Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems

Byington, Carl S.; Garga, Amulya

doi:10.1201/9781420038545.ch23

Cited by 6 publications

(13 citation statements)

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“…The benefit of decision level fusion on estimation of the remaining useful life is shown in Figure 5.10. Kozlowski developed a feature-level fusion approach for battery systems that proved to be very robust (see the discussion in Byington and Garga [16]). Erdley [29] tigated several voting schemes for data fusion for the Penn State mechanical diagnostic test bed, and showed the utility of fusion methods (see Figure 5.11).…”

Section: Decision Level Fusionmentioning

confidence: 99%

“…Various assessments of the state of the art in condition-based maintenance have been conducted. See for example the work by Hall [58] and by Byington and Garga [16]. Additional information is provided by Kumara et al [64], [15], [73], and [87].…”

Section: Integration Of Diagnostics Into Ground Equipment Studymentioning

confidence: 99%

“…First, while mechanical failure is initiated at a materials level, typical sensors observe the results at a subsystem or platform level. Byington and Garga [16] describe an experiment performed with a simple two-gear mechanical gearbox (called the mechanical diagnostic test bed).…”

Section: 431mentioning

confidence: 99%

“…Prognosis is a judgment about the future status based on available information and experience. To be intelligent is to possess powers of learning, reasoning, and / or understanding, especially to a high degree [16].…”

Section: Introduction and Definitionsmentioning

confidence: 99%

“…Wavelet transform has been proved to be more appropriate for analysis of such a signal. The Wavelet Transform (WT) is capable of decomposing a signal into different frequencies with different resolutions (Multi-resolution Analysis-MRA) [16], i.e., it provides a time-scale (frequency) representation of the signal. The power of the WT comes from the fact that WT is able to: 1.…”

Section: Introduction and Definitionsmentioning

confidence: 99%

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Integration of Diagnostics into Ground Equipment Study. Volume 2

Kumara¹,

Gautam²,

Hall³

et al. 2003

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Section: Decision Level Fusionmentioning

confidence: 99%

Section: Integration Of Diagnostics Into Ground Equipment Studymentioning

confidence: 99%

Section: 431mentioning

confidence: 99%

Section: Introduction and Definitionsmentioning

confidence: 99%

Section: Introduction and Definitionsmentioning

confidence: 99%

See 3 more Smart Citations

Integration of Diagnostics into Ground Equipment Study. Volume 2

Kumara¹,

Gautam²,

Hall³

et al. 2003

Self Cite

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Hierarchical HMMs for Autonomous Diagnostics and Prognostics

Camci

Chinnam

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Multilevel Decision Fusion in a Distributed Active Sensor Network for Structural Damage Detection

Wang

Foliente

et al. 2006

Structural Health Monitoring

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Distributed sensor networks are emerging as a critical technical driver in the application of structural health monitoring for large-scale structures as a result of their excellent abilities to enhance the reliability and robustness of monitoring systems. One of the key technical opportunities in the implementation of a distributed sensor network is the application of information fusion. Not only does this enable the integration of data from all sensors for the comprehensive assessment of structural conditions, but it also facilitates the combination of decisions or perceptions from multiple sources or different approaches. In this article, the feasibility of combining a distributed sensor network and several techniques of multilevel decision fusion are demonstrated for damage detection. The level-one decision fusion is first implemented by individual active sensors to create their own perceptions on structural health status. During the fusion process, the active sensors first interrogated local physical sensor nodes in the network, and then combined the perceptions of the local sensors in terms of correlation between features extracted from raw signals and damage scenarios in a knowledge database. Meanwhile, the perceptions of active sensors on structural health status are integrated to represent the decision fusion at level two. Considering the level-two decisions made from different technical approaches, a further combination of all decisions is conducted with implication of information fusion at a higher decision level. As a consequence, the risks of a malfunction of individual sensors or the inappropriateness of individual assessment procedures are significantly reduced, and a robust and error-tolerant structural health monitoring system can be developed. Such an approach is successfully validated by an experimental case study of damage detection in CF/EP composite structures.

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Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems

Cited by 6 publications

References 0 publications

Integration of Diagnostics into Ground Equipment Study. Volume 2

Integration of Diagnostics into Ground Equipment Study. Volume 2

Hierarchical HMMs for Autonomous Diagnostics and Prognostics

Multilevel Decision Fusion in a Distributed Active Sensor Network for Structural Damage Detection

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