Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

Feng, Yang; Zhang, Xiangfeng; Hong, Jiman; Li, Jun

doi:10.3390/s22208017

Cited by 3 publications

(3 citation statements)

References 28 publications

(33 reference statements)

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“…The length of sliding window m = 2∆t + 1 and cluster center number K have a great influence in the calculation efficiency and accuracy. Considering the calculation complexity of the LTSS matrix, the parameter ∆t is set to take value from the range [4,16] with a step size of 3. K is adaptively determined by DB index as described in Section 2.3.2.…”

Section: Experimental Setup and Data Descriptionmentioning

confidence: 99%

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Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network

Li,

He,

Ren

et al. 2024

Sensors

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The identification of compound fault components of a planetary gearbox is especially important for keeping the mechanical equipment working safely. However, the recognition performance of existing deep learning-based methods is limited by insufficient compound fault samples and single label classification principles. To solve the issue, a capsule neural network with an improved feature extractor, named LTSS-BoW-CapsNet, is proposed for the intelligent recognition of compound fault components. Firstly, a feature extractor is constructed to extract fault feature vectors from raw signals, which is based on local temporal self-similarity coupled with bag-of-words models (LTSS-BoW). Then, a multi-label classifier based on a capsule network (CapsNet) is designed, in which the dynamic routing algorithm and average threshold are adopted. The effectiveness of the proposed LTSS-BoW-CapsNet method is validated by processing three compound fault diagnosis tasks. The experimental results demonstrate that our method can via decoupling effectively identify the multi-fault components of different compound fault patterns. The testing accuracy is more than 97%, which is better than the other four traditional classification models.

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Section: Experimental Setup and Data Descriptionmentioning

confidence: 99%

“…Moreover, the fault features could be impacted by multi-source excitations such as random impacts, timevarying load, strong noise, multi-interface attenuation and so on. As a result, it is very difficult to identify the compound fault of a planetary gearbox [3,4].…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network

Li,

He,

Ren

et al. 2024

Sensors

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“…However, most wind turbines are located in remote areas (e.g., mountains, deserts, and offshore) and are usually long-term, operating in severe weather conditions and complex geographical environments, which leads to frequent failures and even shutdowns [5]. Moreover, unexpected faults and shutdowns result in a higher operation and maintenance (O&M) cost and lower reliability of wind turbines, which may adversely influence the financial gains from wind farms and have a significant impact on the thriving wind power industry [6]. Statistically, the O&M cost of an onshore wind turbine accounts for approximately 10-15% of the total production cost, while this figure is as high as 20-30% for offshore wind turbines [7].…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm

Yan

Liu

et al. 2023

Sensors

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Condition-monitoring and anomaly-detection methods used for the assessment of wind turbines are key to reducing operation and maintenance (O&M) cost and improving their reliability. In this study, based on the sparrow search algorithm (SSA), bidirectional long short-term memory networks with a self-attention mechanism (SABiLSTM), and a binary segmentation changepoint detection algorithm (BinSegCPD), a condition-monitoring method (SSA-SABiLSTM-BinSegCPD, SSD) used for wind turbines is proposed. Specifically, the self-attention mechanism, which can mine the nonlinear dynamic characteristics and spatial–temporal features inherent in the SCADA time series, was introduced into a two-layer BiLSTM network to establish a normal-behavior model for wind turbine key components. Then, as a result of the advantages of searching precision and convergence rate methods, the sparrow search algorithm was employed to optimize the constructed SABiLSTM model. Moreover, the BinSegCPD algorithm was applied to the predicted residual sequence to achieve the automatic identification of deterioration conditions for wind turbines. Case studies conducted on multiple wind turbines located in south China showed that the established SSA-SABiLSTM model was superior to other contrast models, achieving a better prediction precision in terms of RMSE, MAE, MAPE, and R2. The MAE, RMSE, and MAPE of SSA-SABiLSTM were 0.2543 °C, 0.3412 °C, and 0.0069, which were 47.23%, 42.19%, and 53.38% lower than those of SABiLSTM, respectively. The R2 of SABiLSTM was 0.9731, which was 4.6% higher than that of SABiLSTM. The proposed SSD method can detect deterioration conditions 47–120 h in advance and trigger fault alarm signals approximately 36 h ahead of the actual failure time.

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Rolling Bearing Fault Diagnosis under Strong Background Noise Based on ACMD and Optimized MOMEDA

Shi,

Wang,

Huang

et al. 2024

Journal of Sensors

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A method based on adaptive chirp mode decomposition (ACMD) and optimized multipoint optimal minimum entropy deconvolution adjusted (OMOMEDA) is proposed to diagnose the rolling bearing fault in the presence of strong background noise. First, ACMD based on the Gini index is used to separate the low resonance impulse component in the fault signal from the harmonic component and noise. After ACMD, the OMOMEDA process is performed on the low resonance impulse component to enhance the fault impulse. After OMOMEDA, the envelope analysis is conducted on the deconvoluted signal to determine the fault condition. It should be noted that OMOMEDA overcomes the parameters’ artificial dependence of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and realizes a superior optimization solution through two aspects of improvement. First, OMOMEDA introduces the augmented gray wolf optimizer and cuckoo search to achieve adaptive determination of the period parameter. Second, OMOMEDA defines balanced permutation entropy (BPE) and uses BPE as a fitness function for finding the best filter length. The simulation and experimental results show that the proposed method can accurately and effectively diagnose the rolling bearing fault in the presence of strong background noise.

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Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

Cited by 3 publications

References 28 publications

Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network

Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network

Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm

Rolling Bearing Fault Diagnosis under Strong Background Noise Based on ACMD and Optimized MOMEDA

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