Load and risk based maintenance management of wind turbines

<p> Wind power has proven to be an important source of renewable energy in the modern electric power systems. Low profit margins due to falling electricity prices and high maintenance costs, over the past few years, have led to a focus on research in the area of maintenance management of wind turbines. The main aim of maintenance management is to find the optimal balance between Preventive Maintenance (PM) and Corrective Maintenance (CM), such that the overall life cycle cost of the asset is minimized. This thesis proposes a maintenance management framework called Self Evolving Maintenance Scheduler (SEMS), which provides guidelines for improving reliability and optimizing maintenance of wind turbines, by focusing on critical components. <p> The thesis introduces an Artificial Intelligence (AI) based condition monitoring method, which uses Artificial Neural Network (ANN) models together with Supervisory Control And Data Acquisition (SCADA) data for the early detection of failures in wind turbine components. The procedure for creating robust and reliable ANN models for condition monitoring applications is presented. The ANN based Condition Monitoring System (CMS) procedure focuses on issues like the selection of configuration of ANN models, the filtering of SCADA data for the selection of correct data set for ANN model training, and an approach to overcome the issue of randomness in the training of ANN models. Furthermore, an anomaly detection approach, which ensures an accuracy of 99% in the anomaly detection process is presented. The ANN based condition monitoring method is validated through case studies using real data from wind turbines of different types and ratings. The results from the case studies indicate that the ANN based CMS method can detect a failure in the wind turbine gearbox components as early as three months before the replacement of the damaged component is required. An early information about an impending failure can then be utilized for optimizing the maintenance schedule in order to avoid expensive unscheduled corrective maintenance. <p> The final part of the thesis presents a mathematical optimization model, called the Preventive Maintenance Scheduling Problem with Interval Costs (PMSPIC), for optimal maintenance decision making. The PMSPIC model provides an Age Based Preventive Maintenance (ABPM) schedule, which gives an initial estimate of the number of replacements, and an optimal ABPM schedule for the critical components during the life of the wind turbine, based on the failure rate models created using the historical failure times. Modifications in the PMSPIC model are presented, which enable an update of the maintenance decisions following an indication of deterioration from the CMS, providing a Condition Based Preventive Maintenance (CBPM) schedule. A hypothetical but realistic case study utilizing the Proportional Hazards Model (PHM) and output from the ANN based CMS method, is presented. The results from the case study demonstrate the possibility of updating the maintenance decisions in continuous time considering the changing conditions of the damaged components. Unlike the previously published mathematical models for maintenance optimization, the PMSPIC based scheduler provides an optimal decision considering the effect of an early replacement of the damaged component on the entire lives of all the critical components in the wind turbine system

PRENATAL DIAGNOSIS FOR SPINAL MUSCULAR ATROPHY

Amaç: Spinal musküler atrofi (SMA), spinal kord ön boynuz hücrelerinin dejenerasyonu, proksimal kaslarda ilerleyici güçsüzlük ve atrofi ile karakterize olup, en sık rastlanan kalıtsal alt motor nöron hastalığıdır. Hastalığın günümüz şartlarında tedavi edilemez olması, prenatal veya preimplantasyon genetik tanının önemine işaret etmekte olup bu amaçla yapılan çalışmalar hastalığın önlenmesinde önemlidir. Gereç ve yöntem: Çalışmada, ailesinde SMA tanısı almış 12 çiftin amniyosentez veya koriyon villus örnekleri SMN1 geninde bulunan delesyonlar açısından Polimeraz Zincir Reaksiyonu-Restriksiyon Fragment Uzunluk Polimorfizm (PCR-RFLP) yöntemleri kullanılarak analiz edilmiştir. Bulgular: Olguların üçünde (%25) exon 7 ve 8 de delesyon saptanmıştır. Sonuç: Elde edilen veriler literatürdeki sonuçlar ile karşılaştırılmış ve SMA hastalığı için prenatal tanının önemi tartışılmıştır. SUMMARY Objective: Spinal muscular atrophy (SMA) is the most common heritable lower motor neuron disease, characterised by degeneration of anterior horn cells of spinal cord and progressive weakness and atrophy in the proximal muscles. Because of Spinal Muscular Atrophy is known as incurable, prenatally and preimplatation genetic diagnosis are considered as the best approaches for this disease. Material and method: In this study, amniosythesis or chorion villus samples obtained during the prenatal diagnosis from 12 subjects with a history of SMA, were analysed for deletion of exon 7 and 8 located in SMN1 gene by Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). Results: Three of 12 subjects had exon 7 and 8 deletions (%25). Conclusion: Results were compared with the literature and the importance of prenatally diagnosis for SMA was discussed

