scholarly journals A Brief Study on a Novel Approach for the Gearbox Fault Diagnosis

2021 ◽  
Author(s):  
Subrata Mukherjee ◽  
Vikash Kumar ◽  
Somnath Sarangi
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Early Stage ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Support Vector ◽  
Temporal Moments ◽  
The Impact

Fault diagnosis of the gearbox is a decisive part of the modern industry to find the many gearbox defects like gear tooth crack, chipped or broken, etc. But sometimes, the nonstationary properties of vibration signal and low energy of minimal faults make this procedure very challenging. Previously, many types of techniques have been developed for gearbox condition monitoring. But most of the methods are dealing with conventional techniques of the gearbox condition monitoring, such as time-domain analysis or frequency domain analysis. Most of the conventional methods are not suitable for the nonstationary vibration signal. Thus, this paper presents a novel gearbox fault diagnosis technique using conditional temporal moments and an optimizable support vector machine (SVM). This work also presents an integrated features extraction technique based on the standard features, i.e., statistical and spectral features with the combinations of moment features. The impact of the four conditional temporal moments of each gearbox condition is also presented. This work shows that the proposed method successfully classifies and categorizes the gearbox faults at an early stage.

scholarly journals Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Sheng Fu ◽  
Kun Liu ◽  
Yonggang Xu ◽  
Yi Liu
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Early Stage ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Rolling Bearing ◽  
Domain Analysis ◽  
Data Matrix ◽  
Adaptive Fuzzy ◽  
Vibration Signal Analysis

Vibration signal analysis is one of the most effective methods for mechanical fault diagnosis. Available part of the information is always concealed in component noise, which makes it much more difficult to detect the defection, especially at early stage of the development. This paper presents a new approach for mechanical fault diagnosis based on time domain analysis and adaptive fuzzyC-means clustering. By analyzing vibration signal collected, nine common time domain parameters are calculated. This lot of data constitutes data matrix as characteristic vectors to be detected. And using adaptive fuzzyC-means clustering, the optimal clustering number can be gotten then to recognize different fault types. Moreover, five parameters, including variance, RMS, kurtosis, skewness, and crest factor, of the nine are selected as the new eigenvector matrix to be clustered for more optimal clustering performance. The test results demonstrate that the proposed approach has a sensitive reflection towards fault identifications, including slight fault.

Research of Gear Fault Comprehensive Diagnosis Method Based on Analysis of Vibration Signal

2014 ◽  
Vol 898 ◽  
pp. 892-895
Author(s):  
Zhan Jie Lv ◽  
Wen Xu ◽  
Gui Ji Tang ◽  
Guo Dong Han ◽  
Shu Ting Wan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Signal Processing Methods

For gearbox common type of fault, leads to common methods gear fault diagnosis, according to the various parameters of the gearbox, to give a gearbox fault frequencies. Using mat lab signal analysis, by the time domain analysis, frequency domain analysis, cestrum analysis, signal processing methods envelope spectrum consolidated results there is a fault in the gearbox countershaft. This papers they have certain significance to gear fault diagnosis.

scholarly journals Bearing Fault Diagnosis using Support Vector Machine with Genetic Algorithms based Optimization and K Fold Cross-Validation Method

2019 ◽  
Vol 8 (2) ◽  
pp. 3242-3250
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Cross Validation ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Actual Condition ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race

Moving component bearing is utilized to convey radial load and axial load or both just as. REB has nonlinear conduct make issue misalignment, surface waviness, fault happen at the inward race, external race, enclosure, ball or roller, so REB has a restricted life. Our concentration to evacuate fault diagnosis of bearing at the outer race has been investigating. For this purpose, REB vibration analysis is used. This paper present a support vector machine algorithm (SVM) approach with GA (Genetic algorithm) based optimization compare the result with SVM with cross-validation (CV) method along these lines, the information is processed correctly and an exact way. Time-domain Analysis, high pass and low pass filtering etc. used for feature extraction from vibration signal. Further, these feature extraction used as input to the SVM classifier. Support vector machine, a training given projected preparing information, the procedure yield perfect hyperplane. Feature extraction help to provides the actual condition of bearing. In this work, different signal processing techniques and process are used for fault diagnosis of bearing

