Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

Asgarpour, M.; Sørensen, John Dalsgaard

doi:10.36001/ijphm.2018.v9i1.2696

Cited by 8 publications

(11 citation statements)

References 16 publications

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“…Asgarpour and Sørensen [111] use an exponential degradation model with a stochastic scale factor to predict RUL. Based on CMS data and using Bayes' rule, the parameters of the model are updated as new data are obtained.…”

Section: Failure Prognosismentioning

confidence: 99%

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

et al. 2022

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Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy.

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Section: Failure Prognosismentioning

confidence: 99%

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

et al. 2022

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“…With higher regard to environmental and operational parameters influencing maintenance processes, (Reder et al, 2018) are looking for approaches to model weather influences on wind turbine failures and (Verhagen and Boer, 2018) proposes timedependent proportional hazard models to identify operational factors influencing maintenance event occurrences. Also in the context of wind turbines, (Asgarpour and Sørensen, 2018) present a Bayesian approach for a prognostic model and degradation monitoring. The usage of Decision Trees and Neural Networks and a general review of various PHM algorithms are discussed by (Carvalho et al, 2019) and (Accorsi et al, 2017).…”

Section: Problem Descriptionmentioning

confidence: 99%

Enhancing the diagnostic performance of Condition Based Maintenance through the fusion of sensor with maintenance data

Simon¹,

Schoenhof²

2021

PHM_CONF

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Although Maintenance data is crucial for authoritative reporting reasons and is generally used to optimizemaintenance planning in terms of budget, scheduling and logistics, the potentials of the implicit given informationfor Prognostics and Health Management (PHM) frameworks are not yet completely leveraged. Traditional PHMframeworks typically rely only on sensor data to derive a system’s health status, while maintenance, repair andoverhaul (MRO) data is not investigated. However, maintenance data contains valuable information on which partof a system is checked, serviced or replaced. At the same time, maintenance data is necessary for the labelling ofsensor data, the differentiation of multiple failure modes and includes the expert knowledge of the worker. Theoverall goal of the presented work is enable a model update through the integration of this information into atraditional (sensor-based) PHM/condition monitoring framework.In this context, the underlying data bases and structures will be analyzed and a generalized methodology isproposed to include maintenance data directly into the forward-modelling phase of a PHM/condition monitoringframework. The main goal is not only to use the labels derived from maintenance data for evaluation purposes(which is a common practice in PHM research), but to use this data to build a memory of the maintenance andhealth state history and thereby enhance the diagnostic capabilities of the framework. Methods from the field ofProbabilistic Programming and Bayesian Statistics seem promising and are implemented in order to incorporatefor uncertainties and to enable a confidence level for the diagnosis. The proposed concept is developed, tested andassessed in a simulation environment, allowing to investigate the influence of data confidence and label uncertaintyon the results. Furthermore, this allows to derive specific requirements for the input data and hence for the dataacquisition in the real world. The proposed concept is described in a generic way to be applicable on differentengineering domains (e.g. wind turbine or production machinery industry), but it will be tested and evaluated on areal world aviation use case. This concluding use case is defined in the context of the project INDI at TU Darmstadt(Intelligent Data Utilization in Maintenance) in cooperation with the project partner Lufthansa Technik AG.

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“…With the growing use of condition monitoring and predictive maintenance on wind turbines, there has been an increasing interest in the use of BNs for fault detection and diagnosis (see Asgarpour and Sørensen, 2018a). BNs are useful in detection and diagnosis of faults in different wind turbine components.…”

Section: Fault Diagnosis and Prognosismentioning

confidence: 99%

Bayesian Network Modelling for the Wind Energy Industry: An Overview

Adedipe¹,

Shafiee

Zio³

2020

Reliability Engineering & System Safety

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Wind energy farms are moving into deeper and more remote waters to benefit from availability of more space for the installation of wind turbines as well as higher wind speed for the production of electricity. Wind farm asset managers must ensure availability of adequate power supply as well as reliability of wind turbines throughout their lifetime. The operating conditions in deep water environments often change very rapidly and, therefore the decision metrics used in different phases of a wind energy project's lifecycle will have to be updated on a very frequent basis, to guarantee higher wind energy system reliability levels. For this reason, there is a crucial need for the wind energy industry to develop advanced computational tools/techniques that are capable of modelling the possible scenarios in (near) real-time and provide a prompt response to any changes in operational/environmental conditions. Bayesian network (BN) is a popular machine learning (ML) method used for system modelling and decision-making under uncertainty. This paper provides a systematic review and evaluation of existing research on the use of BN models in the wind energy sector. To conduct this literature review, all relevant databases from inception to date were searched, and a total of 70 sources (including journal publications, conference proceedings, PhD dissertations, industry reports, best practice documents and software user guides) which met the inclusion criteria were identified, excluding references used in other sections of the text for discussion. Our review findings reveal that the applications of BNs in the wind energy industry are quite diverse, ranging from wind power and weather forecasting to risk management, fault diagnosis and prognosis, structural analysis, reliability assessment, and maintenance planning and updating. Furthermore, a number of case studies are presented to illustrate the applicability of BNs in practice. Although the paper details information applicable to the wind energy industry, knowledge can be transferred to many other sectors.

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Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

Cited by 8 publications

References 16 publications

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Enhancing the diagnostic performance of Condition Based Maintenance through the fusion of sensor with maintenance data

Bayesian Network Modelling for the Wind Energy Industry: An Overview

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