Towards an Industry 4.0-Based Maintenance Approach in the Manufacturing Processes

Towards an Industry 4.0-Based Maintenance Approach in the Manufacturing Processes

J. Roberto Reyes García, Alberto Martinetti, Juan M. Jauregui Becker, Sarbjeet Singh, Leo A. M. van Dongen
DOI: 10.4018/978-1-5225-7152-0.ch008
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Maintenance is one of the key application areas of Industry 4.0. Every day, maintenance managers and technicians face the challenge of ensuring maximum machine reliability and availability, while minimizing the utilization of materials consumed by maintenance and repairs. As productivity is pressured to further improve, finding a successful balance between these aspects is becoming increasingly difficult. Therefore, integrating condition-monitoring systems with predictive and prescriptive maintenance principles, a new Industry 4.0-based maintenance can be obtained that enables maintenance engineers to better deal with this challenge. In this context, Maintenance 4.0 expands existing maintenance functions by the integration of Industry 4.0 technologies, like internet of things, cyber physical systems, augmented reality, and 3D printing. This chapter presents the main maintenance areas that are supported and enabled by Industry 4.0 technologies and introduces an Industry 4.0-based predictive maintenance approach for the manufacturing industry.
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Industry 4.0 And Maintenance

The term Industry 4.0 (I4.0) refers to a new technological vision on how management of manufacturing and production processes can be redefined through the implementation of advanced information technologies, robotics and monitoring devices (Kagermann, Wahlster, & Helbig, 2013) (Deloitte AG, 2015) (Gottorp Jeppesen, 2015). The “4.0” designation suggests that the world is now facing the fourth industrial revolution.

Figure 1 illustrates the transformation of manufacturing industry through the four industrial revolutions moments.

Figure 1.

Industrial revolutions’ timeline

Source: Gottorp Jeppesen, 2015

The First Industrial Revolution ran from 1760 to 1840; it was driven by the introduction of mechanical production facilities with the help of water and steam power to replace manual labor. It includes the adoption of structured chemical manufacturing and iron production processes. In general terms, it can be summarized as the rise of factory systems.

The Second Industrial Revolution, also known as the Technological Revolution, ran from 1870 to 1914 and was driven by the introduction of the division of labor and mass production with the help of electrical power. The factory systems were developed to allow mass production through assembly lines. This booming period was characterized by inventions that radically changed the life style: telegraph and telephone, typewriter, lightbulb, high voltage electric current, first car, first plane and compressed-air brake.

The Third Industrial Revolution, known as the Digital Revolution, started in the 1960’s and marked the change from analogue and mechanical technology to digital technology that further automates production.

Programmable Logic Controller (PLC) is universally considered the main expression of this radical change in production manufacturing. It enabled to automate several processes reducing the human involvement.

Finally, the Fourth Industrial revolution started in 2010 and is based on Cyber-Physical Systems (CPS) and the use of the internet to create networks in the production environment and bring services to the customers and the organizations themselves (Kagermann, Wahlster, & Helbig, 2013) (Deloitte AG, 2015) (PwC, 2016) (Geissbauer, Vedso, & Schrauf, 2016) (Bruurmijn, 2016) (Sniderman, Mahto, & Cotteleer, 2016) (Khan & Turowski, 2016).

In general terms, I4.0 is a new production paradigm in which manufacturing industry and production processes will be redefined through the implementation of advanced information technologies, robotics, monitoring devices, and the internet (Kagermann, Wahlster, & Helbig, 2013) (Deloitte AG, 2015) (Sniderman, Mahto, & Cotteleer, 2016). Moreover, Industry 4.0 enables new business models focused on services. In the very short future, there will be millions of intelligent production facilities that are all across connected, and they will provide huge quantities of data (big data) about their own operating conditions and product statuses in the cloud (BDI, 2016). All that big data can be used to optimize products and the process themselves. Moreover, smart algorithms can link existing (historical) data to new (incoming) information and predict disturbances caused by the new operational conditions.

Besides, big data provides a foundation for offering both to the workers of the production facilities and to the customers, personalized data-based services in addition to the physical end-product. For instance, operators can collect and analyze data about the total amount of raw materials they get for all of the machines they are responsible for, and use it to generate new services, such as an automatic reconfiguration planning that can cope with the lack of materials. Customers on the other hand, can use the data to get services such as personalized reports in their own IT system, with the information of the possible delays and new forecasted delivery time of their products.

Key Terms in this Chapter

Predictive Maintenance (PdM): Maintenance policy focused on the predicted condition of the system based on impending failures as results of data, patterns, and trends analysis.

Internet of Things (IoT): The infrastructure that connects physical devices.

Cyber-Physical Systems (CPS): Environment where physical and digital components are tightly interconnected.

Industrial Internet of Things and Services (IIoTS): The use of internet of things technologies (machine learning, big data, etc.) to enhance manufacturing and industrial processes.

Prescriptive Maintenance (RxM): Maintenance policy focused on PdM actions, empowered with provision of specific recommendations towards a solution-finding process.

Computerized Maintenance Management Systems (CMMS): A computerized database that stores and manages all the information necessary for maintenance operations.

Condition-Based Maintenance (CBM): Maintenance policy focused on the condition of the system based on conditions and remote monitoring.

Condition Monitoring (CM): The set of processes and activities meant to track parameters of a machine or an infrastructure.

Software-as-a-Service (SaaS): Business model based on offering software licensed on subscription and, generally, centrally hosted.

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