Predictive maintenance with industrial sensor data

The Norwegian Ministry of Petroleum and Energy Commissions report shows that the government is making a large step closer to its ambition of allocating regions for 30,000 MW offshore wind via way of means of 2040. According to a report by IRENA, offshore wind operation and maintenance (O & M) costs make up a significant portion of the overall cost of electricity for offshore wind farms in G20 countries, ranging from 16-25%. To address this issue, it is essential to explore methods for improving operational reliability and reducing the maintenance costs of wind turbines. One promising approach is predictive maintenance, which involves leveraging data collected from sensors already equipped with the turbines to detect and address potential issues before they become more serious. Predictive maintenance is important in wind farms to reduce downtime and optimize the performance of wind turbines. Various rotating components in wind turbines make them complicated machinery, and if any of those parts fails, it can cause the entire turbine to shut down. This can result in lost revenue for the wind farm operator and lead to higher maintenance costs if the problem is not addressed quickly. This can be possible through a Supervisory Control and Data Acquisition (SCADA) system, which collects and analyzes data from various turbine components. We have developed a method for detecting and monitoring failures in critical components such as the gearbox and generator, based on historical SCADA data. Our approach utilizes machine learning models, specifically extreme gradient boosting (XGBoost), and has been tested on two real-world case studies involving eight different turbines. The outcomes show both the effectiveness and usefulness of our technique for boosting wind turbine reliability and minimizing maintenance costs

