Anti-noise motor fault diagnosis method based on decision tree and the feature extraction methods in the time domain and frequency domain

Correctly identifying human activities is very significant in modern life. Almost all feature extraction methods are based directly on acceleration and angular velocity. However, we found that some activities have no difference in acceleration and angular velocity. Therefore, we believe that for these activities, any feature extraction method based on acceleration and angular velocity is difficult to achieve good results. After analyzing the difference of these indistinguishable movements, we propose several new features to improve accuracy of recognition. We compare the traditional features and our custom features. In addition, we examined whether the time-domain features and frequency-domain features based on acceleration and angular velocity are different. The results show that (1) our custom features significantly improve the precision of the activities that have no difference in acceleration and angular velocity; and (2) the combination of time-domain features and frequency-domain features does not significantly improve the recognition of different activities.

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A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Sensors ◽

10.3390/s18103521 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3521 ◽

Cited By ~ 6

Author(s):

Funa Zhou ◽

Po Hu ◽

Shuai Yang ◽

Chenglin Wen

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Real Time ◽

Time Domain ◽

Feature Fusion ◽

Rotating Machinery ◽

Time Domain Data ◽

Diagnosis Method ◽

The Time Domain ◽

Potential Frequency

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

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Turbine Generator Fault Diagnosis Based on Time Domain Average Laplace Wavelet

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.701 ◽

2012 ◽

Vol 220-223 ◽

pp. 701-704

Author(s):

Xue Ping Ren ◽

Jin Lei Wang

Keyword(s):

Fourier Transform ◽

Fault Diagnosis ◽

Correlation Coefficient ◽

Time Domain ◽

Wavelet Filter ◽

Turbine Generator ◽

Filter Theory ◽

Diagnosis Method ◽

The Time Domain ◽

Correlation Filtering

In view of turbine generator vibration abnormal,introduces fault diagnosis method based on time domain average Laplace wavelet analysis, and successfully applied to the site. Briefly introduced the time domain average and Laplace wavelet filter theory and algorithms, first use time domain average extract the cycle signal component from complex signal.then use Laplace wavelet correlation filtering to get correlation coefficient, then get characteristic frequency from the correlation coefficient of Fourier transform in order to achieve fault diagnosis. Finally it verified the effectiveness of this method through an instance of the fault diagnosis of turbine generator bearings not verify.

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An Adaptive Spectral Kurtosis Method Based on Optimal Filter

Shock and Vibration ◽

10.1155/2017/6987250 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Yanli Yang ◽

Ting Yu

Keyword(s):

Fault Diagnosis ◽

Frequency Domain ◽

Time Domain ◽

Experimental Results ◽

Bearing Fault ◽

Spectral Kurtosis ◽

Bearing Fault Diagnosis ◽

The Core ◽

Factors Influencing ◽

The Time Domain

As a useful tool to detect protrusion buried in signals, kurtosis has a wide application in engineering, for example, in bearing fault diagnosis. Spectral kurtosis (SK) can further indicate the presence of a series of transients and their locations in the frequency domain. The factors influencing kurtosis values are first analyzed, leading to the conclusion that amplitude, not the frequency of signals, and noise make major contribution to kurtosis values. It is helpful to detect impulsive components if the components with big amplitude are removed from composite signals. Based on this cognition, an adaptive SK algorithm is proposed in this paper. The core steps of the proposed SK algorithm are to find maxima, add window around maxima, merge windows in the frequency domain, and then filter signals according to the merged window in the time domain. The parameters of the proposed SK algorithm are varying adaptively with signals. Some experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

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Induction Motor Fault Diagnosis Using Voltage Spectrum of Auxiliary Winding and Lissajous Curve of its Park Components

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.805-806.963 ◽

2013 ◽

Vol 805-806 ◽

pp. 963-979 ◽

Cited By ~ 4

Author(s):

Lamiaâ El Menzhi ◽

Abdallah Saad

Keyword(s):

Fourier Transform ◽

Fault Diagnosis ◽

Frequency Domain ◽

Induction Motor ◽

Discrete Fourier Transform ◽

Time Domain ◽

New Method ◽

Spectrum Simulation ◽

Simulation Results ◽

The Time Domain

In this paper, a new method for induction motor fault diagnosis is presented. It is based on the so-called an auxiliary winding voltage and its Park components. The auxiliary winding is a small coil inserted between two of the stator phases. Expressions of the inserted winding voltage and its Park components are presented. After that, discrete Fourier transform analyzer is required for converting the signals from the time domain to the frequency domain. A Lissajous curve formed of the two Park components is associated to the spectrum. Simulation results curried out for non defected and defected motor show the effectiveness of the proposed method.

