Maintenance is essential in keeping wind energy assets operating efficiently. With the development of advanced condition monitoring, diagnostics and prognostics, condition‐based maintenance has ...attracted much attention in the offshore wind industry in recent years. This paper models various maintenance activities and their impacts on the degradation and performance of offshore wind turbine components. An integrated maintenance strategy of corrective maintenance, imperfect time‐based preventive maintenance and condition‐based maintenance is proposed and compared with other traditional maintenance strategies. A maintenance simulation programme is developed to simulate the degradation and maintenance of offshore wind turbines and estimate their performance. A case study on a 10‐MW offshore wind turbine (OWT) is presented to analyse the performance of different maintenance strategies. The simulation results reveal that the proposed strategy not only reduces the total maintenance cost but also improves the energy generation by reducing the total downtime and expected energy not supplied. Furthermore, the proposed maintenance strategy is optimised to find the best degradation threshold and balance the trade‐off between the use of condition‐based maintenance and other maintenance activities.
•Three different maintenance cost scenarios are analyzed for different maintenance policies.•Considering different cost, components are optimally chosen for preventive maintenance.•The expected costs ...due to a component and the system are investigated.•Joint loss importance is proposed to determine the expected cost in the system.•A case of the hydraulic system of the aircraft is used to illustrate the proposed method.
System safety assessment is a technique aiming at identifying hazards for the system under analysis and showing the compliance with the safety requirements. In order to increase the safety of a system, one may select components in the system for preventive maintenance, under the constraints of maintenance cost, maintenance time and the availability of maintenance staff. In different maintenance policies, maintenance cost can differ. This paper proposes some measures for component preventive maintenance considering maintenance effectiveness, based on which the expected costs due to a component and the system are investigated, respectively. Three different maintenance cost scenarios are analyzed for different maintenance policies. Considering both cost and maintenance constraints, components are optimally chosen for preventive maintenance. An application of a hydraulic system for an aircraft is then used to illustrate the proposed method.
PurposeThis paper aims to prioritize the factors for the successful implementation of total productive maintenance (TPM).Design/methodology/approachThe technique used for prioritization is the ...analytical hierarchy process (AHP).FindingsThe commitment and involvement of the top management, i.e. the leadership team, is the most critical success factor in the successful implementation of TPM. Employee training is another vital factor. Top management should also encourage a culture favorable for information flow, equipment ownership, the involvement of people and quality management throughout the organization.Research limitations/implicationsManufacturing organizations interested in improving productivity through the implementation of TPM should first involve the leadership team and seek their full support and train all the employees in this philosophy. However, the findings cannot be generalized for global application due to the inputs taken from experts in AHP from limited geography.Practical implicationsReducing production costs is a universal expectation of business leaders. TPM can be used as a long-term strategy to improve productivity by the organization.Social implicationsAll employees have to be trained in this philosophy, and as part of the training and the implementation of TPM, they feel empowered and committed to the organization.Originality/valueThis study has illustrated the use of AHP for the prioritization of success factors. Prioritization of success factors will help in strategy formulation by management for effective maintenance. It will help in improving the productivity and performance of the organization.
The use of prognostic methods in maintenance in order to predict remaining useful life is receiving more attention over the past years. The use of these techniques in maintenance decision making and ...optimization in multi-component systems is however a still underexplored area. The objective of this paper is to optimally plan maintenance for a multi-component system based on prognostic/predictive information while considering different component dependencies (i.e. economic, structural and stochastic dependence). Consequently, this paper presents a dynamic predictive maintenance policy for multi-component systems that minimizes the long-term mean maintenance cost per unit time. The proposed maintenance policy is a dynamic method as the maintenance schedule is updated when new information on the degradation and remaining useful life of components becomes available. The performance, regarding the objective of minimal long-term mean cost per unit time, of the developed dynamic predictive maintenance policy is compared to five other conventional maintenance policies, these are: block-based maintenance, age-based maintenance, age-based maintenance with grouping, inspection condition-based maintenance and continuous condition-based maintenance. The ability of the predictive maintenance policy to react to changing component deterioration and dependencies within a multi-component system is quantified and the results show significant cost savings.
In a semiconductor plasma etcher, it is becoming increasingly necessary to improve productivity by reducing unplanned equipment maintenance. Thus, predictive maintenance (PdM) is typically conducted ...using equipment data to predict the failure timing, after which proactive measures should be taken. In PdM, the planned maintenance schedule is updated on the basis of the predicted failure timing. However, in practice, the predicted failure timing has a probabilistic variability. Therefore, we propose a maintenance schedule update method based on the expected maintenance cost calculated from the probabilistic variability of the failure timing. We applied our method and conventional methods to a dataset of failure cases that model actual component failures of etchers and found that our method was effective in terms of reducing maintenance costs.
In order to provide a reliable service and supply the demand most of the time, all generators in a power grid should be subjected to an effective maintenance plan. The smarter the maintenance ...performed could result in a better performance of the system. However, a challenge is to minimise maintenance costs that do not compromise the benefits. Considering these facts, this study presents a reliability-based smart-maintenance approach of generators to compute the net-maximum economic benefit. The approach is derived from Kijima model type I to characterise the impact of maintenance over the component's virtual age, and Markov chains to model the component's lifetime. To achieve a more realistic model, generators' failure and repair rates are considered time-dependent variables. Then, the optimum preventive maintenance schedule is obtained by using an advanced algorithm named accelerated quantum particle swarm optimisation in combination with sequential Monte Carlo simulation. The effectiveness of the approach is investigated through a case study with four different scenarios: (i) no preventive maintenance plan, (ii) yearly periodic preventive maintenance, (iii) reliability-centred maintenance and (iv) smart maintenance. The results suggest that the approach is convenient for power system generators and delivers a significant knowledge contribution in the area of maintenance.