Reliability Models and Failure Detection Algorithms for Wind Turbines

Durante las pasadas décadas, la industria eólica ha sufrido un crecimiento muysignificativo en Europa llevando a la generación eólica al puesto más relevanteen cuanto a producción energética mediante fuentes renovables. Sin embargo, siconsideramos los aspectos económicos, el sector eólico todavía no ha alcanzadoel nivel competitivo necesario para batir a los sistemas de generación de energíaconvencionales.Los costes principales en la explotación de parques eólicos se asignan a lasactividades relacionadas con la Operación y Mantenimiento (O&M). Esto se debeal hecho de que, en la actualidad, la Operación y Mantenimiento está basadaprincipalmente en acciones correctivas o preventivas. Por tanto, el uso de técnicaspredictivas podría reducir de forma significativa los costes relacionados con lasactividades de mantenimiento mejorando así los beneficios globales de la explotaciónde los parques eólicos.Aunque los beneficios del mantenimiento predictivo se consideran cada díamás importantes, existen todavía la necesidad de investigar y explorar dichastécnicas. Modelos de fiabilidad avanzados y algoritmos de predicción de fallospueden facilitar a los operadores la detección anticipada de fallos de componentesen los aerogeneradores y, en base a ello, adaptar sus estrategias de mantenimiento.Hasta la fecha, los modelos de fiabilidad de turbinas eólicas se basan, casiexclusivamente, en la edad de la turbina. Esto es así porque fueron desarrolladosoriginalmente para máquinas que trabajan en entornos ‘amigables’, por ejemplo, enel interior de naves industriales. Los aerogeneradores, al contrario, están expuestosa condiciones ambientales altamente variables y, por tanto, los modelos clásicosde fiabilidad no reflejan la realidad con suficiente precisión. Es necesario, portanto, desarrollar nuevos modelos de fiabilidad que sean capaces de reproducir el comportamiento de los fallos de las turbinas eólicas y sus componentes, teniendoen cuenta las condiciones meteorológicas y operacionales en su emplazamiento.La predicción de fallos se realiza habitualmente utilizando datos que se obtienendel sistema de Supervisión Control y Adquisición de Datos (SCADA) o de Sistemasde Monitorización de Condición (CMS). Cabe destacar que en turbinas eólicasmodernas conviven ambos tipos de sistemas y la fusión de ambas fuentes de datospuede mejorar significativamente la detección de fallos. Esta tesis pretende mejorarlas prácticas actuales de Operación y Mantenimiento mediante: (1) el desarrollo demodelos avanzados de fiabilidad y detección de fallos basados en datos que incluyanlas condiciones ambientales y operacionales existentes en los parques eólicos y (2)la aplicación de nuevos algoritmos de detección de fallos que usen las condicionesambientales y operacionales del emplazamiento, así como datos procedentes tantode sistemas SCADA como CMS. Estos dos objetivos se han dividido en cuatrotareas.En la primera tarea se ha realizado un análisis exhaustivo tanto de los fallosproducidos en un amplio conjunto de aerogeneradores (amplio en número de turbinasy en longitud de los registros) como de sus tiempos de parada asociados. De estaforma, se han visualizado los componentes que más fallan en función de la tecnologíadel aerogenerador, así como sus modos de fallo. Esta información es vital para eldesarrollo posterior de modelos de fiabilidad y mantenimiento.En segundo lugar, se han investigado las condiciones meteorológicas previasa sucesos con fallos de los principales componentes de los aerogeneradores. Seha desarrollado un entorno de aprendizaje basado en datos utilizando técnicas deagrupamiento ‘k-means clustering’ y reglas de asociación ‘a priori’. Este entorno escapaz de manejar grandes cantidades de datos proporcionando resultados útiles yfácilmente visualizables. Adicionalmente, se han aplicado algoritmos de detecciónde anomalías y patrones para encontrar cambios abruptos y patrones recurrentesen la serie temporal de la velocidad del viento en momentos previos a los fallosde los componentes principales de los aerogeneradores. En la tercera tarea, sepropone un nuevo modelo de fiabilidad que incorpora directamente las condicionesmeteorológicas registradas durante los dos meses previos al fallo. El modelo usados procesos estadísticos separados, uno genera los sucesos de fallos, así comoceros ocasionales mientras que el otro genera los ceros estructurales necesarios paralos algoritmos de cálculo. Los posibles efectos no observados (heterogeneidad) en el parque eólico se tienen en cuenta de forma adicional. Para evitar problemas de‘over-fitting’ y multicolinearidades, se utilizan sofisticadas técnicas de regularización.Finalmente, la capacidad del modelo se verifica usando datos históricos de fallosy lecturas meteorológicas obtenidas en los mástiles meteorológicos de los parqueseólicos.En la última tarea se han desarrollado algoritmos de predicción basados encondiciones meteorológicas y en datos operacionales y de vibraciones. Se ha‘entrenado’ una red de Bayes, para predecir los fallos de componentes en unparque eólico, basada fundamentalmente en las condiciones meteorológicas delemplazamiento. Posteriormente, se introduce una metodología para fusionar datosde vibraciones obtenidos del CMS con datos obtenidos del sistema SCADA, conel objetivo de analizar las relaciones entre ambas fuentes. Estos datos se hanutilizado para la predicción de fallos en el eje principal utilizando varios algoritmosde inteligencia artificial, ‘random forests’, ‘gradient boosting machines’, modelosgeneralizados lineales y redes neuronales artificiales. Además, se ha desarrolladouna herramienta para la evaluación on-line de los datos de vibraciones (CMS)denominada DAVE (‘Distance Based Automated Vibration Evaluation’).Los resultados de esta tesis demuestran que el comportamiento de los fallos delos componentes de aerogeneradores está altamente influenciado por las condicionesmeteorológicas del emplazamiento. El entorno de aprendizaje basado en datos escapaz de identificar las condiciones generales y temporales específicas previas alos fallos de componentes. Además, se ha demostrado que, con los modelos defiabilidad y algoritmos de detección propuestos, la Operación y Mantenimiento delas turbinas eólicas puede mejorarse significativamente. Estos modelos de fiabilidady de detección de fallos son los primeros que proporcionan una representaciónrealística y específica del emplazamiento, al considerar combinaciones complejasde las condiciones ambientales, así como indicadores operacionales y de estadode operación obtenidos a partir de la fusión de datos de vibraciones CMS y datosdel SCADA. Por tanto, este trabajo proporciona entornos prácticos, modelos yalgoritmos que se podrán aplicar en el campo del mantenimiento predictivo deturbinas eólicas.<br /

