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15 pages, 4258 KiB

Open AccessArticle

Extreme-Low-Speed Heavy Load Bearing Fault Diagnosis by Using Improved RepVGG and Acoustic Emission Signals

by Peng Jiang, Wenyu Sun, Wei Li, Hongyu Wang and Cong Liu

Sensors 2023, 23(7), 3541; https://doi.org/10.3390/s23073541 - 28 Mar 2023

Cited by 6 | Viewed by 2620

With the worldwide carbon neutralization boom, low-speed heavy load bearings have been widely used in the field of wind power. Bearing failure generates impulses when the rolling element passes the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques [...] Read more.

With the worldwide carbon neutralization boom, low-speed heavy load bearings have been widely used in the field of wind power. Bearing failure generates impulses when the rolling element passes the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect failure signals. However, the high sampling rates of AE signals make it difficult to design and extract fault features; thus, deep neural network-based approaches have been proposed. In this paper, we proposed an improved RepVGG bearing fault diagnosis technique. The normalized and noise-reduced bearing signals were first converted into Mel frequency cepstrum coefficients (MFCCs) and then inputted into the model. In addition, the exponential moving average method was used to optimize the model and improve its accuracy. Data were extracted from the test bench and wind turbine main shaft bearing. Four damage classes were studied experimentally. The experimental results demonstrated that the improved RepVGG model could be employed for classifying low-speed heavy load bearing states by using MFCCs. Furthermore, the effectiveness of the proposed model was assessed by performing comparisons with existing models. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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14 pages, 6815 KiB

Open AccessArticle

A Permanent Magnet Ferromagnetic Wear Debris Sensor Based on Axisymmetric High-Gradient Magnetic Field

by Bin Fan, Yong Liu, Peng Zhang, Lianfu Wang, Chao Zhang and Jianguo Wang

Sensors 2022, 22(21), 8282; https://doi.org/10.3390/s22218282 - 28 Oct 2022

Cited by 4 | Viewed by 1927

Abstract

The detection of wear debris in lubricating oil is effective for determining current equipment operating conditions for fault diagnosis. In this paper, a permanent magnet ferromagnetic wear debris sensor is proposed that is composed of a compact structure and a detection coil that [...] Read more.

The detection of wear debris in lubricating oil is effective for determining current equipment operating conditions for fault diagnosis. In this paper, a permanent magnet ferromagnetic wear debris sensor is proposed that is composed of a compact structure and a detection coil that generates an induced voltage when wear debris passes through a magnetic field. A three-dimensional model of the sensor is established, the internal axisymmetric high-gradient magnetic field of the sensor is analyzed, and a mathematical model of the sensor signal is proposed. The effects of the air gap structure of the sensor and the relative permeability, velocity, and volume of the wear debris on the sensor performance are analyzed. The correctness of the theoretical results is proven by single particle experiments, and the sensor is calibrated to achieve quantitative analysis of the wear debris. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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21 pages, 8231 KiB

Open AccessArticle

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

by Yang Feng, Xiangfeng Zhang, Hong Jiang and Jun Li

Sensors 2022, 22(20), 8017; https://doi.org/10.3390/s22208017 - 20 Oct 2022

Cited by 6 | Viewed by 1974

Abstract

Wind turbines usually operate in harsh environments. The gearbox, the key component of the transmission chain in wind turbines, can easily be affected by multiple factors during the operation process and develop compound faults. Different types of faults can occur, coupled with each [...] Read more.

Wind turbines usually operate in harsh environments. The gearbox, the key component of the transmission chain in wind turbines, can easily be affected by multiple factors during the operation process and develop compound faults. Different types of faults can occur, coupled with each other and staggered interference. Thus, a challenge is to extract the fault characteristics from the composite fault signal to improve the reliability and the accuracy of compound fault diagnosis. To address the above problems, we propose a compound fault diagnosis method for wind turbine gearboxes based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and parallel parameter optimized resonant sparse decomposition (RSSD). Firstly, the MOMEDA is applied to the preprocess, setting the deconvolution period with different fault frequency types to eliminate the interference of the transmission path and environmental noise, while decoupling and separating the different types of single faults. Then, the RSSD method with parallel parameter optimization is applied for decomposing the preprocessed signal to obtain the low resonance components, further suppressing the interference components and enhancing the periodic fault characteristics. Finally, envelope demodulation of the enhanced signal is applied to extract the fault features and identify the different fault types. The effectiveness of the proposed method was verified using the actual data from the wind turbine gearbox. In addition, a comparison with some existing methods demonstrates the superiority of this method for decoupling composite fault characteristics. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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17 pages, 5367 KiB

Open AccessArticle

A Probabilistic Bayesian Parallel Deep Learning Framework for Wind Turbine Bearing Fault Diagnosis

by Liang Meng, Yuanhao Su, Xiaojia Kong, Xiaosheng Lan, Yunfeng Li, Tongle Xu and Jinying Ma

Sensors 2022, 22(19), 7644; https://doi.org/10.3390/s22197644 - 9 Oct 2022

Cited by 9 | Viewed by 2145

Abstract

The technology of fault diagnosis helps improve the reliability of wind turbines. Difficulties in feature extraction and low confidence in diagnostic results are widespread in the process of deep learning-based fault diagnosis of wind turbine bearings. Therefore, a probabilistic Bayesian parallel deep learning [...] Read more.