The increasing availability of condition monitoring data for aircraft components has incentivized the development of Remaining Useful Life (RUL) prognostics in the past years. However, only few ...studies consider the integration of such prognostics into maintenance planning. In this paper we propose a dynamic, predictive maintenance scheduling framework for a fleet of aircraft taking into account imperfect RUL prognostics. These prognostics are periodically updated. Based on the evolution of the prognostics over time, alarms are triggered. The scheduling of maintenance tasks is initiated only after these alarms are triggered. Alarms ensure that maintenance tasks are not rescheduled multiple times. A maintenance task is scheduled using a safety factor, to account for potential errors in the RUL prognostics and thus avoid component failures. We illustrate our approach for a fleet of 20 aircraft, each equipped with 2 turbofan engines. A Convolution Neural Network is proposed to obtain RUL prognostics. An integer linear program is used to schedule aircraft for maintenance. With our alarm-based maintenance framework, the costs with engine failures account for only 7.4% of the total maintenance costs. In general, we provide a roadmap to integrate imperfect RUL prognostics into the maintenance planning of a fleet of vehicles.
•Predictive maintenance of aircraft engines integrating imperfect RUL prognostics.•Obtaining RUL prognostics for turbofan engines using Convolutional Neural Networks.•Remaining Useful Life prognostics with C-MAPSS degradation data of turbofan engines.•Based on RUL prognostics, proposing an alarm policy to trigger maintenance tasks.•Analysis of costs for predictive maintenance with imperfect RUL prognostics.
Summary
This European guideline for the diagnosis and treatment of insomnia was developed by a task force of the European Sleep Research Society, with the aim of providing clinical recommendations ...for the management of adult patients with insomnia. The guideline is based on a systematic review of relevant meta‐analyses published till June 2016. The target audience for this guideline includes all clinicians involved in the management of insomnia, and the target patient population includes adults with chronic insomnia disorder. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) system was used to grade the evidence and guide recommendations. The diagnostic procedure for insomnia, and its co‐morbidities, should include a clinical interview consisting of a sleep history (sleep habits, sleep environment, work schedules, circadian factors), the use of sleep questionnaires and sleep diaries, questions about somatic and mental health, a physical examination and additional measures if indicated (i.e. blood tests, electrocardiogram, electroencephalogram; strong recommendation, moderate‐ to high‐quality evidence). Polysomnography can be used to evaluate other sleep disorders if suspected (i.e. periodic limb movement disorder, sleep‐related breathing disorders), in treatment‐resistant insomnia, for professional at‐risk populations and when substantial sleep state misperception is suspected (strong recommendation, high‐quality evidence). Cognitive behavioural therapy for insomnia is recommended as the first‐line treatment for chronic insomnia in adults of any age (strong recommendation, high‐quality evidence). A pharmacological intervention can be offered if cognitive behavioural therapy for insomnia is not sufficiently effective or not available. Benzodiazepines, benzodiazepine receptor agonists and some antidepressants are effective in the short‐term treatment of insomnia (≤4 weeks; weak recommendation, moderate‐quality evidence). Antihistamines, antipsychotics, melatonin and phytotherapeutics are not recommended for insomnia treatment (strong to weak recommendations, low‐ to very‐low‐quality evidence). Light therapy and exercise need to be further evaluated to judge their usefulness in the treatment of insomnia (weak recommendation, low‐quality evidence). Complementary and alternative treatments (e.g. homeopathy, acupuncture) are not recommended for insomnia treatment (weak recommendation, very‐low‐quality evidence).
Decision-making in highway preventive maintenance (PM) is generally costly and complicated. An inappropriate maintenance strategy could yield a low efficiency of budget usage and untreated road ...distress. This study describes an innovative predictive maintenance strategy that provides direct maintenance guidance to specific highway mileposts. This was achieved with the application of the artificial neural network (ANN) algorithm to mine a maintenance database. Ten-year distress measurement data at 100-m intervals, traffic load data, climatic history, and maintenance records of a chosen highway were regarded as the input data of the ANN model. A data quality control method was proposed to ensure asphalt pavement performance improvement continuity over time based on the idea of the maintenance year as the starting point for prediction. The backpropagation neural network (BPNN) model and a hybrid neural network (HNN) were applied to predict five indexes of the highway asphalt pavement performance, and the genetic algorithm (GA) was employed to optimize the hyperparameters of these models. The results indicate that the GA enhanced HNN model could increase the accuracy by 35% on average compared with traditional ANN in predicting the highway asphalt distress performance. Furthermore, a notable agreement is attained when comparing the predicted indexes to the whole-year measurement data invalidation with average coefficient of determination (R2) reaches 0.74. This study demonstrates the potential of an innovative ANN method in highway distress prediction to provide direct guidance for long-term highway asphalt pavement optimal rehabilitation and maintenance (R&M) decisions.
•An innovative predictive maintenance strategy for specific highway mileposts is proposed.•Ten-year distress measurement data of a chosen highway were regarded as the input data of the ANN model.•Genetic algorithm (GA) was employed to optimize the hyperparameters of the models.•Provide direct guidance for long-term highway asphalt pavement optimal rehabilitation and maintenance (R&M) decisions.
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