Machine learning-based investigation of the cancer protein secretory pathway

Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets

A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines

Smart manufacturing is reshaping the manufacturing industry by boosting the integration of information and communication technologies and manufacturing process. As a result, manufacturing companies generate large volumes of machine data which can be potentially used to make data-driven operational decisions using informative computerized algorithms. In the manufacturing domain, it is well-known that the productivity of a production line is constrained by throughput bottlenecks. The operational dynamics of the production system causes the bottlenecks to shift among the production resources between the production runs. Therefore, prediction of the throughput bottlenecks of future production runs allows the production and maintenance engineers to proactively plan for resources to effectively manage the bottlenecks and achieve higher throughput. This paper proposes an active period based data-driven algorithm to predict throughput bottlenecks in the production system for the future production run from the large sets of machine data. To facilitate the prediction, we employ an auto-regressive integrated moving average (ARIMA) method to predict the active periods of the machine. The novelty of the work is the integration of ARIMA methodology with the data-driven active period technique to develop a bottleneck prediction algorithm. The proposed prediction algorithm is tested on real-world production data from an automotive production line. The bottleneck prediction algorithm is evaluated by treating it as a binary classifier problem and adapted the appropriate evaluation metrics. Furthermore, an attempt is made to determine the amount of past data needed for better forecasting the active periods

Data-driven algorithm for throughput bottleneck analysis of production systems

The digital transformation of manufacturing industries is expected to yield increased productivity. Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A\ua0challenge for these companies is identifying throughput bottlenecks using the real-time machine data they collect. This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected. The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES datasets were used to develop and test the algorithm, producing research outcomes useful to\ua0manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences

Recommended practices for wind farm data collection and reliability assessment for O&amp;M optimization

The paper provides a brief overview of the aims and main results of IEA Wind Task 33. IEA Wind Task 33 was an expert working group with a focus on data collection and reliability assessment for O &amp; M optimization of wind turbines. The working group started in 2012 and finalized the work in 2016. The complete results of IEA Wind Task 33 are described in the expert group report on recommended practices for "Wind farm data collection and reliability assessment for O &amp; M optimization" which will be published by IEA Wind in 2017. This paper briefly presents the background of the work, the recommended process to identify necessary data, and appropriate taxonomies structuring and harmonizing the collected entries. Finally, the paper summarizes the key findings and recommendations from the IEA Wind Task 33 work

Load and Risk Based Maintenance Management of Wind Turbines

The cost of maintenance is a considerable part of the total life cycle cost in wind turbines, especially for offshore applications. Research has shown that some critical components account for most of the downtime in the wind turbines. An improvement of maintenance practices and focused condition based maintenance for critical components can improve the reliability of the wind turbines; at the same time appropriate maintenance management can reduce maintenance costs.This thesis presents the conceptual application of the reliability centered asset management (RCAM) approach, which was defined for electrical distribution systems by Bertling in 2005, to wind turbine application. Following the RCAM approach failure statistics extracted from the maintenance records of 28 onshore wind turbines, rated 2MW, are presented. It is realized from the statistics that gearbox is a critical component for the system and the gearbox bearings are major cause of failures in gearboxes.A maintenance management framework called self evolving maintenance scheduler (SEMS) is proposed in the thesis. The SEMS framework considers the indication of deterioration from various condition monitoring systems to formulate an optimal maintenance strategy for the damaged component. In addition to SEMS, an artificial neural network (ANN) based condition monitoring approach using the data stored in the supervisory control and data acquisition (SCADA) system is proposed. The proposed approach uses a statistical distance measurement called Mahalanobis distance to identify any abnormal operation of monitored component. A self evolving feature to keep the ANN model up-to-date with the changing operating conditions is also proposed.The proposed ANN based condition monitoring approach is applied for gearbox bearing monitoring to two cases with real SCADA data, from two wind turbines of the same manufacturer, rated 2 MW, and situated in the south of Sweden. The results show that the proposed approach is capable of detecting damage in the gearbox bearings in good time before a complete failure. The application of the proposed condition monitoring approach with the SEMS maintenance management framework has a potential to reduce the maintenance cost for critical components close to end of life