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

scholarly journals Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

2021 ◽  
pp. 095440622110165
Author(s):  
Zhang Chao ◽  
Wang Wei-zhi ◽  
Zhang Chen ◽  
Fan Bin ◽  
Wang Jian-guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Short Term Memory ◽  
Vibration Signal ◽  
Short Term ◽  
Global Features ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

scholarly journals A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3309 ◽  
Author(s):  
Zia Ullah ◽  
Jin Hur
Keyword(s):  
Fault Diagnosis ◽  
Early Stage ◽  
Short Circuit ◽  
Industrial Applications ◽  
Operational Environment ◽  
Compact Structure ◽  
Torque Density ◽  
Prime Concern ◽  
Attractive Option ◽  
The Impact

Contemporary research has shown impetus in the diagnostics of permanent magnet (PM) type machines. The manufacturers are now more interested in building diagnostics features in the control algorithms of machines to make them more salable and reliable. A compact structure, exclusive high-power density, high torque density, and efficiency make the PM machine an attractive option to use in industrial applications. The impact of a harsh operational environment most often leads to faults in PM machines. The diagnosis and nipping of such faults at an early stage have appeared as the prime concern of manufacturers and end users. This paper reviews the recent advances in fault diagnosis techniques of the two most frequently occurring faults, namely inter-turn short fault (ITSF) and irreversible demagnetization fault (IDF). ITSF is associated with a short circuit in stator winding turns in the same phase of the machine, while IDF is associated with the weakening strength of the PM in the rotor. A detailed literature review of different categories of fault indexes and their strengths and weaknesses is presented. The research trends in the fault diagnosis and the shortcomings of available literature are discussed. Moreover, potential research directions and techniques applicable for possible solutions are also extensively suggested.

scholarly journals Intelligent Fault Diagnosis of Synchromesh Gearbox Using Fusion of Vibration and Acoustic Emission Signals for Performance Enhancement

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
T Praveenkumar ◽  
M Saimurugan ◽  
K I Ramachandran
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Support Vector ◽  
Feature Level Fusion ◽  
Sensor Signals ◽  
Early Fault Detection ◽  
Ae Signals ◽  
Level Fusion

Condition monitoring system monitors the system degradation and it identifies common failure modes. Several sensor signals are available for monitoring the changes in system components. Vibration signal is one of the most extensively used technique for monitoring rotating components as it identifies faults before the system fails. Early fault detection is the significant factor for condition monitoring, where Acoustic Emission ( AE ) sensor signals have been applied for early fault detection due to their high sensitivity and high frequency. In this paper, vibration and acoustic emission signals are acquired under various simulated gear and bearing fault conditions from the synchromesh gearbox. Then the statistical features are extracted from vibration and AE signals and then the prominent features are selected using J48 decision tree algorithm respectively. The best features from the vibration and AE signals are then fused using feature-level fusion strategy and it is classified using Support Vector Machine ( SVM ) and Proximal Support Vector Machine ( PSVM ) classifiers and it is compared with individual signals for fault diagnosis of the synchromesh gearbox. From the experiments, it is observed that the performance of the fault diagnosis system has been improved for the proposed feature level fusion technique compared to the performance of unfused vibration and AE feature sets.

Research on gearbox impact feature extraction method based on improved ESMD

2022 ◽  
Vol 64 (1) ◽  
pp. 20-27
Author(s):  
Fengfeng Bie ◽  
Sheng Gu ◽  
Yue Guo ◽  
Gang Yang ◽  
Jian Peng
Keyword(s):  
Feature Vector ◽  
Vibration Signal ◽  
Permutation Entropy ◽  
Significant Feature ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Envelope Spectrum ◽  
Impact Characteristics ◽  
The Impact

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.

scholarly journals A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3105 ◽  
Author(s):  
Cong Dai Nguyen ◽  
Alexander Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Reference Signal ◽  
Vibration Signal ◽  
Support Vector ◽  
Health States ◽  
Informative Signal ◽  
Vibration Signals ◽  
Optimal Output ◽  
Gear Fault ◽  
Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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