2023
DOI: 10.48084/etasr.5595
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A Fault Diagnosis Technique for Wind Turbine Gearbox: An Approach using Optimized BLSTM Neural Network with Undercomplete Autoencoder

Abstract: The gearbox is one of the critical components of a wind turbine. Proactive maintenance of wind turbine gearboxes is crucial to decrease maintenance and operational costs and the long downtime of the complete system. As the gearbox is a significant part of the wind turbine, a fault in the gearbox leads to the breakdown of the wind turbine system. Hence, it is important to study and analyze the faults in wind turbine gearbox systems. In this article, a neural network-based model, a Bidirectional Long Short-Term … Show more

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Cited by 11 publications
(5 citation statements)
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“…Ref. [73] used a model based on a BLSTM neural network together with an autoencoder, with the function of identifying faults in a speed multiplier box in a wind turbine. The performance of the suggested model was analyzed using a vibration vibration dataset.…”
Section: Approach Based On Detection Of Anomalies and Failuresmentioning
confidence: 99%
“…Ref. [73] used a model based on a BLSTM neural network together with an autoencoder, with the function of identifying faults in a speed multiplier box in a wind turbine. The performance of the suggested model was analyzed using a vibration vibration dataset.…”
Section: Approach Based On Detection Of Anomalies and Failuresmentioning
confidence: 99%
“…They claimed that BLSTM accuracy with an incomplete autoencoder is extremely reliable and suitable for time series data-based health monitoring of wind turbine gearbox systems. Authors in [20] studied metallic clutch damper springs included in the 1-D modeling of the powertrain system that was subjected to vibration optimization using the Simulated Annealing (SA) technique. This cutting-edge technology expedites the optimization of the engine system's vibration and offers presumptions that save money and time during actual vehicle testing.…”
Section: Wwwetasrcom Mogal Et Al: Fault Diagnosis Of Rotating Machine...mentioning
confidence: 99%
“…An auto encoder and bidirectional long short-term memory (BLSTM) are used in a neural network-based model presented by Sreenatha, M. and P. Mallikarjuna [9] to categorize the state of the gearbox for wind turbines into excellent or bad (broken tooth) condition. To assess the trade-off between three functions-axle stiffness, assemblability score, and overall mass-a MOOP is performed in [10].…”
Section: Introductionmentioning
confidence: 99%