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

Use of advanced analytics for health estimation and failure prediction in wind turbines

Tesi en modalitat de tesi per compendiThe energy sector has undergone drastic changes and critical revolutions in the last few decades. Renewable energy sources have grown significantly, now representing a sizeable share of the energy production mix. Wind energy has seen increasing rate of adoptions, being one of the more convenient and sustainable mean of producing energy. Research and innovation have helped greatly in driving down production and operation costs of wind energy, yet important challenges still remain open. This thesis addresses predictive maintenance and monitoring of wind turbines, aiming to present predictive frameworks designed with the necessities of the industry in mind. More concretely: interpretability, scalability, modularity and reliability of the predictions are the objectives —together with limited data requirements— of this project. Of all the available data at the disposal of wind turbine operators, SCADA is the principal source of information utilized in this research, due to its wide availability and low cost. Ensemble models played an important role in the development of the presented predictive frameworks thanks to their modular nature which allows to combine very diverse algorithms and data types. Important insights gained from these experiments are the beneficial effect of combining multiple and diverse sources of data —for example SCADA and alarms logs—, the easiness of combining different algorithms and indicators, and the noticeable gain in predicting performance that it can provide. Finally, given the central role that SCADA data plays in this thesis, but also in the wind energy industry, a detailed analysis of the limitations and shortcomings of SCADA data is presented. In particular, the ef- fect of data aggregation —a common practice in the wind industry— is determined developing a methodological framework that has been used to study high–frequency SCADA data. This lead to the conclusion that typical aggregation periods, i.e. 5–10 minutes that are the standard in wind energy industry are not able to capture and maintain the information content of fast–changing signals, such as wind and electrical measurements.El sector energètic ha experimentat importants canvis i revolucions en les últimes dècades. Les fonts d’energia renovables han crescut significativament, i ara representen una part important en el conjunt de generació. L’energia eòlica ha augmentat significativament, convertint-se en una de les millors alternatives per produir energia verda. La recerca i la innovació ha ajudat a reduir considerablement els costos de producció i operació de l’energia eòlica, però encara hi ha oberts reptes importants. Aquesta tesi aborda el manteniment predictiu i el seguiment d’aerogeneradors, amb l’objectiu de presentar solucions d’algoritmes de predicció dissenyats tenint en compte les necessitats de la indústria. Més concretament conceptes com, la interpretabilitat, escalabilitat, modularitat i fiabilitat de les prediccions ho són els objectius, juntament amb els requisits limitats per les de dades disponibles d’aquest projecte. De totes les dades disponibles a disposició dels operadors d’aerogeneradors, les dades del sistema SCADA són la principal font d’informació utilitzada en aquest projecte, per la seva àmplia disponibilitat i baix cost. En el present treball, els models de conjunt tenen un paper important en el desenvolupament dels marcs predictius presentats gràcies al seu caràcter modular que permet l’ús d’algoritmes i tipus de dades molt diversos. Resultats importants obtinguts d’aquests experiments són l’efecte beneficiós de combinar múltiples i diverses fonts de dades, per exemple, SCADA i dades d’alarmes, la facilitat de combinar diferents algorismes i indicadors i el notable guany en predir el rendiment que es pot oferir. Finalment, donat el paper central que SCADA l’anàlisi de dades juga en aquesta tesi, però també en la indústria de l’energia eòlica, una anàlisi detallada de la es presenten les limitacions i les mancances de les dades SCADA. En particular es va estudiar l’efecte de l’agregació de dades -una pràctica habitual en la indústria eòlica-. Dins d’aquest treball es proposa un marc metodològic que s’ha utilitzat per estudiar dades SCADA d’alta freqüència. Això va portar a la conclusió que els períodes d’agregació típics, de 5 a 10 minuts que són l’estàndard a la indústria de l’energia eòlica, no són capaços de capturar i mantenir el contingut d’informació de senyals que canvien ràpidament, com ara mesures eòliques i elèctriquesPostprint (published version

