crack fault classification for planetary gearbox based

Fault Detection and Maintenance Prediction for Gear of

2019-6-20used vibration signals as features and for fault detection support vector machine is used Biswanath Samanta [3] also did experiments using artificial neural network and SVM Sreenath et al [4] have used Navie Bayes and decision tree algorithm-s for classification Many research articles used tree-based methods like decision tree and SVM

Fault Diagnosis and Prognosis of Critical Components

Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System An Effective Fault Feature Extraction

Local and Global SR for Bearing Sensor

2020-4-14Local and Global SR for Bearing Sensor-based Vibration Signal Classification Shaohui Zhang a Man Wang a Canyi Du b * and Edgar Estupinan c a School of Mechanical Engineering Dongguan University of Technology Dongguan 523808 China b School of Automobile and Transportation Engineering Guangdong Polytechnic Normal University Guangzhou

Figure 4 from Crack propagation assessment for spur

Model-based gear dynamic analysis and simulation has been a promising way for developing effective gearbox vibration monitoring approaches In this paper based on the dynamic model of a one-stage gearbox with spur gears and one tooth crack statistical indicators and the discrete wavelet transform (DWT) technique are investigated to identify effective and sensitive health indicators for

Fault Diagnosis and Prognosis of Critical Components

Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System An Effective Fault Feature Extraction

Gearbox fault classification using dictionary sparse

An approach based on dictionary learning for sparse representations of vibration signals aiming at gearbox fault detection and classification is proposed A gearbox signal dataset with 900 records considering the normal case and nine fault classes is analyzed A dictionary is learned by using a training set of signals from the normal case

Transmission characteristics of planetary gear wear in

Gear tooth surface wear is a common failure mode It takes a long time and can cause other major faults The wear fault signal is weak and hard to identify This paper reveals the transmission characteristics of the planetary gear wear and the relationship between multistage gear meshing through the transmission characteristic analysis Taking the multistage gear transmission system fault

Gearbox fault classification using dictionary sparse

An approach based on dictionary learning for sparse representations of vibration signals aiming at gearbox fault detection and classification is proposed A gearbox signal dataset with 900 records considering the normal case and nine fault classes is analyzed A dictionary is learned by using a training set of signals from the normal case

Gear fault feature extraction and diagnosis method

M Khazaee H Ahmadi M Omid A Banakar and A Moosavian Feature–level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals Insight 55 (6) (2013) 323–329 CrossRef Google Scholar

Fault diagnosis for planetary gearboxes using multi

2019-12-10Fault diagnosis for planetary gearboxes using multi-criterion fusion feature selection framework Application of spectral kurtosis for detection of a tooth crack in planetary gear of wind turbine Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

A Transient Feature Learning

improving the classification accuracy of the planetary gearbox fault diagnosis model with DAE-ELM Experiments on real data show that the proposed method has higher diagnosis accuracy The rest of the paper is organized as follows The preliminaries are briefly reviewed in Section 1 Section 2 introduces the proposed method The

EMD

And then a planetary gearbox condition recognition method is proposed based on EMD and EDT First extract the new combination feature parameter of each selected IMF and constitute the characteristic matrix as the input of EDT calculating and then output the evaluation results intelligently

DC Fault Detection and Classification Approach of

Abstract: Because the adaptive learning degree of the existing protection scheme in MMC-HVDC system is low a novel DC fault detection and classification scheme based convolutional neural network(CNN) is proposed The approach is divided into CNN DC fault detection model (model I) and CNN DC fault classification model (model II) The wavelet logarithmic energy entropy (WLEE) is applied as the

Intelligent Detection of a Planetary Gearbox

Due to the existence of multiple rotating parts in the planetary gearbox—such as the sun gear planet gears planet carriers and its unique planetary motion etc —the vibration signals generated under multiple fault conditions are time-varying and nonstable thus making fault diagnosis difficult In order to solve the problem of planetary gearbox composite fault diagnosis an improved

Fault diagnosis for wind turbine planetary gearboxes

Downloadable (with restrictions)! Planetary gearboxes play an important role in wind turbine drive trains Fault diagnosis of planetary gearboxes is a key topic for maintenance of wind turbines Considering the spectral complexity of planetary gearbox vibration signals as well as their amplitude modulation and frequency modulation (AMFM) nature we propose a simple yet effective method to

