A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears

Tan, Chee Keong; Irving, P.E.; Mba, David

doi:10.1016/j.ymssp.2005.09.015

Cited by 246 publications

(128 citation statements)

References 14 publications

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“…The results showed that the AEbased approach was more reliable, robust and sensitive to the detection of defects than the vibration based monitoring scheme. Similar approach is presented by Tan et al [12]. In [13], Li and He presented a methodology for gear health monitoring in rotational machinery.…”

Section: Introductionmentioning

confidence: 87%

Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

Delgado-Prieto

Millán

2017

IEEE Trans. Ind. Electron.

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Section: Introductionmentioning

confidence: 87%

Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

Delgado-Prieto

Millán

2017

IEEE Trans. Ind. Electron.

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“…Gear defects: (a) scuffing, 46 (b) pitting 60 and (c) crack. 32 under excessively high stresses; thus, excessive load can cause permanent deformations; the accumulated plastic deformation mechanism consists of low cycle fatigue (LCF).…”

Section: Gearsmentioning

confidence: 99%

A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

Cubillo

Perinpanayagam

Esperon-Miguez

2016

Advances in Mechanical Engineering

204

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Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal-metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery.

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“…Once some abnormalities are detected these components are classed as failing and so preventive maintenance is planned. 79,80 etc. are modules that collect condition monitoring signal from wind turbine controller and transmits it to remote locations where these signals are studied and failures are detected.…”

Section: Section a A1 Planning For A Cbm Maintenancementioning

confidence: 99%

Failure Prognostic Schemes and Database Design of a Software Tool for Efficient Management of Wind Turbine Maintenance

Sinha

Steel

2015

Wind Engineering

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This is an author produced version of a paper published in Wind Engineering (ISSN 0309-524X, eISSN 2048-402X) This version may not include final proof corrections and does not include published layout or pagination. Citation DetailsCitation CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated. This is the author's reviewed version, the full published article may be found at http://dx.doi.org/10.1260/0309-524X.39. AbstractWind Turbines require numerous and varied types of maintenance activities throughout their lifespan, the frequency of which increases with years in operation. At present the proportion of maintenance cost to the total cost for wind turbines is significant particularly for offshore wind turbines (OWT) where this ratio is ~35%. If this ratio is to be reduced in-spite of adverse operating conditions, pre-mature component failures and absence of reliability database for wind turbine components, there is a need to design unconventional maintenance scheme preferably by including novel failure prediction methodologies. Several researchers have advocated the use of Artificial Neural Networks (ANN), Bayesian Network Theory (BNT) and other statistical methods to predict failure so as to plan efficient maintenance of wind turbines, however novelty and randomness of failures, nature and number of parameters involved in statistical calculations and absence of required amount of fundamental work required for such advanced analysis have continued to maintain the high cost of maintenance. This work builds upon the benefits of condition monitoring to design methods to predict generic failures in wind turbine components and exhibits how such prediction methods can assist in cutting the maintenance cost of wind turbines. This study proposes using a dedicated tool to assist with failure prediction and planning and execution of wind turbine maintenance. The design and development of such an all-inclusive tool will assist in performing administrative works, inventory control, financial calculations and service management apart from failure prediction in wind turbine components. Its database will contain reference to standard management practices, regulatory provisions, staff details and their skillsets, service call register, troubleshooting manuals, installation guide, service history, details of customers and clients etc. that would cater to multiple avenues of wind turbine maintenance. In order to build such a software package, a robust design of its database is crucial. This work lists prerequisites for choosing a physical database and identifies the benefits of relational database software in controlling large amounts of data of various formats that are stored in such physical databases. Such a database wou...

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A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears

Cited by 246 publications

References 14 publications

Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

Failure Prognostic Schemes and Database Design of a Software Tool for Efficient Management of Wind Turbine Maintenance

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