&lt;title&gt;Neural-networks-based sensor validation and recovery methodology for advanced aircraft engines&lt;/title&gt;

Uluyol, Önder; Buczak, Anna L.; Nwadiogbu, Emmanuel

doi:10.1117/12.434229

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…In cases where the input vector represents sensor measurements, AANN have been proved successful in noise filtering. Applications of pre-processing sensor measurements for noise filtering on gas turbines using AANN have been discussed in [71][72][73][74].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A review of model based and data driven methods targeting hardware systems diagnostics

Skliros¹,

Miguez²,

Ali³

et al. 2018

Diagnostyka

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System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A review of model based and data driven methods targeting hardware systems diagnostics

Skliros¹,

Miguez²,

Ali³

et al. 2018

Diagnostyka

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“…The residuals between the model output and the input can then be used to detect anomalies and isolate faults. See [Uluyol 2001 andUluyol 2003] for previous applications of this approach for in-range sensor fault detection, isolation and recovery.…”

Section: Fault Detection Using Associative Modelsmentioning

confidence: 99%

Multi-turbine Associative Model for Wind Turbine Performance Monitoring

Uluyol

Parthasarathy

2012

PHM_CONF

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Comparing the performance parameters of a set of wind turbines in a single region will provide insights that prevent raising unnecessary alarm, while confirming actual faults. Wind turbines operating in a wind farm experience similar operating and environmental conditions that could indicate either normality for that group or failures that manifest in those conditions. These norms and failures are an orthogonal set of data rich in information that can be utilized in performance monitoring algorithms to supply better prediction accuracy and low false positives. In this paper, we describe the use of an associative model (AM) for fault detection in a population. An associative model maps system parameters to an identical set of virtual parameters. The AM-based approach can be used to capture the underlying correlation of an observable system, such as performance parameters of a set of wind turbines in a wind farm. The residuals between the model output and the input can then be used to detect anomalies and isolate faults.

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“…4, to produce an output that is identical to the input at the final layer [21]. This type of network learns an approximation of the identity mapping between the inputs and the outputs.…”

Section: Non-linear Pcamentioning

confidence: 99%

Use of SCADA Data for Failure Detection in Wind Turbines

Kim

Parthasarathy

Uluyol

et al. 2011

ASME 2011 5th International Conference on Energy Sustainability, Parts A, B, and C

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110

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High operations and maintenance costs for wind turbines reduce their overall cost effectiveness. One of the biggest drivers of maintenance cost is unscheduled maintenance due to unexpected failures. Continuous monitoring of wind turbine health using automated failure detection algorithms can improve turbine reliability and reduce maintenance costs by detecting failures before they reach a catastrophic stage and by eliminating unnecessary scheduled maintenance. A SCADA (Supervisory Control and Data Acquisition System) -data based condition monitoring system uses data already collected at the wind turbine controller. It is a cost-effective way to monitor wind turbines for early warning of failures and performance issues. In this paper, we describe our exploration of existing wind turbine SCADA data for development of fault detection and diagnostic techniques for wind turbines. We used a number of measurements to develop anomaly detection algorithms and investigated classification techniques using clustering algorithms and principal components analysis for capturing fault signatures. Anomalous signatures due to a reported gearbox failure are identified from a set of original measurements including rotor speeds and produced power.

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<title>Neural-networks-based sensor validation and recovery methodology for advanced aircraft engines</title>

Cited by 3 publications

References 2 publications

A review of model based and data driven methods targeting hardware systems diagnostics

A review of model based and data driven methods targeting hardware systems diagnostics

Multi-turbine Associative Model for Wind Turbine Performance Monitoring

Use of SCADA Data for Failure Detection in Wind Turbines

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