Disk failure prediction based on multi-layer domain adaptive learning

Large scale data storage is susceptible to failure. As disks are damaged and replaced, traditional machine learning models, which rely on historical data to make predictions, struggle to accurately predict disk failures. This paper presents a novel method for predicting disk failures by leveraging multi-layer domain adaptive learning techniques. First, disk data with numerous faults is selected as the source domain, and disk data with fewer faults is selected as the target domain. A training of the feature extraction network is performed with the selected origin and destination domains. The contrast between the two domains facilitates the transfer of diagnostic knowledge from the domain of source and target. According to the experimental findings, it has been demonstrated that the proposed technique can generate a reliable prediction model and improve the ability to predict failures on disk data with few failure samples

Data Mining for Anomaly Detection

The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments

Online Novelty Detection System: One-Class Classification of Systemic Operation

Presented is an Online Novelty Detection System (ONDS) that uses Gaussian Mixture Models (GMMs) and one-class classification techniques to identify novel information from multivariate times-series data. Multiple data preprocessing methods are explored and features vectors formed from frequency components obtained by the Fast Fourier Transform (FFT) and Welch\u27s method of estimating Power Spectral Density (PSD). The number of features are reduced by using bandpower schemes and Principal Component Analysis (PCA). The Expectation Maximization (EM) algorithm is used to learn parameters for GMMs on feature vectors collected from only normal operational conditions. One-class classification is achieved by thresholding likelihood values relative to statistical limits. The ONDS is applied to two different applications from different application domains. The first application uses the ONDS to evaluate systemic health of Radio Frequency (RF) power generators. Four different models of RF power generators and over 400 unique units are tested, and the average robust true positive rate of 94.76% is achieved and the best specificity reported as 86.56%. The second application uses the ONDS to identify novel events from equine motion data and assess equine distress. The ONDS correctly identifies target behaviors as novel events with 97.5% accuracy. Algorithm implementation for both methods is evaluated within embedded systems and demonstrates execution times appropriate for online use

An integrated data-driven model-based approach to condition monitoring of the wind turbine gearbox

Condition Monitoring (CM) is considered an effective method to improve the reliability of wind turbines and implement cost-effective maintenance. This paper presents a single hidden-layer feed forward neural network (SLFN), trained using an extreme learning machine (ELM) algorithm, for condition monitoring of wind turbines. Gradient-based algorithms are commonly used to train SLFNs; however, these algorithms are slow and may become trapped in local optima. The use of an ELM algorithm can dramatically reduce learning time and overcome issues associated with local optima. In this paper, the ELM model is optimized using a genetic algorithm. The residual signal obtained by comparing the model and actual output is analyzed using the Mahalanobis distance measure due to its ability to capture correlations among multiple variables. An accumulated Mahalanobis distance value, obtained from a range of components, is used to evaluate the heath of a gearbox, one of the critical subsystems of a wind turbine. Models have been identified from supervisory control and data acquisition (SCADA) data obtained from a working wind farm. The results show that the proposed training method is considerably faster than traditional techniques, and the proposed method can efficiently identify faults and the health condition of the gearbox in wind turbines

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

Data-driven Models for Remaining Useful Life Estimation of Aircraft Engines and Hard Disk Drives

Failure of physical devices can cause inconvenience, loss of money, and sometimes even deaths. To improve the reliability of these devices, we need to know the remaining useful life (RUL) of a device at a given point in time. Data-driven approaches use data from a physical device to build a model that can estimate the RUL. They have shown great performance and are often simpler than traditional model-based approaches. Typical statistical and machine learning approaches are often not suited for sequential data prediction. Recurrent Neural Networks are designed to work with sequential data but suffer from the vanishing gradient problem over time. Therefore, I explore the use of Long Short-Term Memory (LSTM) networks for RUL prediction. I perform two experiments. First, I train bidirectional LSTM networks on the Backblaze hard-disk drive dataset. I achieve an accuracy of 96.4\% on a 60 day time window, state-of-the-art performance. Additionally, I use a unique standardization method that standardizes each hard drive instance independently and explore the benefits and downsides of this approach. Finally, I train LSTM models on the NASA N-CMAPSS dataset to predict aircraft engine remaining useful life. I train models on each of the eight sub-datasets, achieving a RMSE of 6.304 on one of the sub-datasets, the second-best in the current literature. I also compare an LSTM network\u27s performance to the performance of a Random Forest and Temporal Convolutional Neural Network model, demonstrating the LSTM network\u27s superior performance. I find that LSTM networks are capable predictors for device remaining useful life and show a thorough model development process that can be reproduced to develop LSTM models for various RUL prediction tasks. These models will be able to improve the reliability of devices such as aircraft engines and hard-disk drives

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

Detection of Anomalies in User Behaviour

Internetové vyhledáváče se staly nepostradatelným nástrojem našeho každodenního života. Možnost pohodlně a bez prodlevy vyhledávát informace denně přiláká miliardy lidí. Bohužel někteří uživatelé, lidští či naprogramovaní, se snaží vyhledávače zneužívat ve svůj prospěch, a to například tím, že klikají v nadměrném množství na výsledky vedoucí na jejich doménu, aby zvýšili její popularitu, a tím zlepšili pořadí domény mezi výsledky. Takové chování však může často vést ke zhoršení uživatelského požitku ostatních návštěvníků, a obecně funkcionality vyhledávače. Možností, jak se proti podvodnému a jinému atypickému chování bránit, mají vyhledávače málo, jelikož musí zůstat snadno dostupné. Z důvodu obrovského objemu uživatelů také nepřipadá v úvahu detekovat podvádějící uživatele manuálně, což dále znemožňuje případné natrénování jednoduchého klasifikátoru. Tato bakalářská práce se zabývá způsoby hledání klasifikátoru pomocí metody učení bez učitele, který umožní detekovat toto anomální uživatelské chování. Dohromady ukazuje tři modely využívající různé charakteristiky uživatelských relací. Předbězné výsledky ukazují, že dosažené poznatky by se po dalším rozpracování mohly využít i v praxi.Search engines have become a fundamental tool of everyday live, billions of users are using them to get the information they desire in a comfortable way. Unfortunately, some visitors exhibit various kinds of malicious behavior. For instance, they try to deplete competitions advertising budget through excessive clicking on sponsored results. Such anomalous behavior often leads to a worsened user experience of "normal users". In addition, due to the vast amounts of visitors, such behavior is hard to detect manually, which further means that we can't use standard supervised methods to train a user classifier. This thesis introduces three unsupervised models for atypical user detection, all of them evaluate distinct user session characteristics, for instance, click, query and behavioral patterns. The preliminary results show, that with some further improvements the current findings could be deployed for real world use