Gearbox condition monitoring in wind turbines: A review

Salameh, Jack P.; Cauët, Sébastien; Etien, Erik; Sakout, Anas; Rambault, Laurent

doi:10.1016/j.ymssp.2018.03.052

Cited by 287 publications

(142 citation statements)

References 86 publications

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“…These ripples are attributed to the damage component associated with the first gear that manifest in the SASE of the second gear. This indicates that the SASE of the gear and the pinion are not independent from one another as indicated by Equation (5).…”

Section: Performance Of Smse and Sase In Impulsive Noisementioning

confidence: 97%

“…Gearboxes are critical components in many industries and are subjected to harsh operating conditions which make them susceptible to damage and failure, with long downtimes associated with each replacement [5,6]. Localised gear damage and distributed gear damage modes are frequently encountered in gearboxes and therefore need to be detected and characterised early to ensure that the appropriate maintenance decisions can be made [7].…”

Section: Introductionmentioning

confidence: 99%

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A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments

Schmidt

Zimroz

Chaari

et al. 2020

Sensors

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Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the squared envelope is a relatively simple yet effective method to perform fault detection, fault identification and fault trending under constant and time-varying operating conditions. However, its performance is impeded by the presence of impulsive signal components attributed to impulsive noise or the presence of other damage modes in the machine. In this work, it is proposed that the synchronous median of the squared envelope should be used instead of the synchronous average of the squared envelope for gearbox fault diagnosis. It is shown on numerical and experimental datasets that the synchronous median is more robust to the presence of impulsive signal components and is therefore more reliable for estimating the condition of specific machine components.

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Section: Performance Of Smse and Sase In Impulsive Noisementioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments

Schmidt

Zimroz

Chaari

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In this equation, L is the Lagrangian and η i ,η * i ,α i ,α * i are Lagrange multipliers. Hence the dual variables in Equation (5) have to satisfy the positivity constraints shown in Equation (5). Note that α ( * ) i refers to α i and α * i here.…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…As to the fault detection and prediction of key components in turbines, recent advances in sensors and signal processing have been contributing significantly to the monitoring and prognosis of WT gearboxes. However, accurate fault detection and reliable diagnosis in wind turbine gearboxes are still challenging due to the inherited complexities in mechanical systems and operation conditions [5]. Thus, more accurate and cost-efficient online monitoring is of essential importance on troubleshooting and enhancement of turbine system resilience.…”

Section: Introductionmentioning

confidence: 99%

Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

et al. 2019

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Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

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“…The condition of gearboxes faces several challenges in the coming years [32], as all approaches require techniques for processing and analysis. SCADA requires methods to predict failures in advance with reasonable time for optimal maintenance of the WT.…”

mentioning

confidence: 99%

Maintenance Management in Wind Turbines by Monitoring the Bearing Temperature

Chacón

Zhang

Márquez

2019

Advances in Intelligent Systems and Computing

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Wind turbines are increasing in number, size and market share. It is determined whether they are efficient through operating and maintenance costs. Therefore, one of the main objectives of the wind turbines is to increase the service life of the components by applying different methodologies for fault detection. The gearbox is a critical component since it causes the most downtime and failure rate of the wind turbines. The Supervisory Control and Data Acquisition system offers the measurement of several variables, and by a correct analysis it is possible to detect the faults before they occur. This paper analyses the temperature curve of bearing versus wind speed as significant variables of a gearbox failure for fault detection.

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Gearbox condition monitoring in wind turbines: A review

Cited by 287 publications

References 86 publications

A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments

A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments

Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Maintenance Management in Wind Turbines by Monitoring the Bearing Temperature

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