A Fuzzy-FMEA Risk Assessment Approach for Offshore Wind Turbines

Failure Mode and Effects Analysis (FMEA) has been extensively used by wind turbine assembly manufacturers for risk and reliability analysis. However, several limitations are associated with its implementation in offshore wind farms: (i) the failure data gathered from SCADA system is often missing or unreliable, and hence, the assessment information of the three risk factors (i.e., severity, occurrence, and fault detection) are mainly based on experts’ knowledge; (ii) it is rather difficult for experts to precisely evaluate the risk factors; (iii) the relative importance among the risk factors is not taken into consideration, and hence, the results may not necessarily represent the true risk priorities; and etc. To overcome these drawbacks and improve the effectiveness of the traditional FMEA, we develop a fuzzy-FMEA approach for risk and failure mode analysis in offshore wind turbine systems. The information obtained from the experts is expressed using fuzzy linguistics terms, and a grey theory analysis is proposed to incorporate the relative importance of the risk factors into the determination of risk priority of failure modes. The proposed approach is applied to an offshore wind turbine system with sixteen mechanical, electrical and auxiliary assemblies, and the results are compared with the traditional FMEA

Fault detection of a wind turbine generator bearing using interpretable machine learning

A wind turbine is subjected to a number of degradation mechanisms during its operational lifetime. If left unattended, the degradation of components will result in poor performance and potential failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbines are regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally six-month intervals) and the maintenance program (list of tasks) is prepared using experience or risk-based reliability analysis, like risk-based inspection (RBI) and reliability-centered maintenance (RCM). This time-based maintenance program can be improved by incorporating results from condition monitoring (CM) involving data acquisition using sensors and fault detection using data analytics. It is important to ensure quality and quantity of data and to use correct procedures for data interpretation for fault detection to properly carry out condition assessment. This thesis contains the work carried out to develop a machine learning (ML) based methodology for detecting faults in a wind turbine generator bearing. The methodology includes application of ML using supervisory control and data acquisition (SCADA) data for predicting the operating temperature of a healthy bearing, and then comparing the predicted bearing temperature with the actual bearing temperature. Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing. This fault detection can then be used for rescheduling the maintenance tasks. The methodology is discussed in detail using a case study. In this thesis, interpretable ML tools are used to identify faults in a wind turbine generator bearing. Furthermore, variables affecting the generator bearing temperature are investigated. The analysis used two years of operational data from a 2 MW offshore wind turbine located in the Gulf of Guinea off the west coast of Africa. Out of the four ML models that were evaluated, the XGBoost model was determined to be the most effective performer. After utilizing the Shapley additive explanations (SHAP) to analyze the XGBoost model, it was determined that the temperature in the generator phase windings had the most significant effect on the model's predictions. Finally, based upon the deviation between the actual and the predicted temperatures, an anomaly in the generator bearing was successfully identified two months prior to a generator failure occurring.Masteroppgave i havteknologiHTEK3995MAMN-HTEKMAMN-HTE

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Health monitoring of renewable energy systems

The offshore wind energy industry has grown exponentially; globally, there is 12GW of installed capacity of offshore wind, of which over 95% has been installed in the past ten years. Access and maintenance in offshore wind farms can be difficult and considerably more expensive than onshore wind farms. Additionally, with low availability levels and greater downtime due to failures, there is a growing interest in the optimisation of operation and maintenance (O&M) activities to maximise profitability. Traditionally, maintenance activities on critical components and subsystems have deployed two maintenance approaches; time-based preventative or corrective. Time-based preventative or scheduled maintenance approaches are based on intervening at fixed intervals, determined in advance for each component. Scheduling is based on failure statistics such as mean time between failures (MTBF), mean time to repair (MTTR) or mean time to failure (MTTF). These come either from publicly available databases or operational measurements. As part of preventive maintenance activities, there are annual services of the turbine to replace and maintain any component or assembly based on manufacturers’ indications. On the other hand, the corrective maintenance approach involves operating equipment until it fails and then restoring it, repairing it, or replacing it. Due to conservative estimates regarding the probability of failure, preventive and corrective maintenance approaches have financial implications associated with them. In the preventive approach, components are frequently replaced before they reach the end of their working life. In contrast, corrective maintenance guarantees that the serviceable life of a component is maximised, but it is subjected to long downtime, which is expensive regarding energy generation loss. Additionally, failure of the component may cause consequential damage to other parts of the wind turbine system, resulting in even greater repair costs, downtime and loss of revenue. A comprehensive literature review has been undertaken in the areas of maintenance, turbine reliability, turbine failure modes and causes, physics of failure, condition monitoring techniques, and costs. The limitations and disadvantages of current operation and maintenance practices are identified, and new approaches combining the knowledge of the condition of components and historical data are proposed and compared to achieve optimal turbine availability and maintenance cost reduction. A Failure Modes and Effects Analysis (FMEA) was performed for the functional modes of each system, subsystem, assembly and component following the British standard BS EN 60812:2006. Currently, the most common offshore wind turbine uses three blades, a 3-stage gearbox, induction generator and a fully rated power converter. The Siemens 3.6MW -120 turbine is selected for this project as an example of this configuration. The main objectives of undertaking this comprehensive FMEA are to identify critical components and their failures with significant impact on the wind turbine operation in terms of maintainability, safety and availability. The assessment identified 500 components and almost 1000 failure causes. The most critical assemblies identified in terms of severity, occurrence and undetectability of the failure are; the frequency converter, pitch system, yaw system and gearbox. The implementation of a condition-based maintenance philosophy, including the development of real predictive approaches which estimate the remaining useful life of degrading critical components has been analysed by the recent literature. However, developing such capabilities for the critical assemblies identified is a significant technical challenge. This study aims to develop and demonstrate the implementation of a methodology and appropriate algorithms to optimise O&M of offshore wind farms, by estimating the remaining useful life of critical components with greater accuracy using a combination of physics-based models, statistical-based models and data mining approaches. A register of trends and likely the main causes of failures of the power converter, gearbox, yaw system and pitch system was generated through a thorough literature search and participation in conferences and workshops during the project. The main sources of failure of the power converter and gearbox have been represented by algorithms and physics-based models developed in Python and proprietary software, respectively. These algorithms comprise two phases: diagnosis or learning phase using historical data (such as SCADA or digital information recorded by condition monitoring systems) and prognosis phase using simulated data (using as a basis the wind turbine aero-elastic software FASTv8). The pitch system failure mechanisms were explored using a combination of data mining approaches and subject matter expert knowledge. Examples of approaches investigated and implemented include: Support Vector Machine (SVM) to define normal behaviour and K Nearest Neighbour (KNN) to classify new observations regarding operation state (green for normal operation, amber for abnormal operation, red for failure). New observations with amber or red colours need to be analysed further, to diagnose potential failure modes using a decision tree algorithm with more variables related to the pitch system. The goals of developing a well-defined strategy for maintenance interventions and optimised management of wind farm logistics are required to effectively improve wind farm availability while reducing the cost of operations. Additionally, a clear identification of uncertainties inherent in stochastic processes, necessary for estimating access, failure prognosis and failure probabilities is required for operators to make informed decisions. The final output of this work is an O&M cost model which analyses and compares a conventional O&M strategy using a combination of preventive and reactive maintenance against an O&M strategy using the approaches described above for failure prognosis and diagnosis. The analysis is performed for a fictitious offshore wind farm with one-year operational data. The results include availability, downtime, the cost of repair, loss of production, revenue losses and the hidden CO2 emissions of the maintenance activities taking into account a combined probability level to account for the uncertainties

