Alexandros Bousdekis

A proactive decision making framework for condition-based maintenance

– The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM. , – Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed. , – In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified. , – The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required. , – The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance

In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to predict the health state of equipment and to continuously update maintenance-related recommendations. The current work aims at providing a literature review for prognostic-based decision support methods for CBM. We analyse the literature in order to identify combinations of methods for prognostic-based decision support for CBM, propose a practical technique for selecting suitable combinations of methods and set the guidelines for future research.

A Real-Time Architecture for Proactive Decision Making in Manufacturing Enterprises

We outline a new architecture for supporting proactive decision making in manufacturing enterprises. We argue that event monitoring and data processing technologies can be coupled with decision methods effectively providing capabilities for proactive decision-making. We present the main conceptual blocks of the architecture and their role in the realization of the proactive enterprise. We illustrate how the proposed architecture supports decision-making ahead of time on the basis of real-time observations and anticipation of future undesired events by presenting a practical condition-based maintenance scenario in the oil and gas industry. The presented approach provides the technological foundation and can be taken as a blueprint for the further development of a reference architecture for proactive applications.

Continuous Improvement of Proactive Event-driven Decision Making through Sensor-Enabled Feedback (SEF)

The incorporation of feedback in the proactive event-driven decision making can improve the recommendations generated and be used to inform users online about the impact of the recommended action following its implementation. We propose an approach for learning cost functions from Sensor-Enabled Feedback (SEF) for the continuous improvement of proactive event-driven decision making. We suggest using Kalman Filter, dynamic Curve Fitting and Extrapolation to update online (i.e. during action implementation) cost functions of actions, with the aim to improve the parameters taken into account for generating recommendations and thus, the recommendations themselves. We implemented our approach in a real proactive manufacturing scenario and we conducted extensive experiments in order to validate its effectiveness.

Condition-Based Predictive Maintenance in the Frame of Industry 4.0

The 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 Proactive Model for Joint Maintenance and Logistics Optimization in the Frame of Industrial Internet of Things

Equipment failures in manufacturing processes concern industries because they can lead to severe issues regarding human safety, environmental impact, reliability, and production costs. The stochastic nature of equipment degradation and the uncertainty about future breakdowns affect significantly the maintenance and inventory decisions. Proactive event processing can facilitate this decision-making process in an Industrial Internet of Things (IIoT) environment, but real-time data processing poses several challenges in efficiency and scalability of the associated information systems. Therefore, appropriate real-time, event-driven algorithms and models are required for deciding on the basis of predictions, ahead of time. We propose a proactive event-driven model for joint maintenance and logistics optimization in a sensor-based, data-rich industrial environment. The proposed model is able to be embedded in a real-time, event-driven information system in order to be triggered by prediction events about the future equipment health state. Moreover, the proposed model handles multiple alternative (imperfect and perfect) maintenance actions and associated spare parts orders and facilitates proactive decision making in the context of Condition-Based Maintenance (CBM). The proposed proactive decision model was validated in real industrial environment and was further evaluated with a comparative and a sensitivity analysis.

Enabling condition-based maintenance decisions with proactive event-driven computing

Abstract Condition Based Maintenance (CBM) can take advantage of the emergence of Internet of Things (IoT) and the proactive event-driven computing paradigm for fully exploiting its capabilities by enabling proactive maintenance decisions ahead of time. In this paper, proactive event-driven computing is used as a lever in order to provide a holistic CBM approach along with an information system for generating proactive maintenance recommendations. Since the Detect and the Predict phases of the “Detect-Predict-Decide-Act” proactivity principle as applied to the CBM framework are well-studied fields in literature, the focus of the current research is on the Decide phase that is still an unexplored area. Therefore, in the context of this paper, the approach, contributing decision methods and technical specifications of an associated information system are defined and developed for this phase. The proposed approach is implemented in an information system, which is deployed and evaluated in the context of two real industrial scenarios in the area of oil and gas and automotive lighting equipment industries. The results of a comparative and a sensitivity evaluation analysis show that the proposed approach can enable industrial business transformation from a reactive to a proactive mode of operation, in order to eliminate maintenance-related losses and optimize business performance.

A Framework for Integrated Proactive Maintenance Decision Making and Supplier Selection

The increasing use of sensors in manufacturing enterprises has led to the need for real-time data-driven information systems capable of processing huge amounts of data in order to provide meaningful insights about the actual and the predicted business performance. We propose a framework for real-time, event-driven proactive supplier selection driven by Condition Based Maintenance (CBM). The proposed framework was tested in a real in automotive lighting equipment scenario.