Fault diagnosis using quantitative and qualitative knowledge integration

Benkhedda, H.; Patton, Ron J.

doi:10.1049/cp:19960663

Cited by 4 publications

(2 citation statements)

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“…The prior knowledge of the process is represented in terms of quantitative or qualitative functional relationships and often dictates the approach to developing such models. A good survey on various physics-based diagnostic models can be obtained in the following literature [13][14][15].…”

Section: Data-driven Approaches To Systems Health Managementmentioning

confidence: 99%

“…Table 5 represents the outcome M using Eqs. (13) and (14). Note that because the classifier is not exclusively characterizing the changes in the signature (i.e., healthy-defective), it would very often observe a majority of common attributes in a given data set, and therefore imposing selection criteria and mutual information [Eqs.…”

Section: Combining Information From Sensor Pairsmentioning

confidence: 99%

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Classifying Induced Damage in Composite Plates Using One-Class Support Vector Machines

2010

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For many engineering and aerospace applications, detection and quantification of multiscale damage in fiberreinforced composite structures is increasing in importance. Consequently, the development of an efficient and costeffective diagnosis scheme that can accurately sense, characterize, and evaluate the existence of any form of damage will offer significant potential for improving the performance, reliability, and extending the operational life of these complex systems. We present an approach to characterize and classify different damage states in composite laminates by measuring the change in the signature of the resultant wave that propagates through the anisotropic media under forced excitation. The wave propagation is measured using surface-mounted piezoelectric transducers. Sensor signals collected from test specimens with various forms of induced damage are analyzed using a patternrecognition algorithm known as the one-class support vector machines. The one-class support-vector-machine algorithm performs automatic anomaly detection and classification of damage signatures using various features from the sensor readings. The results obtained suggest that the one-class support-vector-machine algorithm, along with appropriate preprocessing techniques, can often achieve better accuracy than the popular k-nearest-neighbor method in detecting and classifying anomalies caused by structural defects, even when the perturbations caused in the sensor signals due to different damage states are minimal.Nomenclature fx = decision function Kx; x j = inner product kernel R ij = matrix with correct classification rate w = weight vector wn = random noise x i = input pattern y i = label i = Lagrangian multipliers = slack variable = bias = time delay = kernel width

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Section: Data-driven Approaches To Systems Health Managementmentioning

confidence: 99%

Section: Combining Information From Sensor Pairsmentioning

confidence: 99%