An Evaluation of Empirical Approach for Gearbox

planetary gearbox in an excavator using vehicle-based test First we analyze the types of gear faults and the parts where failures occur mainly through the database of field failures From this result several fault types to be used in the experiment are selected and an arbitrary fault is applied to gears

A novel gearbox fault feature extraction and

2019-8-22HEWT a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices Then the singular value vectors as the fault feature vectors were acquired by applying the SVD Last the SOM was used for automatic gearbox fault identification and classification

Incipient fault detection for the planetary gearbox in

2020-2-1Three fault modes (crack pitting and tooth chipped) of a planetary gearbox used in rotary aircraft are investigated and the fault symptoms for each fault mode is identified Then a fault indicator is extracted for each fault mode based on the statistical metric of the analog tachometer signals

Resultant vibration signal model based fault diagnosis

Downloadable (with restrictions)! Planetary gear trains equipped in wind turbine often run under slow speed and non-stationary load condition The incipient gear faults in a wind turbine gearbox can hardly be detected yet might cause tremendous loss In order to detect the incipient faults a resultant vibration signal model is proposed to characterize the faulty features of a single stage

Analysis of weak faults of planetary gears based on

A planetary gearbox fault diagnosis test rig (including experimental systems and three-dimensional models of the experimental systems [21]) as shown in Fig 12 was used to validate the proposed method The planetary gearbox is comprised of a sun gear three planetary gears a planetary

Rough set based rule learning and fuzzy classification

The fault diagnosis problem is conceived as a classification problem In the present study vibration signals are used for fault diagnosis of centrifugal pumps using wavelet analysis Rough set theory is applied to generate the rules from the vibration signals Based on the strength of the rules the faults are identified The different faults considered for this study are: pump at good

Rough set based rule learning and fuzzy classification

The fault diagnosis problem is conceived as a classification problem In the present study vibration signals are used for fault diagnosis of centrifugal pumps using wavelet analysis Rough set theory is applied to generate the rules from the vibration signals Based on the strength of the rules the faults are identified The different faults considered for this study are: pump at good

An integrated approach to planetary gearbox fault

2016-12-29Aiming at improving the accuracy of planetary gearbox fault diagnosis an integrated scheme based on dimensionality reduction method and deep belief networks (DBNs) is presented in this paper Blunt D M and Keller J A 2006 Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis 2011 Multi-sensor health

EMD

And then a planetary gearbox condition recognition method is proposed based on EMD and EDT First extract the new combination feature parameter of each selected IMF and constitute the characteristic matrix as the input of EDT calculating and then output the evaluation results intelligently

2016-12-16And machinery intelligent fault diagnosis is a promising tool to deal with mechanical big data In the big data era new opportunities have been brought to intelligent fault diagnosis For instance data-centric academic thinking will become mainstream it makes fault diagnosis in the system level possible and a comprehensive analysis of faults becomes a trend

A novel gearbox fault feature extraction and

2019-8-22HEWT a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices Then the singular value vectors as the fault feature vectors were acquired by applying the SVD Last the SOM was used for automatic gearbox fault identification and classification

Pitting damage levels estimation for planetary gear

The planetary gearbox is a critical mechanism in helicopter transmission systems Tooth failures in planetary gear sets will cause great risk to helicopter operations (2009) Gear Crack Level Identification Based on Weighted K Nearest Neighbor Classification Algorithm " (2003) Gearbox fault diagnosis using adaptative wavelet filter "

Fault diagnosis for wind turbine planetary gearboxes

Downloadable (with restrictions)! Planetary gearboxes play an important role in wind turbine drive trains Fault diagnosis of planetary gearboxes is a key topic for maintenance of wind turbines Considering the spectral complexity of planetary gearbox vibration signals as well as their amplitude modulation and frequency modulation (AMFM) nature we propose a simple yet effective method to

Gear Crack Level Classification Based on EMD and EDT

Gears are the most essential parts in rotating machinery Crack fault is one of damage modes most frequently occurring in gears So this paper deals with the problem of different crack levels classification The proposed method is mainly based on empirical mode decomposition (EMD) and Euclidean distance technique (EDT) First vibration signal acquired by accelerometer is processed by EMD and