Load and Risk Based Maintenance Management of Wind Turbines

The cost of maintenance is a considerable part of the total life cycle cost in wind turbines, especially for offshore applications. Research has shown that some critical components account for most of the downtime in the wind turbines. An improvement of maintenance practices and focused condition based maintenance for critical components can improve the reliability of the wind turbines; at the same time appropriate maintenance management can reduce maintenance costs.This thesis presents the conceptual application of the reliability centered asset management (RCAM) approach, which was defined for electrical distribution systems by Bertling in 2005, to wind turbine application. Following the RCAM approach failure statistics extracted from the maintenance records of 28 onshore wind turbines, rated 2MW, are presented. It is realized from the statistics that gearbox is a critical component for the system and the gearbox bearings are major cause of failures in gearboxes.A maintenance management framework called self evolving maintenance scheduler (SEMS) is proposed in the thesis. The SEMS framework considers the indication of deterioration from various condition monitoring systems to formulate an optimal maintenance strategy for the damaged component. In addition to SEMS, an artificial neural network (ANN) based condition monitoring approach using the data stored in the supervisory control and data acquisition (SCADA) system is proposed. The proposed approach uses a statistical distance measurement called Mahalanobis distance to identify any abnormal operation of monitored component. A self evolving feature to keep the ANN model up-to-date with the changing operating conditions is also proposed.The proposed ANN based condition monitoring approach is applied for gearbox bearing monitoring to two cases with real SCADA data, from two wind turbines of the same manufacturer, rated 2 MW, and situated in the south of Sweden. The results show that the proposed approach is capable of detecting damage in the gearbox bearings in good time before a complete failure. The application of the proposed condition monitoring approach with the SEMS maintenance management framework has a potential to reduce the maintenance cost for critical components close to end of life

Mixing dynamics in municipal water storage tanks

The purpose of this study is to investigate the effects of different control variables on mixing in municipal water storage tanks using Computational Fluid Dynamics(CFD) solutions with ANSYS FLUENT for isothermal, positively and negatively buoyant inflow conditions. Poor mixing of old water and new water leads to dead zone formation, which when introduced in the distribution system can cause major water quality issues. Data for this study was generated using Multiphase CFD flow modeling technique using Volume of Fluid (VOF) approach with species transport. The vessel was considered to be mixed when Coefficient of Variation (COV) dropped below 0.10. Results of isothermal flows show that for unity aspect ratio vessels, increase in momentum to eight times the baseline value cause a reduction of 67.7% reduction in the COV maximum value. With sufficient momentum, even tall vessels were found to mix. Positively buoyant inflow conditions are therefore uniformly desirable for mixing. Buoyancy was found to be a more dominant source of mixing than momentum. Negatively buoyant solutions indicate a very high dependance of mixing on buoyancy and hence are found to be undesirable to achieve uniform mixing. Results show that with increase in negative buoyancy effects, even at high velocities, the vessel was unmixed. At lower jet momentum and higher buoyancy effects, deadzones occupied more than 50% of the tank volume. Higher aspect ratio vessels are especially prone to stratification and poor mixing was generally observed with negatively buoyant inflows. Overall results reveal that some tanks which did not get mixed during the filling process got mixed in the hold cycle (inflow shutoff). A sufficient settling time is necessary after the fill duration to achieve the highest possible degree of mixing for a tank with a given momentum, aspect ratio and buoyancy --Abstract, page iii