Novelty detection in a changing environment: A negative selection approach

Surace, Cecilia; Worden, Keith

doi:10.1016/j.ymssp.2009.09.009

Cited by 54 publications

(23 citation statements)

References 17 publications

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“…These algorithms assess statistical distributions of the measured or derived features to enhance the damage identification process [204]. When applied in an Unsupervised Learning mode, statistical models are typically used to answer questions regarding the existence and location of damage.…”

Section: Outlier Detectionmentioning

confidence: 99%

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Martínez-Luengo

Kolios

Wang

2016

Renewable and Sustainable Energy Reviews

217

124

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Section: Outlier Detectionmentioning

confidence: 99%

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Martínez-Luengo

Kolios

Wang

2016

Renewable and Sustainable Energy Reviews

217

124

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“…In [34], Surace and Worden propose a negative selection algorithm inspired by the human immune system to detect novelties in structures subject to varying operational and environmental conditions. Cointegration is a promising analysis tool imported from the field of econometrics.…”

Section: Different Approaches To Damage Assessment Of Time-varying Symentioning

confidence: 99%

Damage assessment of pre-stressed structures: A SVD-based approach to deal with time-varying loading

Ruocci

Cumunel

et al. 2014

Mechanical Systems and Signal Processing

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ABSTRACT. Vibration-based methods are well-established and effective tools to assess the health state of civil, mechanical and aerospace engineering structures. However, their reliability is still affected by the variability of the features commonly used for damage detection. Environmental effects and changes in operational conditions are the main sources of variability in the structural response. As a consequence, the modal identification used to extract damage sensitive features has to face constricting requirements in terms of signals stationarity and performance accuracy. Moreover, with reference to the damage assessment, large variations of monitored features mask subtle effects due to damage, which remain undetected. This study is conceived to address both these issues by focusing, in particular, on the non-stationarity of the loading conditions of tensioned structures, such as cables and pre-stressed beams. The capability of spectral methods to deal with the modal identification of non-stationary systems is enhanced by a curve-fitting procedure based on nonlinear least squares optimisation. Wavelet analysis is applied for comparison and validation of the FFT-based technique. Identified natural frequencies are then used for the damage detection, exploiting the capacity of singular values decomposition to discriminate between damage-related events and the intrinsic non-stationary nature of the structural response. A reduced-order realization of the features set is performed to amplify changes not belonging to measurement variability but deriving from exogenous events, such as damage. The proposed methodology is validated by experimental analyses carried out on beams subjected to time-varying loading conditions in order to simulate the health monitoring of quasi and non-stationary systems.

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“…Novelty detection methods that have been used in health monitoring include probability/density estimation methods [2][3][4][5], immune system based methods [6], neural networks [2,[7][8], support vector methods [5,[9][10], etc. Markou and Singh made outstanding reviews on these methods [1,11].…”

Section: Introductionmentioning

confidence: 99%

Novelty detection methods for online health monitoring and post data analysis of turbopumps

Zhang

et al. 2013

J Mech Sci Technol

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As novelty detection works when only normal data are available, it is of considerable promise for health monitoring in cases lacking fault samples and prior knowledge. In this paper, two novelty detection methods are presented for health monitoring of turbopumps in large-scale liquid-propellant rocket engines. The first method is the adaptive Gaussian threshold model. This method is designed to monitor the vibration of the turbopumps online because it has minimal computational complexity and is easy for implementation in real time. The second method is the One-Class Support Vector Machine (OCSVM) which is developed for post analysis of historical vibration signals. Via post analysis the method not only confirms the online monitoring results but also provides diagnostic results so that faults from sensors are separated from those actually from the turbopumps. Both of these two methods are validated to be efficient for health monitoring of the turbopumps.

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Novelty detection in a changing environment: A negative selection approach

Cited by 54 publications

References 17 publications

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Damage assessment of pre-stressed structures: A SVD-based approach to deal with time-varying loading

Novelty detection methods for online health monitoring and post data analysis of turbopumps

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