Integrated production quality and condition-based maintenance optimisation for a stochastically deteriorating manufacturing system

This paper investigates the problem of optimally integrating production quality and condition-based maintenance in a stochastically deteriorating single- product, single-machine production system. Inspections are periodically performed on the system to assess its actual degradation status. The system is considered to be in ‘fail mode’ whenever its degradation level exceeds a predetermined threshold. The proportion of non-conforming items, those that are produced during the time interval where the degradation is beyond the specification threshold, are replaced either via overtime production or spot market purchases. To optimise preventive maintenance costs and at the same time reduce production of non-conforming items, the degradation of the system must be optimally monitored so that preventive maintenance is carried out at appropriate time intervals. In this paper, an integrated optimisation model is developed to determine the optimal inspection cycle and the degradation threshold level, beyond which preventive maintenance should be carried out, while minimising the sum of inspection and maintenance costs, in addition to the production of non-conforming items and inventory costs. An expression for the total expected cost rate over an infinite time horizon is developed and solution method for the resulting model is discussed. Numerical experiments are provided to illustrate the proposed approach

Optimal inspection and maintenance for stochastically deteriorating systems

This thesis concerns the optimisation of maintenance and inspection for stochastically deteriorating systems. The motivation for this thesis is the problem of determining condition based maintenance policies, for systems whose degradation may be modelled by a continuous time stochastic process. Our emphasis is mainly on using the information gained from inspecting the degradation to determine efficient maintenance and inspection policies. The system we shall consider is one in which the degradation is modelled by a Levy process, and in which failure is defined to occur when the degradation reaches a critical level. It is assumed that the system may be inspected or repaired at any time, and that the costs of inspections and repairs may depend on the level of system degradation. Initially we look at determining optimal inspection policies for systems whose degradation may be directly and perfectly observed, before extending this analysis to the case where the degradation is unobservable, and a related covariate process is used to determine maintenance decisions. In both cases it is assumed the replacement policy is fixed and known in advance. Finally we consider the case of joint optimisation of maintenance and inspection, for cases in which the maintenance action has either deterministic or random effect on the degradation level. In all of these cases we use the properties of the Levy process degradation model to form a recursive relationship which allows us to determine integral and functional equations for the maintenance cost of the system. Solutions to these determine optimal periodic and non-periodic inspection and maintenance policies. Throughout the thesis we use the gamma process degradation model as an example. For this model we determine optimal perfect inspection policies for the cases when inspections are periodic and non-periodic. As a special case of a covariate process we consider the optimal imperfect periodic inspection policy. Finally we obtain jointly optimal deterministic-maintenance and periodic-inspection policies

Optimization of maintenances following proof tests for the final element of a safety-instrumented system

2019 The Authors Safety-instrumented systems (SISs) have been widely installed to prevent accidental events and mitigate their consequences. Mechanical final elements of SISs often become vulnerable with time due to degradations, but the particulars in SIS operations and assessment impede the adaption of state-of-art research results on maintenances into this domain. This paper models the degradation of SIS final element as a stochastic process. Based on the observed information during a proof test, it is essential to determine an optimal maintenance strategy by choosing a preventive maintenance (PM) or corrective maintenance (CM), as well deciding what degree of mitigation of degradation is enough in case of a PM. When the reasonable initiation situation of a PM and the optimal maintenance degree are identified, lifetime cost of the final element can be minimized while keeping satisfying the integrity level requirement for the SIS. A numerical example is introduced to illustrate how the presenting methods are used to examine the effects of maintenance strategies on cost and the average probability of failure on demands (PFDavg) of a SIS. Intervals of the upcoming tests thus can be updated to provide maintenance crews with more clues on cost-effective tests without weakening safety

Condition-based maintenance—an extensive literature review

This paper presents an extensive literature review on the field of condition-based maintenance (CBM). The paper encompasses over 4000 contributions, analysed through bibliometric indicators and meta-analysis techniques. The review adopts Factor Analysis as a dimensionality reduction, concerning the metric of the co-citations of the papers. Four main research areas have been identified, able to delineate the research field synthetically, from theoretical foundations of CBM; (i) towards more specific implementation strategies (ii) and then specifically focusing on operational aspects related to (iii) inspection and replacement and (iv) prognosis. The data-driven bibliometric results have been combined with an interpretative research to extract both core and detailed concepts related to CBM. This combined analysis allows a critical reflection on the field and the extraction of potential future research directions

Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review

This is the final version. Available on open access from MDPI via the DOI in this record. Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and prognosis techniques. Future scenarios in robotics, artificial intelligence and data processing are investigated. The application challenges of these strategies and Industry 4.0 concepts in the offshore renewables sector are scrutinised, together with the potential implications of early-stage project integration. The identified technologies are ranked against a series of indicators, providing a reference for a range of industry stakeholders.Engineering and Physical Sciences Research Council (EPSRC)European Union Horizon 202

Optimising airline maintenance scheduling decisions

Airline maintenance scheduling (AMS) studies how plans or schedules are constructed to ensure that a fleet is efficiently maintained and that airline operational demands are met. Additionally, such schedules must take into consideration the different regulations airlines are subject to, while minimising maintenance costs. In this thesis, we study different formulations, solution methods, and modelling considerations, for the AMS and related problems to propose two main contributions. First, we present a new type of multi-objective mixed integer linear programming formulation which challenges traditional time discretisation. Employing the concept of time intervals, we efficiently model the airline maintenance scheduling problem with tail assignment considerations. With a focus on workshop resource allocation and individual aircraft flight operations, and the use of a custom iterative algorithm, we solve large and long-term real-world instances (16000 flights, 529 aircraft, 8 maintenance workshops) in reasonable computational time. Moreover, we provide evidence to suggest, that our framework provides near-optimal solutions, and that inter-airline cooperation is beneficial for workshops. Second, we propose a new hybrid solution procedure to solve the aircraft recovery problem. Here, we study how to re-schedule flights and re-assign aircraft to these, to resume airline operations after an unforeseen disruption. We do so while taking operational restrictions into account. Specifically, restrictions on aircraft, maintenance, crew duty, and passenger delay are accounted for. The flexibility of the approach allows for further operational restrictions to be easily introduced. The hybrid solution procedure involves the combination of column generation with learning-based hyperheuristics. The latter, adaptively selects exact or metaheuristic algorithms to generate columns. The five different algorithms implemented, two of which we developed, were collected and released as a Python package (Torres Sanchez, 2020). Findings suggest that the framework produces fast and insightful recovery solutions

Study of dynamic workload assignment strategies on production performance

As maintenance has grown to be seen as a prospective tool for production value generation and business performance improvement, it can no longer be considered as isolated from other production activities. Studies have shown that the degradation process of machines is dependent on the operation being performed (e.g., higher workload results in faster degradation). However, the decision-making in maintenance planning with dynamic operation/workload adjustment considerations has not been addressed until recently. Moreover, the existing approaches attempting to tackle this problem have overlooked the fact that dynamics exist in both external production environment and internal production conditions and thus prove to be inefficient to react to unexpected situations arising. This paper has explored the impacts of different workload adjustment strategies on system production performance by a numerical study using agent-based simulation. A detailed discussion is given on the implication of the simulation outcome, based on which some insights into potential future work are also presented.EU H2020 Acknowledgments to financial support of Cambridge Trust and China Scholarship Counci