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

Improved wind turbine monitoring using operational data

With wind energy becoming a major source of energy, there is a pressing need to reduce all associated costs to be competitive in a market that might be fully subsidy-free in the near future. Before thousands of wind turbines were installed all over the world, research in e.g. understanding aerodynamics, developing new materials, designing better gearboxes, improving power electronics etc., helped to cut down wind turbine manufacturing costs. It might be assumed, that this would be sufficient to reduce the costs of wind energy as the resource, the wind itself, is free of costs. However, it has become clear that the operation and maintenance of wind turbines contributes significantly to the overall cost of energy. Harsh environmental conditions and the frequently remote locations of the turbines makes maintenance of wind turbines challenging. Just recently, the industry realised that a move from reactive and scheduled maintenance towards preventative or condition-based maintenance will be crucial to further reduce costs. Knowing the condition of the wind turbine is key for any optimisation of operation and maintenance. There are various possibilities to install advanced sensors and monitoring systems developed in recent years. However, these will inevitably incur new costs that need to be worthwhile and retro-fits to existing turbines might not always be feasible. In contrast, this work focuses on ways to use operational data as recorded by the turbine's Supervisory Control And Data Acquisition (SCADA) system, which is installed in all modern wind turbines for operating purposes -- without additional costs. SCADA data usually contain information about the environmental conditions (e.g. wind speed, ambient temperature), the operation of the turbine (power production, rotational speed, pitch angle) and potentially the system's health status (temperatures, vibration). These measurements are commonly recorded in ten-minutely averages and might be seen as indirect and top-level information about the turbine's condition. Firstly, this thesis discusses the use of operational data to monitor the power performance to assess the overall efficiency of wind turbines and to analyse and optimise maintenance. In a sensitivity study, the financial consequences of imperfect maintenance are evaluated based on case study data and compared with environmental effects such as blade icing. It is shown how decision-making of wind farm operators could be supported with detailed `what-if' scenario analyses. Secondly, model-based monitoring of SCADA temperatures is investigated. This approach tries to identify hidden changes in the load-dependent fluctuations of drivetrain temperatures that can potentially reveal increased degradation and possible imminent failure. A detailed comparison of machine learning regression techniques and model configurations is conducted based on data from four wind farms with varying properties. The results indicate that the detailed setup of the model is very important while the selection of the modelling technique might be less relevant than expected. Ways to establish reliable failure detection are discussed and a condition index is developed based on an ensemble of different models and anomaly measures. However, the findings also highlight that better documentation of maintenance is required to further improve data-driven condition monitoring approaches. In the next part, the capabilities of operational data are explored in a study with data from both the SCADA system and a Condition Monitoring System (CMS) based on drivetrain vibrations. Analyses of signal similarity and data clusters reveal signal relationships and potential for synergistic effects of the different data sources. An application of machine learning techniques demonstrates that the alarms of the commercial CMS can be predicted in certain cases with SCADA data alone. Finally, the benefits of having wind turbines in farms are investigated in the context of condition monitoring. Several approaches are developed to improve failure detection based on operational statistics, CMS vibrations or SCADA temperatures. It is demonstrated that utilising comparisons with neighbouring turbines might be beneficial to get earlier and more reliable warnings of imminent failures. This work has been part of the Advanced Wind Energy Systems Operation and Maintenance Expertise (AWESOME) project, a European consortium with companies, universities and research centres in the wind energy sector from Spain, Italy, Germany, Denmark, Norway and UK. Parts of this work were developed in collaboration with other fellows in the project (as marked and explained in footnotes)

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Anomaly-based fault detection in wind turbine main bearings

Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used sources of energy, and therefore the need for predictive maintenance management to guarantee the reliability and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks.</p