&lt;title&gt;Helicopter gearbox diagnostics and prognostics using vibration signature analysis&lt;/title&gt;

Parker, B.; Nigro, Todd M.; Carley, M. P.; Barron, Roger L.; Ward, Dave; Poor, H. Vincent; Rock, Dennis; DuBois, Thomas A.

doi:10.1117/12.152553

Cited by 20 publications

(15 citation statements)

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“…Many of the existing approaches to data-driven prognosis have used artificial neural networks to model the system. 2,3,4,5 Artificial neural networks are a type of model based loosely on the neural structure of the brain, in which the weights of the connections among neurons are automatically adjusted to maximize the fit of the model to the data. 6 Much of the work in prognostics has been for structural prognostics.…”

Section: Data-driven Prognosismentioning

confidence: 99%

A Survey of Data-Driven Prognostics

2005

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Integrated Systems Health Management includes fault detection, fault diagnosis (or fault isolation), and fault prognosis. We define prognosis to be detecting the precursors of a failure, and predicting how much time remains before a likely failure. Algorithms that use the data-driven approach to prognosis learn models directly from the data, rather than using a hand-built model based on human expertise. This paper surveys past work in the datadriven approach to prognosis. It also includes related work in data-driven fault detection and diagnosis, and in model-based diagnosis and prognosis, particularly as applied to space systems.

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Section: Data-driven Prognosismentioning

confidence: 99%

A Survey of Data-Driven Prognostics

2005

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“…In the industrial and manufacturing arenas, prognosis is interpreted to answer the question: what is the remaining useful lifetime of a machine or a component once an impending failure condition is detected and identified? Stochastic Auto-Regressive Integrated Moving Average (ARIMA) models (Jardim-Goncalves et al 1996), fuzzy pattern recognition principles (Frelicot 1996), knowledge-intensive expert systems (Lembessis et al 1989), nonlinear stochastic models of fatigue crack dynamics (Ray & Tangirala 1994), polynomial neural networks (Parker et al 1993), Weibull models (Groer 2000), and other techniques have been introduced over the past years to address the diagnostic/prognostic problem. This paper attempts to address this issue by introducing a novel combination of a "virtual" sensor as a mapping tool between known measurements and "difficult-to-access" quantities and a dynamic wavelet neural network as the "predictor", i.e.…”

Section: Introductionmentioning

confidence: 99%

<title>Intelligent approach to prognostic enhancements of diagnostic systems</title>

et al. 2001

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This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.

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“…Model-based state-estimation methods were suggested in Begg et al (1999). Stochastic autoregressive integrated moving average models (JardimGoncalves et al 1996), fuzzy-pattern recognition principles (Frelicot 1996), knowledge-intensive expert systems (Lembessis et al 1989), nonlinear stochastic models of fatigue crack dynamics (Ray and Tangirala 1996), polynomial neural networks (Parker et al 1993), and *Corresponding author. Email: yhb@sia.cn other approaches have been discussed to address the fault-diagnostic problem.…”

Section: Introductionmentioning

confidence: 99%

Development and application of internet-based remote monitoring and diagnostic platform for key equipment

Huang

Guo

et al. 2007

International Journal of Computer Integrated Manufacturing

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Requirements for high efficiency in manufacturing, while pushing the development of key equipment, need high-performance maintenance as a safeguard. Internet-related techniques are enabling remote monitoring and maintenance to extend beyond local areas. Considering the need for software architecture with openness and interchangeability for flexible selection and replacement of data analysis techniques, this research develops a standardized multi-layer platform using Web Services, realizing data transmission among networks. The independence between the software platform and data analysis guarantees their research and development in parallel. Studies were done on nonlinear-data-analysis techniques, such as wavelet neural networks for fault diagnostics, particle filters for estimates, and support vector regression for condition prediction. These studies were included in a comprehensive application. The experimental results support condition-based maintenance for key equipment.

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<title>Helicopter gearbox diagnostics and prognostics using vibration signature analysis</title>

Cited by 20 publications

References 0 publications

A Survey of Data-Driven Prognostics

A Survey of Data-Driven Prognostics

<title>Intelligent approach to prognostic enhancements of diagnostic systems</title>

Development and application of internet-based remote monitoring and diagnostic platform for key equipment

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