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

Joint maintenance-inventory optimisation of parallel production systems

We model a joint inspection and spare parts inventory policy for maintaining machines in a parallel system, where simultaneous downtime seriously impacts upon production performance and has a significant financial consequence. This dependency between system components means that analysis of realistic maintenance models is intractable. Therefore we use simulation and a numerical optimisation tool to study the cost-optimality of several policies. Inspection maintenance is modelled using the delay-time concept. Critical spare parts replenishment is considered using several variants of a periodic review policy. In particular, our results indicate that the cost-optimal policy is characterised by equal frequencies of inspection and replenishment, and delivery of spare parts that coincides with maintenance intervention. In general, our model provides a framework for studying the interaction of spare parts ordering with maintenance scheduling. The sensitivity analysis that we present offers insights for the effective management of such parallel systems, not only in a paper-making plant, which motivates our modelling development, but also in other manufacturing contexts

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

Maintenance Optimization and Inspection Planning of Wind Energy Assets: Models, Methods and Strategies

Designing cost-effective inspection and maintenance programmes for wind energy farms is a complex task involving a high degree of uncertainty due to diversity of assets and their corresponding damage mechanisms and failure modes, weather-dependent transport conditions, unpredictable spare parts demand, insufficient space or poor accessibility for maintenance and repair, limited availability of resources in terms of equipment and skilled manpower, etc. In recent years, maintenance optimization has attracted the attention of many researchers and practitioners from various sectors of the wind energy industry, including manufacturers, component suppliers, maintenance contractors and others. In this paper, we propose a conceptual classification framework for the available literature on maintenance policy optimization and inspection planning of wind energy systems and structures (turbines, foundations, power cables and electrical substations). The developed framework addresses a wide range of theoretical and practical issues, including the models, methods, and the strategies employed to optimise maintenance decisions and inspection procedures in wind farms. The literature published to date on the subject of this article is critically reviewed and several research gaps are identified. Moreover, the available studies are systematically classified using different criteria and some research directions of potential interest to operational researchers are highlighted

Information-Based Maintenance Optimization with Focus on Predictive Maintenance (Informatiegebaseerde onderhoudsoptimalisatie met focus op predictief onderhoud)

This dissertation presents an information-based maintenance optimization methodology for physical assets; with focus on, but not limited to, predictive maintenance (PdM). The overall concept of information-based maintenance is that of updating maintenance decisions based on evolving knowledge of operation history and anticipated usage of the machinery, as well as the physics and dynamics of material degradation in critical machinery components. Within this concept, predictive maintenance is a maintenance policy that specifically uses predictions of component remaining useful life (RUL) to dynamically schedule maintenance activities. Analysis of the available information-based maintenance methodologies and e-maintenance standards identified the development of advanced maintenance policies like predictive maintenance as the most important challenge. Generally speaking, within e-maintenance the sensor module, the signal-processing module, the condition monitoring module and the diagnostic model can all be (partially) developed using standard means and models. However, this is currently not the case for the decision support modules. Moreover, the evolution of maintenance is not solely based on technical but rather on techno-economic considerations. The right maintenance decision making structure should be in place to fully exploit the potential of these new emerging technologies. Therefore, decision support models and tools for predictive maintenance performance evaluation and optimization are developed in this thesis. Hence, a detailed study on the business economics related to the implementation of an information-based/predictive maintenance policy is performed. Predictive maintenance models for long-term performance evaluation, real-time and dynamic decision making and a combination of both are developed. As such contributions are made towards (i) the development of an imperfect condition monitoring system (CMS) model, (ii) predictive maintenance models incorporating product quality and production capacity and (iii) a dynamic predictive maintenance policy for complex dependent multi-component systems. These models provide maintenance decision support in order to take cost-effective decisions based on predictive maintenance information. Moreover, they provide sound business insight for the justification of PdM and as such assist to determine the cases in which PdM is expected to be very beneficial, beneficial, neutral or possibly too expensive. Furthermore, the effect of predictive maintenance information on inventory management decisions is studied. The major contribution of this dissertation lies within the development of predictive maintenance models. However, contributions to other problems within maintenance management, like (i) the urge for more application based maintenance optimization, (ii) the limited scope with regard to maintenance objectives and criteria and (iii) the availability of maintenance data, are made. As such most of the developed models are applied to real-life case studies to illustrate their applicability in an industrial setting. A methodology, based on the analytic network process (ANP), is developed to select and prioritize business specific maintenance objectives and criteria. And finally, the developed models possess the capability to solve the data problem by providing the maintenance decision maker the right information at the right time to make the right maintenance decision.nrpages: 280status: publishe

Quantifying the Benefit of Prognostic Information in Maintenance Decision Making

Many models and methodologies to predict the remaining useful life (RUL) of a component or system are investigated nowadays. However, decision making based on these predictions (RUL) is still an underexplored area in maintenance management. The objective of this paper is to quantify the added value of this prognostic information (RUL). This is done by constructing a stochastic discrete-event simulation model, which optimizes maintenance action scheduling, based on prognostic information on the different components. Cost and availability criteria are taken into account in this optimization model as the objectives. The added value of the prognostic information is determined by comparing this prognostic maintenance policy to four other conventional maintenance policies: corrective maintenance, preventive maintenance, offline condition-based maintenance and online condition-based maintenance. The benefit of prognostic information and the stochastic discrete-event simulation model are validated by a real life case study on bearings of manufacturing equipment. Looking at more than one machine, a plant level approach is taken in the case study.no ISBNstatus: publishe

Selection of Business Specific Maintenance Criteria Using the Analytic Network Process (ANP)

The maintenance optimization criteria used in most of the maintenance optimization models are not addressing the business specific context of a company. Mostly only cost is taken into account and sometimes availability of the equipment. Safety only comes into the picture in very specific cases, like for example nuclear power plants. But there is never a clear proof that these criteria are the real objectives that need to be optimized to get to the optimal maintenance strategy. Moreover, there does not exist a clear cut method to determine which optimization criteria are important in certain environments, which makes it difficult to determine business specific maintenance optimization criteria. This can lead to suboptimal solutions to a maintenance optimization problem. Because there is no clear general method of how to perform maintenance optimization with the right objectives, all optimization models have a limited scope. The objective of this research is to develop a generic method to determine the maintenance optimization criteria for a business specific environment by applying the analytic network process (ANP). This ensures that the optimization objectives in a maintenance optimization model are the right ones, which assures that an optimal solution to the maintenance optimization problem is found. Of course it is also very important that the maintenance objectives are in line with the general business objectives of a company. The developed maintenance criteria selection tool is applied to two case studies. This selection method will be a first step in developing a generic maintenance optimization approach. After selection of the business specific maintenance criteria, these criteria and their corresponding weights will be used as objective functions in a maintenance optimization model.status: publishe