Papers by Author: Keith Worden

Abstract: 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.

Abstract: 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.

Abstract: This paper discusses the main issues of Uncertainty Analysis (UA) in general and also argues and illustrates its particular relevance to structural dynamics. Brief descriptions are given of the most prevalent of the many frameworks for uncertainty representation. The three main uncertainty-related problems of relevance to structural dynamics are then discussed, namely quantification, fusion and propagation. In order to illustrate the application of ideas of UA in a realistic scenario, there then follows a case study conducted on an aerospace structure, namely the wing of a Gnat trainer aircraft. The case study considers evidence-based classifiers as an alternative to probabilistic classifiers for the problem of damage location within the context of Structural Health Monitoring. Dempster-Shafer theory is employed to construct neural network classifiers with the potential to admit ignorance, rather than misclassify.

Abstract: 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.

Abstract: The RAPTOR telescope systems are astronomical observatories that operate in remote locations in New Mexico searching for astrophysical transients called gamma-ray bursts. Their operating condition should remain at good levels in order to have accurate observations. Currently, the first component of the RAPTOR telescopes to fail is a capstan driving mechanism that operates in a run-to failure mode. The capstans wear relatively frequently because of their manufacturing material and can cause damage to other more expensive components, such as the drive wheels and the telescope optics. Monitoring the condition of these systems seems a reasonable solution since the unpredictable rate at which the capstans experience wear, in combination with the remote locations and high duty cycles of these telescope systems, make it unprofitable to choose a strategy of replacing the capstans at chosen intervals. Experimental tests of the telescope systems reported here recorded vibration signals during clockwise and counterclockwise rotations, similar to a motion known as "homing-sequence". The Empirical Mode Decomposition (EMD) method in combination with the Hilbert Transform (HT) and a new alternative method for the estimation of the instantaneous features of a signal that applies an energy tracking operator, called Teager-Kaiser Energy operator, and an energy separation algorithm to the data being analysed, are the time-frequency analysis methods used for analysis here.

Abstract: The central target of this work is to provide an alternative to machine learning approaches to structural health monitoring with one of robust multivariate statistic novelty detection. Damage detection and identification is a procedure that is hierarchical in nature. At its most sophisticated, diagnosis of the damage could include localisation, classification and severity assessment and even go so far as to estimate the time-to-failure of the structure. In this paper, robust multivariate statistics were investigated focused mainly on a high level estimation of the outliers which determines only the presence or absence of novelty - something that is of fundamental interest. These methods allow a diagnosis of deviation from normality and the option of identifying the presence of masking effects caused by multiple outliers. This paper is trying to introduce a new scheme for damage detection by adopting simple measurements and exploiting robust multivariate statistics.

Abstract: The paper summarises some advanced damage detection approaches used for Structural Health Monitoring (SHM) and Condition Monitoring (CM) of wind turbine systems. In the signal processing part, recent time-frequency analysis methods will be presented and examples of their application on condition monitoring of gearboxes will be given. In the pattern recognition part, examples of damage detection in blades will be used to introduce different algorithms for novelty detection.

Abstract: 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.

Abstract: This work constitutes a damage detection study of a glass plate using the statistical approach of outlier analysis, which is also referred to here as novelty detection. A glass plate instrumented with low-profile, surface-bonded transducers is used in the investigation. Ultrasonic Lamb waves are applied for detecting various crack length on the same plate. The study reveals a distinction between the damage and undamaged plate, and also assesses the severity of damage.

Abstract: New generations of offshore wind turbines are playing a leading role in the energy arena. One of the target challenges is to achieve reliable Structural Health Monitoring (SHM) of the blades. Fault detection at the early stage is a vital issue for the structural and economical success of the large wind turbines. In this study, experimental measurements of Frequency Response Functions (FRFs) are used and identification of mode shapes and natural frequencies is accomplished via an LMS system. Novelty detection is introduced as a robust statistical method for low-level damage detection which has not yet been widely used in SHM of composite blades. Fault diagnosis of wind turbine blades is a challenge due to their composite material, dimensions, aerodynamic nature and environmental conditions. The novelty approach combined with vibration measurements introduces an online condition monitoring method. This paper presents the outcomes of a scheme for damage detection of carbon fibre material in which novelty detection approaches are applied to FRF measurements. The approach is demonstrated for a stiffened composite plate subject to incremental levels of impact damage.