Artificial Intelligence and Industry 4.0

Tato bakalářská práce se zabývá rešerší používanými metodami umělé inteligence v diagnostiky technických soustav. První část práce je věnována rešerši používaných metod v oblasti diagnostiky technických soustav. V druhé části je uvedeno shrnutí vlastností a použití těchto metod. Závěr práce se věnuje aplikaci metody umělé neuronové sítě a hybridní metody „adaptivní neuro fuzzy inferenční systém“, jejímž základem je neuronová síť. Tato část se zabývá podrobným popisem a použitím těchto metod v reálných technických soustavách.This bachelor thesis deals with search for artificial intelligence methods used in the diagnostics of technical systems. The first part of the work is devoted to the search of used methods in the field of diagnostics of technical systems. The second part summarizes the characteristics and applications of these methods. The conclusion of the thesis deals with the application of the artifical neural network method and the hybrid method „adaptive neuro-fuzzy inference systém“, which is based on the neural network. This part deals with a detailed description and use of these methods in real technical systems.

Optimisation of offshore wind farm maintenance.

The installed capacity of European Offshore Wind Turbines (OWT) is likely to rise from the 2014 value of 7GW to 150GW in 2030. However maintenance of OWT is facing unprecedented challenges and cost 35% of lifetime costs. This will be equivalent to £14billion/year by 2030 if current OWT maintenance schemes are not changed. However the complexities around OWT operation require tools and systems to optimise OWT maintenance. The design of optimal OWT maintenance requires failure analysis of over 10,000 components in OWT for which there is little published work relating to performance and failure. In this work, inspection reports of over 400 wind turbine gearboxes (source: Stork Technical Services) and SCADA data (source: Shetland Aerogenerators Ltd) were studied to identify issues with performance and failures in wind turbines. A modified framework of Failure Mode Effects and Criticality Analysis (i.e. FMECA+) was designed to analyse failures according to the unique requirements of OWT maintenance planners. The FMECA+ framework enables analysis and prediction of failures for varied root causes, and determines their consequences over short and long periods of time. A software tool has been developed around FMECA+ framework that enables prediction of component level failures for varied root causes. The tool currently stores over 800 such instances. The need to develop a FMECA+ based Enterprise Resource Planning tool has been identified and preliminary results obtained from its development have been shown. Such a software package will routinely manage OWT data, predict failures in components, manage resources and plan an optimal maintenance. This will solve some big problems that OWT maintenance planners currently face. This will also support the use of SCADA and condition monitoring data in planning OWT maintenance, something which has been difficult to manage for a long time

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Fault detection of a wind turbine generator bearing using interpretable machine learning

Introduction: During its operational lifetime, a wind turbine is subjected to a number of degradation mechanisms. If left unattended, the degradation of components will result in its suboptimal performance and eventual failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbine be regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally 6-month intervals) and the program (list of tasks) is prepared using experience or risk-reliability analysis, like Risk-based inspection (RBI) and Reliability-centered maintenance (RCM). This time-based maintenance program can be improved upon by incorporating results from condition monitoring involving data collection using sensors and fault detection using data analytics. In order to properly carry out condition assessment, it is important to assure quality & quantity of data and to use correct procedures for interpretation of data for fault detection. This paper discusses the work carried out to develop a machine learning based methodology for detecting faults in a wind turbine generator bearing. Explanation of the working of the machine learning model has also been discussed in detail.Methods: The methodology includes application of machine learning model using SCADA data for predicting operating temperature of a healthy bearing; and then comparing the predicted bearing temperature against the actual bearing temperature.Results: Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing.Discussion: This fault detection can then be used for rescheduling the maintenance tasks. The working of this methodology is discussed in detail using a case study

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen