Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Development and implementation of preventive-maintenance practices in Nigerian industries.

A methodology for the development of PM using the modern approaches of FMEA, root-cause analysis, and fault-tree analysis is presented. Applying PM leads to a cost reduction in maintenance and less overall energy expenditure. Implementation of PM is preferable to the present reactive maintenance procedures (still prevalent in Nigeria

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms

Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the Industry 4.0 or fourth industrial revolution. There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case.This research was funded by the HAZITEK call of the Basque Government, project acronym HORDAGO

Cyber-Enabled Product Lifecycle Management: A Multi-Agent Framework

Trouble free use of a product and its associated services for a specified minimum period of time is a major factor to win the customer\u27s trust in the product. Rapid and easy serviceability to maintain its functionalities plays a key role in achieving this goal. However, the sustainability of such a model cannot be promised unless the current health status of the product is monitored and condition-based maintenance is exercised. Internet of Things (IoT), an important connectivity paradigm of recent times, which connects physical objects to the internet for real-time information exchange and execution of physical actions via wired/wireless protocols. While the literature is full of various feasibility and viability studies focusing on architecture, design, and model development aspects, there is limited work addressing an IoT-based health monitoring of systems having high collateral damage. This motivated the research to develop a multi-agent framework for monitoring the performance and predicting impending failure to prevent unscheduled maintenance and downtime over internet, referred to as for cyber-enabled product lifecycle management (C-PLM). The framework incorporates a number of autonomous agents, such as hard agent, soft agent, and wave agent, to establish network connectivity to collect and exchange real-time health information for prognostics and health management (PHM). The proposed framework will help manufacturers not only to resolve the warranty failure issues more efficiently and economically but also improve their corporate image. The framework further leads to efficient handling of warranty failure issues and reduces the chances of future failure, i.e., offering durable products. From the sustainability point of view, this framework also addresses the reusability of the parts that still have a significant value using the prognostics and health data. Finally, multi-agent implementation of the proposed approach using a power substations for IoT-based C-PLM is included to show is efficacy

Maintenance optimization in industry 4.0

This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the uncertainties affecting the behavior of the systems and the environment, and can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience

A Constructivist Grounded Theory Study of Airfield Lighting Maintenance Management Strategy

Asset management programs can keep senior airport managers informed of the performance and life-cycle costs of assets critical to airport operations. With this information, managers can adjust operations and maintenance to minimize costs without sacrificing service quality. However, program implementation is costly and time-consuming. In addition to management and information technology changes, the individual maintenance shops must also develop and incorporate new data collection processes into their everyday workflow. Knowledgeable and experienced maintenance managers must evaluate the data, consider alternatives, and find strategies to reduce costs without negative impact. Unfortunately, such managers are rare for highly specialized assets like airfield lighting systems and often gain most of their experience working at one airport. This research investigated the maintenance strategies most often used for airfield lighting, examined which criteria affected strategy choices, and asked how managers make their selections. The researcher interviewed 23 participants from 15 airports, including facility managers, maintenance engineers, and supervisors. Interview statements were first individually coded in detail and then grouped using focused codes to enable the continuous comparison of each organization’s approach to addressing common problems. Ultimately, the analysis identified eight primary criteria that managers should consider when selecting a maintenance strategy. The process used by U.S. commercial service airports for selecting a maintenance management strategy is modeled as a Multi-Criteria Decision-Making (MCDM) problem. The model includes a problem goal, the criteria affecting the decision, and all the possible alternatives. MCDM models can employ various quantitative decision support systems such as Simple Additive Weighting (SAW), which requires subject matter experts to assign weights to the performance of the multiple alternatives for each of the criteria. However, the research shows that airports consistently use an intuitive decision-making process that relies on the expertise and experience of their maintenance staff. Therefore, this research constructed a theory of airfield lighting maintenance strategy selection modeled as an MCDM problem using an intuitive decision support system. Maintenance managers should consider each of the following criteria when considering their work strategy: access, environment, regulations, budget, design, condition, impetus, and staff. Data analysis also found nine alternative maintenance strategies divided into corrective and preventive types. Corrective maintenance involves action after an asset degradation or failure has occurred. Preventive maintenance is the action taken before problems to prevent degradation and failure. Research shows that maintenance managers consider corrective maintenance to be less costly. However, overuse of corrective maintenance results in higher risks of unexpected asset failure and higher costs over the long-term. In comparison, preventive maintenance may require more daily effort but yields more reliable system performance and lower asset life-cycle costs. In practice, successful maintenance requires using both strategies. Asset management practices require maintenance managers to measure and analyze their system performance, then regularly consider how they might change the maintenance program to minimize operating and maintenance costs without sacrificing performance. This research provides information helpful to maintenance managers with their strategy selection. Future research should investigate developing a quantitative decision-support system that maintenance managers could integrate into the current process and potentially deploy to maintenance organizations wanting supplemental guidance

Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms

[EN] Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the Industry 4.0 or fourth industrial revolution. There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case.This research was funded by the HAZITEK call of the Basque Government, project acronym HORDAGO.Goti, A.; Oyarbide-Zubillaga, A.; Alberdi, E.; Sánchez Galdón, AI.; Garcia-Bringas, P. (2019). Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms. Applied Sciences. 9(15). https://doi.org/10.3390/app915306891