The technology of fault diagnosis helps improve the reliability of wind turbines. Difficulties in feature extraction and low confidence in diagnostic results are widespread in the process of deep learning-based fault diagnosis of wind turbine bearings. Therefore, a probabilistic Bayesian parallel deep learning (BayesianPDL) framework is proposed and then achieves fault classification. A parallel deep learning (PDL) framework is proposed to solve the problem of difficult feature extraction of bearing faults. Next, the weights and biases in the PDL framework are converted from deterministic values to probability distributions. In this way, an uncertainty-aware method is explored to achieve reliable machine fault diagnosis. Taking the fault signal of the gearbox output shaft bearing of a wind turbine generator in a wind farm as an example, the diagnostic accuracy of the proposed method can reach 99.14%, and the confidence in diagnostic results is higher than other comparison methods. Experimental results show that the BayesianPDL framework has unique advantages in the fault diagnosis of wind turbine bearings. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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22 pages, 1478 KiB

Open AccessArticle

Data-Driven Assessment of Wind Turbine Performance Decline with Age and Interpretation Based on Comparative Test Case Analysis

by Davide Astolfi, Ravi Pandit, Ludovica Celesti, Matteo Vedovelli, Andrea Lombardi and Ludovico Terzi

Sensors 2022, 22(9), 3180; https://doi.org/10.3390/s22093180 - 21 Apr 2022

Cited by 12 | Viewed by 3879

Abstract

An increasing amount of wind turbines, especially in Europe, are reaching the end of their expected lifetimes; therefore, long data sets describing their operation are available for scholars to analyze the performance trends. On these grounds, the present work is devoted to test [...] Read more.

An increasing amount of wind turbines, especially in Europe, are reaching the end of their expected lifetimes; therefore, long data sets describing their operation are available for scholars to analyze the performance trends. On these grounds, the present work is devoted to test case studies for the evaluation and the interpretation of wind turbine performance decline with age. Two wind farms were studied, featuring widely employed wind turbine models: the former is composed of 6 Senvion MM92 and the latter of 11 Vestas V52 wind turbines, owned by the ENGIE Italia company. SCADA data spanning, respectively, 10 and 7 years were analyzed for the two test cases. The effect of aging on the performance of the test case wind turbines was studied by constructing a data-driven model of appropriate operation curves, selected depending on the working region. For the Senvion MM92, we found that it is questionable to talk about performance aging because there is no evident trend in time: the performance variation year by year is in the order of a few kW and is therefore irrelevant for practical applications. For the Vestas V52 wind turbines, a much wider variability is observed: two wind turbines are affected by a remarkable performance drop, after which the behavior is stable and under-performing with respect to the rest of the wind farm. Particular attention is devoted to the interpretation of the results: the comparative discussion of the two test cases indicates that the observed operation curves are compatible with the hypothesis that the worsening with age of the two under-performing Vestas V52 can be ascribed to the behavior of the hydraulic blade pitch. Furthermore, for both test cases, it is estimated that the gearbox-aging contributes negligibly to the performance decline in time. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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17 pages, 8348 KiB

Open AccessArticle

Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

by Maria del Cisne Feijóo, Yovana Zambrano, Yolanda Vidal and Christian Tutivén

Sensors 2021, 21(10), 3333; https://doi.org/10.3390/s21103333 - 11 May 2021

Cited by 27 | Viewed by 4390

Abstract

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this [...] Read more.

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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Figure 1
Fixed-type WT foundations [<a href="#B9-sensors-21-03333" class="html-bibr">9</a>]. Monopile (a), jacket (b), and gravity-based (c). Full article ">Figure 2
Location of the sensors (accelerometers) in the structure [<a href="#B19-sensors-21-03333" class="html-bibr">19</a>]. Full article ">Figure 3
Damage location in the different levels of the jacket structure (left), [<a href="#B19-sensors-21-03333" class="html-bibr">19</a>]. Crack damage where L is the length of the bar, d = 5 mm is the crack size, and <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>=</mo> <mi>L</mi> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math> is the location of the crack in the bar (right). Full article ">Figure 4
Flowchart of the stated damage detection methodology. Full article ">Figure 5
Autoencoder proposed architecture. There are 3 inputs (mean, standard deviation, and entropy) for each one of the 24 sensors giving a total of 72 inputs. The hidden layer is set to 30 nodes. Full article ">Figure 6
Mean squared error of training and validation data sets. Full article ">Figure 7
RE for test samples corresponding to amplitude 0.5 (left) and zoom in the region with y-axis from 0 to 6 (right). Full article ">Figure 8
RE for test samples corresponding to amplitude 1 (left) and zoom in the region with y-axis from 0 to 7 (right). Full article ">Figure 9
RE for test samples corresponding to amplitude 2 (left) and zoom in the region with y-axis from 0 to 6 (right). Full article ">

