Vibration Signal-Based Diagnosis Of Wind Turbine

Di: Ava

This research provides a condition monitoring and predictive maintenance framework for wind turbines based on artificial intelligence. This paper aims to create a model that categorizes various blade defects using statistical attributes with acquired vibration signals. According to the vibration signal characteristics of WTs, Ren et al. [20] proposed a fault diagnosis method based on Variational Mode Decomposition (VMD) Multi-scale Permutation Entropy (MPE) and Feature-based Transfer Learning (FTL) to monitor the health condition of WT gearbox under variable working conditions.

Wind turbine condition monitoring dataset of Fraunhofer LBF

This dataset provides vibration data for faulty wind turbine blades, which covers common vibration excitation mechanisms associated with various faults and operating conditions, including wind speed. Numerous approaches have been developed for WT gearbox condition monitoring and diagnosis based on vibration signals, which could be categorized as model-based, signal-based and data-driven methods (A. Wang et al., 2022, Wang et al., 2022, Wang et al., 2022). Model-based approaches rely on a high level of expert knowledge to build physical or Currently, most fault diagnosis methods for wind turbine gearboxes rely on certain unimodal signal, such as vibration or current, which cannot enable reliable and satisfactory performance due to its limited presentation ability. To this end, this paper proposes a new multiview enhanced fault diagnosis framework to learn the correlated and complementary

(PDF) Monitoring Wind Turbine Vibration Based on SCADA Dataresearch ...

Relying on expert diagnosis, it solves the problem of fan failure efficiency and meets the needs of automatic inspection and intelligent operation monitoring of fans. In order to make up for the deficiency of intelligent diagnosis of bearing fault based on vibration signal detection, signal transformation, and convolution neural network identification and improve the In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade. The bump wavelet-based continuous wavelet transform with a convolutional neural network model is employed to classify the faulty wind turbine blades based on the extracted vibration signals of turbine blades. This approach distinguishes between different states of blade faults affecting wind turbine blades during operational phases.

The methodologies for analysis and diagnosis of bearing faults are commonly based on vibration signals which offer more useful information than other types of signals. This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade.

Owing to the shortage of available labeled data on wind turbine bearings, a new wind turbine bearing fault diagnosis method based on a dynamic multi-adversarial adaptive network (DMAAN) was proposed. In this new method, a laboratory data were used to obtain fault diagnosis models for wind turbine bearings. The first step was evaluating the interdomain

In wind turbine planetary gearboxes, the sun gears are generally designed to float in the radial direction to achieve the uniform load distribution among different planet gears, but will lead to gear mesh errors and cause complicated vibration signals on the contrary. The floating sun gear is misdiagnosed as a distributed defect easily. Additionally, the floating sun gear’s The nonstationary operation of wind turbines leads to evolving fault characteristics and spectral leakage in collected vibration signals. These complex operating conditions often mask weaker faults beneath more severe ones, causing existing methods to frequently overlook less prominent fault features. This article introduces a novel adaptive variational mode extraction (AVME) This paper analyzes the structure features of different drivetrains of mainstream wind turbines and introduces a vibration data acquisition system. Almost all the research on the vibration-based diagnosis algorithm for wind turbines in the past decade is reviewed, with its effects being discussed.

The method is evaluated by the simulation and experiment signals. Abstract The fault diagnosis of wind turbines is crucial for wind power generation. However, the violent variation in wind power and directions of offshore wind turbines often results in high nonstationary vibration signals, which poses a challenge for effective fault Offshore wind turbines play a vital role in transferring wind energy to electricity, which could help relieve the energy crisis and improve the global climate. In general, offshore wind turbines are installed open sea to avoid the potential interruption of people’s daily life. In such kind of harsh operating environment, the wind turbine transmission system is prone to Wind energy is one of the fast evolving renewable energy sources that has seen widespread application. Therefore, research on its carrier, the wind turbine, is growing, and the majority of them concentrate on the diagnosis of wind turbine faults. In this paper, the vibration signals collected in the time domain by vibration monitoring were analyzed, and the fault

CHAOZHAO-1/Awsome-Multi-modal-based-PHM

Prolonged exposure of wind turbine blades to wind forces can lead to blade twisting and structural loosening. These defects result in uneven mass distribution, causing severe vibrations in wind turbines, which reduce energy efficiency and increase operational costs. To address the challenges of weak vibration signal feature extraction and poor diagnostic model In this paper, an experimental setup is designed and constructed to successfully collect damage acoustic emission signals from wind turbine pitch bearings, and spatiotemporal clustering fault mapping based on acoustic emission with channel-attention-depth subdomain adaptive residual network (AMDSAN) is proposed for condition Diagnosis of bearing faults has significant meaning to the maintenance of wind turbines in real industry. Well-performed bearing fault diagnosis generally requires effective features extracted from vibration signals. However, conventional methods have shortages at obtaining comprehensive information of vibration signals.

(PDF) Fault Diagnosis of Wind Turbine Gearbox Using Vibration Scatter ...

Vibration signal analysis-based fault diagnosis techniques have yielded remarkable outcomes in the past, thanks to the affordability of acceleration sensors and the easy accessibility of vibration signals, thereby leading to their widespread application. Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational Floating wind turbines (FWTs) operate in offshore environments under harsh and varying operating conditions, making frequent in situ monitoring dangerous for maintenance teams and costly for operators. Remote and automated diagnosis, including the stages of detection, identification, and severity characterization of early stage damages in FWTs through

The fault diagnosis of the gearbox of wind turbines is a crucial task for wind turbine operation and maintenance. Although a convolutional neural network can extract the related information of adjacent sampling points using kernels, traditional deep learning methods have not leveraged related information from points with a large span of vibration signal data. In this article, a novel Abstract—The fault diagnosis of wind turbines under nonstationary conditions is still challenging. This paper proposes a novel tacho-less generalized demodulation (NTLGD) method for the wind turbine fault diagnosis. First, one instantaneous frequency is extracted from the time-frequency representation of the vibration signal. Signal analysis and machine learning are common methods for diagnosing rotor imbalances. The signal serves as the primary carrier of operational information. 13 Most of the recent detections of wind turbine imbalances have been based on vibration analysis. 14–16 With the advances in sensor technology, various methods, such as spectral analysis, 17 Lamb

This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades. The blade conditions were healthy, and transverse cracked at the root, This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor However, there are still many challenges in applying deep learning methods to wind turbine vibrational signals. First of all, the wind turbine is in a complex working condition during actual operation, which will generate very strong noises. Noises will overwhelm the vibration amplitude changes caused by the BRL change.

Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study September 2023 DOI: Enhancing the reliability of wind turbines (WTs) is essential for reducing operational and maintenance costs in wind farms. However, the challenges of effectively extracting spatiotemporal features of fault signals in harsh environments, along with the limitations imposed by traditional diagnostics that rely solely on a single signal, inhibit improvements in diagnostic accuracy. To

This paper proposes a new intelligent fault diagnosis approach based on multimodal deep learning to fuse vibration and current signals to diagnose wind turbine gearbox faults. The proposed method typically consists of modality-specific feature learning network and feature fusion network, specifically based on a popular deep learning model named deep belief Vibration signal analysis-based fault diagnosis techniques have yielded remarkable outcomes in the past, thanks to the affordability of acceleration sensors and the easy accessibility of vibration signals, thereby leading to their widespread application.

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