laboratory in Sweden.

Research within smart maintenance has become a popular research topic largely focused on how the nine technologies of Industry 4.0, such as Industrial Internet of Things (IIoT) and Augmented Reality (AR), as well as Artificial Intelligence (AI) and Cyber Physical System (CPS), can be used for, e.g., condition monitoring of equipment, remote services, modelling wear of components, calculating Remaining Useful Life (RUL) and prediction of failure. Due to the new generation of maintenance, new skills are required regarding the interaction between humans and technologies. Human-technology interaction in smart maintenance research is not highlighted in a structured way and according to any type of socio-technical system. The aim of the paper is to study the challenges and their associated theoretically grounded enablers regarding the implementation of and use of smart maintenance technologies, through the interplay between humans, technologies and organizations. The paper is based on empirical data from seven large manufacturing companies in Sweden as well as a literature review.

Industry 4.0 is usually presented as usage of technologies. Some of these play an important role in the development of smart maintenance technologies. However, although the subject of smart maintenance has been discussed for more than 10 years, the manufacturing industry still finds it challenging to implement smart maintenance technologies to add benefits to maintenance organizations in line with company’s goals. This study presents a conceptual process for implementing smart maintenance technologies, challenges and enablers to consider when implementing, and benefits. This article is based on an analysis of empirical findings from seven large manufacturing companies in Sweden, previous maintenance research, and authors’ three previous smart maintenance research articles. In the first article, the authors explored perspectives on smart maintenance technologies from 11 large companies within the manufacturing industry, while in the second one, perspectives on smart maintenance technologies from 15 manufacturing Small and medium-sized enterprises (SMEs) were presented. In the third and final one, the authors developed and presented a testbed for smart maintenance technologies.

Industry 4.0 presents nine technologies including Industrial Internet of Things (IIoT), Big Data and Analytics, Augmented Reality (AR), etc. Some of the technologies play an important role in the development of smart maintenance technologies. Previous research presents several technologies for smart maintenance. However, one problem is that the manufacturing industry still finds it challenging to implement smart maintenance technologies in a value-adding way. Open questionnaires and interviews have been used to collect information about the current needs of the manufacturing industry. Both the empirical findings of this paper, as well as previous research, show that knowledge is the most common challenge when implementing new technologies. Therefore, in this paper, we develop and present a testbed for how to approach smart maintenance technologies and to share technical knowledge to the manufacturing industry.

Vibration signal analysis is an effective tool for fault diagnosis in industrial/manufacturing machinery. Gearboxes are a fundamental component of many industrial machines, and their failure can cause significant downtime, production losses, and safety hazards. Analyzing vibration signals makes it possible to detect, classify, and diagnose faults in gearboxes, enabling timely maintenance and preventing catastrophic failures. Vibration signals are sensitive to changes in the operating conditions and internal components of gearboxes, making them a reliable indicator of potential faults. This paper introduces a new vibration signal data set, referred to as VibraFault, which has been the focus of the ICPHM23 data challenge. The dataset contains vibration signals acquired from a test rig consisting of a driving motor, a two-stage planetary gearbox, a two-stage parallel gearbox, and a magnetic brake. The experiments include various operating conditions and focus on common sun gear faults on the planetary gearbox, such as surface wear, chipped, crack, and tooth-missing. For each operating condition, normal and fault vibration signals have been recorded at a sampling frequency of 10 kHz. Vibration signals have been collected in three directions to facilitate more comprehensive research studies on mapping between different types of faults and the system's vibration response. The dataset has the potential to promote research in fault diagnosis, particularly in the development of advanced solutions based on Machine Learning (ML) and Deep Neural Networks (DNN).

The advanced technologies available in the development of Smart Maintenance within Industry 4.0 have the potential to significantly improve the efficiency of industrial maintenance. However, it is important to be careful when deciding which technologies to implement for a given application and when evaluating the quality of the data generated. Otherwise, what should be cost-effective solutions may end up being cost-driving. The use of domain knowledge in selecting, developing, implementing, setting up, and utilizing these technologies is increasingly important for achieving success. In this paper, we will elaborate on this topic by presenting and analyzing insights from industrial cases, drawing on the authors’ extensive experience in the field.

Industry 4.0 consists of nine technological pillars: IIoT, Cloud Computing, Big Data and Analytics, AR, etc. Some of the pillars play an essential role in maintenance development. Previous research presents many technologies for smart maintenance, but one prevailing problem is that there are still challenges to implementing smart maintenance technologies cost-effectively in the manufacturing industry. Therefore, we explore perspectives on smart maintenance technologies from respondents within 15 manufacturing SMEs. We start by investigating whether the companies had implemented smart maintenance technologies, if so, in what context. Then, we explore perspectives from the manufacturing SMEs on added values, challenges, opportunities, advantages, and disadvantages of smart maintenance technologies. However, as none of the case companies had implemented any Smart Maintenance Technologies, only implementation challenges could be investigated.

This handbook is developed for thesis students, as well as academic supervisors and industrial supervisors. The handbook aims to clarify the thesis process and make it easier for you to work as a team. By following this process, you will make sure that the thesis will have both academic and industrial contributions.

The manufacturing industry faces significant technical challenges due to the industry 4.0 technologies, which play an essential role in maintenance development. Maintenance in industry 4.0, also named smart maintenance, maintenance 4.0, predictive maintenance, etc., is boosted using industry 4.0 technologies, such as Industrial Internet of Things (IIoT), Big Data and Analytics, Cloud Computing, Augmented Reality (AR), Additive Manufacturing (AM), etc. Previous research presents several smart maintenance technologies, but the manufacturing industry still finds it challenging to implement the technologies cost-effectively. One problem is that there is insufficient research on how smart maintenance technologies can be implemented cost-effectively and add value to the manufacturing industry. Therefore, this paper aims to explore perspectives on smart maintenance technologies: 1) if there are any implemented smart maintenance technologies, 2) in what context, 3) added values, 4) challenges, 5) opportunities, 6) advantages, and 7) disadvantages with the technologies. This paper presents the results of a case study based on an online open questionnaire with respondents working in maintenance organizations in large manufacturing companies. 

Industry 4.0 presents nine technologies including Industrial Internet of Things (IIoT), Big Data and Analytics, Augmented Reality (AR), etc. Some of the technologies play an important role in the development of smart maintenance technologies. Previous research presents several technologies for smart maintenance. However, one problem is that the manufacturing industry still finds it challenging to implement smart maintenance technologies in a value-adding way. Open questionnaires and interviews have been used to collect information about the current needs of the manufacturing industry. Both the empirical findings of this paper, as well as previous research, show that knowledge is the most common challenge when implementing new technologies. Therefore, in this paper, we develop and present a testbed for how to approach smart maintenance technologies and to share technical knowledge to the manufacturing industry.