Ifigeneia Antoniadou

Fault detection in rolling element bearings using wavelet-based variance analysis and novelty detection

Fractal signal processing and novelty detection are used for fault detection in rolling element bearings. The former applies the concept of self-similarity based on wavelet variance, and the latter is based on machine learning and utilises artificial neural networks. The method is demonstrated using simulated and experimental vibration data. The work presented involves validation both on laboratory test rig data and industrial wind turbine data. The results show that the method can be used successfully for automated fault detection in ball bearings under real operational conditions.

A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm

The use of offshore wind farms has been growing in recent years. Europe is presenting a geometrically growing interest in exploring and investing in such offshore power plants as the continents water sites offer impressive wind conditions. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore, their reliability is crucial if they ever need to be fully integrated into the energy arena. This paper presents an analysis of supervisory control and data acquisition (SCADA) extracts from the Lillgrund offshore wind farm for the purposes of monitoring. An advanced and robust machine-learning approach is applied, in order to produce individual and population-based power curves and then predict measurements of the power produced from each wind turbine (WT) from the measurements of the other WTs in the farm. Control charts with robust thresholds calculated from extreme value statistics are successfully applied for the monitoring of the turbines.

Aspects of structural health and condition monitoring of offshore wind turbines

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading

The remarkable evolution of new generation wind turbines has led to a dramatic increase of wind turbine blade size. In turn, a reliable structural health monitoring (SHM) system will be a key factor for the successful implementation of such systems. Detection of damage at an early stage is a crucial issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of Frequency Response Functions (FRFs) extracted by using an input/output acquisition technique under a fatigue loading of a 9m CX-100 blade at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC) performed in the Los Alamos National Laboratory. The blade was harmonically excited at its first natural frequency using a Universal Resonant Excitation (UREX) system. For analysis, the Auto-Associative Neural Network (AANN) is a non-parametric method where a set of damage sensitive features gathered from the measured structure are used to train a network that acts as a novelty detector. This traditionally has a highly complex bottleneck structure with five layers in the AANN. In the current paper, a new attempt is also exploited based on an AANN with one hidden layer in order to reduce the theoretical and computational difficulties. Damage detection of composite bodies of blades is a grand challenge due to varying aerodynamic and gravitational loads and environmental conditions. A study of the noise tolerant capability of the AANN which is associated to its generalisation capacity is addressed. It will be shown that vibration response data combined with AANNs is a robust and powerful tool, offering novelty detection even when operational and environmental variations are present. The AANN is a method which has not yet been widely used in the structural health monitoring of composite blades.

An illustration of new methods in machine condition monitoring, Part I: Stochastic resonance

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach.

Machine Learning Applications for a Wind Turbine Blade under Continuous Fatigue Loading

Structural health monitoring (SHM) systems will be one of the leading factors in the successful establishment of wind turbines in the energy arena. Detection of damage at an early stage is a vital issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of vibration analysis, extracted of a 9m CX-100 blade under fatigue loading. For analysis, machine learning techniques utilised for failure detection of wind turbine blades will be applied, like non-linear Neural Networks, including Auto-Associative Neural Network (AANN) and Radial Basis Function (RBF) networks models.

On damage detection in wind turbine gearboxes using outlier analysis

The proportion of worldwide installed wind power in power systems increases over the years as a result of the steadily growing interest in renewable energy sources. Still, the advantages offered by the use of wind power are overshadowed by the high operational and maintenance costs, resulting in the low competitiveness of wind power in the energy market. In order to reduce the costs of corrective maintenance, the application of condition monitoring to gearboxes becomes highly important, since gearboxes are among the wind turbine components with the most frequent failure observations. While condition monitoring of gearboxes in general is common practice, with various methods having been developed over the last few decades, wind turbine gearbox condition monitoring faces a major challenge: the detection of faults under the time-varying load conditions prevailing in wind turbine systems. Classical time and frequency domain methods fail to detect faults under variable load conditions, due to the temporary effect that these faults have on vibration signals. This paper uses the statistical discipline of outlier analysis for the damage detection of gearbox tooth faults. A simplified two-degree-of-freedom gearbox model considering nonlinear backlash, time-periodic mesh stiffness and static transmission error, simulates the vibration signals to be analysed. Local stiffness reduction is used for the simulation of tooth faults and statistical processes determine the existence of intermittencies. The lowest level of fault detection, the threshold value, is considered and the Mahalanobis squared-distance is calculated for the novelty detection problem.

Cointegration and the Empirical Mode Decomposition for the Analysis of Diagnostic Data

The use of cointegration has been proposed recently as a potentially powerful means of removing confounding influences from structural health monitoring (SHM) data. On the other hand the Empirical Mode Decomposition method is a recent multi-scale decomposition technique with the ability to decompose a signal into meaningful signal components. In this paper the EMD method will be used to highlight the dominant time-scales that have been affected by varying environmental and operational conditions and the time-scales that are related to damage. Then cointegration will be used to remove the nonstationary effects not associated with damage at the time-scales of interest in the data. The final step of the damage detection approach proposed, will be the use of two different amplitude-frequency separation methods, the Hilbert Transform and the more recent Teager Kaiser energy operator approach in order to compare the merits of both, concerning the estimation of the instantaneous characteristics of the signals.

An SHM View of a CFD Model of Lillgrund Wind Farm

Reliability of offshore wind farms is one of the key areas for the successful implementation of these renewable power plants in the energy arena. Failure of the wind turbine (WT) in general could cause massive financial losses but especially for structures that are operating in offshore sites. Structural Health Monitoring (SHM) of WTs is essential in order to ensure not only structural safety but also avoidance of overdesign of components that could lead to economic and structural inefficiency. A preliminary analysis of a machine learning approach in the context of WT SHM is presented here; it is based on results from a Computational Fluid Dynamics (CFD) model of Lillgrund Wind farm. The analysis is based on neural network regression and is used to predict the measurement of each WT from the measurements of other WTs in the farm. Regression model error is used as an index of abnormal response.

Envelope Analysis Using the Teager-Kaiser Energy Operator for Condition Monitoring of a Wind Turbine Bearing

Different signal processing methods are applied to experimental data obtained from a rolling element bearing rig in order to perform damage detection. Among these methods the Teager-Kaiser energy operator is also proposed as a more novel approach. This energy operator is an amplitude-frequency demodulation method used in this paper as an alternative to the Hilbert Transform in order to perform envelope analysis on the datasets analysed.