22 pages, 6062 KiB

Open AccessEditor’s ChoiceArticle

Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

by Ángel Encalada-Dávila, Bryan Puruncajas, Christian Tutivén and Yolanda Vidal

Sensors 2021, 21(6), 2228; https://doi.org/10.3390/s21062228 - 23 Mar 2021

Cited by 62 | Viewed by 10657

Abstract

As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long [...] Read more.

As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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20 pages, 2638 KiB

Open AccessEditor’s ChoiceArticle

An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

by Mattia Beretta, Anatole Julian, Jose Sepulveda, Jordi Cusidó and Olga Porro

Sensors 2021, 21(4), 1512; https://doi.org/10.3390/s21041512 - 22 Feb 2021

Cited by 29 | Viewed by 5638

Abstract

A novel and innovative solution addressing wind turbines’ main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as [...] Read more.

A novel and innovative solution addressing wind turbines’ main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs. Results of interpretable algorithms, which are tailored to capture specific aspects of main bearing failures, are merged into a combined health status indicator making use of Ensemble Learning principles. Based on multiple specialized indicators, the interpretability of the results is greater compared to black-box solutions that try to address the problem with a single complex algorithm. The proposed methodology has been tested on a dataset covering more than two year of operations from two onshore wind farms, counting a total of 84 turbines. All four main bearing failures are anticipated at least one month of time in advance. Combining individual indicators into a composed one proved effective with regard to all the tracked metrics. Accuracy of 95.1%, precision of 24.5% and F1 score of 38.5% are obtained averaging the values across the two windfarms. The encouraging results, the unsupervised nature and the flexibility and scalability of the proposed solution are appealing, making it particularly attractive for any online monitoring system used on single wind farms as well as entire wind turbine fleets. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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33 pages, 9872 KiB

Open AccessArticle

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

by Diego Sandoval, Urko Leturiondo, Yolanda Vidal and Francesc Pozo

Sensors 2021, 21(3), 849; https://doi.org/10.3390/s21030849 - 27 Jan 2021

Cited by 21 | Viewed by 3932

Abstract

To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads [...] Read more.

To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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17 pages, 37699 KiB

Open AccessArticle

Development of a Linear Acoustic Array for Aero-Acoustic Quantification of Camber-Bladed Vertical Axis Wind Turbine

by Abdul Hadi Butt, Bilal Akbar, Jawad Aslam, Naveed Akram, Manzoore Elahi M Soudagar, Fausto Pedro García Márquez, Md. Yamin Younis and Emad Uddin

Sensors 2020, 20(20), 5954; https://doi.org/10.3390/s20205954 - 21 Oct 2020

Cited by 13 | Viewed by 3936

Abstract

Vertical axis wind turbines (VAWT) are a source of renewable energy and are used for both industrial and domestic purposes. The study of noise characteristics of a VAWT is an important performance parameter for the turbine. This study focuses on the development of [...] Read more.

Vertical axis wind turbines (VAWT) are a source of renewable energy and are used for both industrial and domestic purposes. The study of noise characteristics of a VAWT is an important performance parameter for the turbine. This study focuses on the development of a linear microphone array and measuring acoustic signals on a cambered five-bladed 45 W VAWT in an anechoic chamber at different tip speed ratios. The sound pressure level spectrum of VAWT shows that tonal noises such as blade passing frequencies dominate at lower frequencies whereas broadband noise corresponds to all audible ranges of frequencies. This study shows that the major portion of noise from the source is dominated by aerodynamic noises generated due to vortex generation and trailing edge serrations. The research also predicts that dynamic stall is evident in the lower Tip speed ratio (TSR) region making smaller TSR values unsuitable for a quiet VAWT. This paper compares the results of linear aeroacoustic array with a 128-MEMS acoustic camera with higher resolution. The study depicts a 3 dB margin between two systems at lower TSR values. The research approves the usage of the 8 mic linear array for small radius rotary machinery considering the results comparison with a NORSONIC camera and its resolution. These observations serve as a basis for noise reduction and blade optimization techniques. Full article

(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)

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