Applying Predictive Maintenance in Flexible Manufacturing

In Industry 4.0 context, manufacturing related processes e.g. design processes, maintenance processes are collaboratively processed across different factories and enterprises. The state i.e. operation, failures of production equipment tools could easily impact on the collaboration and related processes. This complex collaboration requires a flexible and extensible system architecture and platform, to support dynamic collaborations with advanced capabilities such as big data analytics for maintenance. As such, this paper looks at how to support data-driven and flexible predictive maintenance in collaboration using FIWARE? Especially, applying big data analytics and data-driven approach for effective maintenance schedule plan, employing FIWARE Framework, which leads to support collaboration among different organizations modularizing of different related functions and security requirements

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

Strategic maintenance technique selection using combined quality function deployment, the analytic hierarchy process and the benefit of doubt approach

The business performance of manufacturing organizations depends on the reliability and productivity of equipment, machineries and entire manufacturing system. Therefore, the main role of maintenance and production managers is to keep manufacturing system always up by adopting most appropriate maintenance methods. There are alternative maintenance techniques for each machine, the selection of which depend on multiple factors. The contemporary approaches to maintenance technique selection emphasize on operational needs and economic factors only. As the reliability of production systems is the strategic intent of manufacturing organizations, maintenance technique selection must consider strategic factors of the concerned organization along with operational and economic criteria. The main aim of this research is to develop a method for selecting the most appropriate maintenance technique for manufacturing industry with the consideration of strategic, planning and operational criteria through involvement of relevant stakeholders. The proposed method combines quality function deployment (QFD), the analytic hierarchy process (AHP) and the benefit of doubt (BoD) approach. QFD links strategic intents of the organizations with the planning and operational needs, the AHP helps in prioritizing the criteria for selection and ranking the alternative maintenance techniques, and the BoD approach facilitates analysing robustness of the method through sensitivity analysis through setting the realistic limits for decision making. The proposed method has been applied to maintenance technique selection problems of three productive systems of a gear manufacturing organization in India to demonstrate its effectiveness

The effect of maintenance quality on spare parts inventory for a fleet of assets

This paper considers the effect of fleet size on a joint policy of maintenance and spare parts inventory when spare parts are of varying quality. We consider N identical one-component systems subject to age-based replacement, and with a single echelon periodic review spare-parts policy. The joint policy is optimised with regard to the long-run total cost per unit time, where the cost components include both replacement and inventory related costs. In particular, we are interested in the effect of spare parts quality and the size of the fleet on the variability in the demand for spare parts. Furthermore, the effects of changing lead time, different failure characteristics, and simultaneous deployment of the N systems over a finite horizon on the optimal joint policy are investigated. We develop a stochastic simulation model to investigate these effects. We find that the scale effect varies with the quality of spare parts: the poorer the quality of spare parts, the smaller the scale effect. Our approach allows the value (e.g. cost of poor quality spare parts) in spare parts provisioning for maintenance to be quantified

Condition-Based Predictive Maintenance in the Frame of Industry 4.0

Part 6: Intelligent Diagnostics and Maintenance SolutionsInternational audienceThe emergence of Industry 4.0 leads to the optimization of all the industrial operations management. Maintenance is a key operation function, since it contributes significantly to the business performance. However, the definition and conceptualization of Condition-based Predictive Maintenance (CPM) in the frame of Industry 4.0 is not clear yet. In the current paper, we: (i) explicitly define CPM in the frame of Industry 4.0 (alternatively referred as Proactive Maintenance); (ii) develop a unified approach for its implementation; and, (iii) provide a conceptual architecture for associated information systems

A Fog Computing Approach for Predictive Maintenance

Technological advances in areas such as communications, computer processing, connectivity, data management are gradually introducing the internet of things (IoT) paradigm across companies of different domain. In this context and as systems are making a shift into cyberphysical system of systems, connected devices provide massive data, that are usually streamed to a central node for further processing. In particular and related to the manufacturing domain, Data processing can provide insight in the operational condition of the organization or process monitored. However, there are near real time constraints for such insights to be generated and data-driven decision making to be enabled. In the context of internet of things for smart manufacturing and empowered by the aforementioned, this study discusses a fog computing paradigm for enabling maintenance related predictive analytic in a manufacturing environment through a two step approach: (1) Model training on the cloud, (2) Model execution on the edge. The proposed approach has been applied to a use case coming from the robotic industry