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Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

Sensors ◽

10.3390/s21041483 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1483

Author(s):

Yu Wang ◽

Lei Chen ◽

Yang Liu ◽

Lipeng Gao

Keyword(s):

Fault Diagnosis ◽

Frequency Domain ◽

Time Domain ◽

Vibration Signal ◽

Training Phase ◽

Practical Applications ◽

The Time Domain ◽

Domain Information ◽

Machine Learning Models ◽

Better Than

Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

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Sound Visualization and Convolutional Neural Network in Fault Diagnosis of Electric Motorbike

Traitement du signal ◽

10.18280/ts.380626 ◽

2021 ◽

Vol 38 (6) ◽

pp. 1819-1827

Author(s):

Jian-Da Wu ◽

Che-Yuan Hsieh ◽

Wen-Jun Luo

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Power Systems ◽

Convolutional Neural Network ◽

Frequency Domain ◽

Time Domain ◽

Electric Motor ◽

Combustion Engine ◽

Electric Power System ◽

The Time Domain

This study proposed convolutional neural network (CNN) training for different figure recognition to diagnose electric motorbike faults. Traditional motorbike maintenance is usually carried out by technicians to find the problem step by step. Many resources are wasted and time consumed in diagnosing maintenance problems. Due to rising environmental protection awareness, motorbike power systems gradually transformed from combustion engines into the electric motor. The sound amplitude generated by the combustion engine is great and may cover other faulty sounds. The electric power system sound amplitude is greatly decreased, permitting various fault diagnosis to be performed by extracting the electric motor sound. With the development of computers and image processing, deep learning neural network for picture recognition technology becomes more feasible. This study presents the motor system sound visualization for fault diagnosis. First obtain the sound signals of the motor in the five different states of the operation in the laboratory and the road test, and draw the time domain graph, frequency domain graph and spectrogram graph to be used as the test database. The results graphs of various states were trained through a CNN. The signal states were then classified to achieve fault diagnosis. Experiments and identification results show that the spectrogram and CNN method can identify motorbike faults most effectively compared to the time domain graph and the frequency domain graph.

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Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Entropy ◽

10.3390/e23121704 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1704

Author(s):

Jiaqi Xue ◽

Biao Ma ◽

Man Chen ◽

Qianqian Zhang ◽

Liangjie Zheng

Keyword(s):

Fault Diagnosis ◽

Time Domain ◽

Vibration Signal ◽

Bench Test ◽

Vehicle Performance ◽

Vibration Signals ◽

Hilbert Spectrum ◽

Wet Clutch ◽

Diagnosis Method ◽

The Time Domain

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.

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Application of Xgboost Feature Extraction in Fault Diagnosis of Rolling Bearing

Mechanical Engineering Science ◽

10.33142/me.v1i2.1659 ◽

2020 ◽

Vol 1 (2) ◽

Author(s):

Xingang WANG ◽

Chao WANG

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Fault Diagnosis ◽

Frequency Domain ◽

Time Domain ◽

Vibration Signal ◽

Rolling Bearing ◽

Support Vector ◽

Time Frequency ◽

Diagnosis Accuracy

Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy, a fault diagnosis method based on Xgboost algorithm feature extraction is proposed. When the Xgboost algorithm classifies features, it generates an order of importance of the input features. The time domain features were extracted from the vibration signal of the rolling bearing, the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition. Firstly, the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy. Then, Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis. Finally, important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy. The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost.

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Analysis and Implementation of Brain Waves Feature Extraction and Classification to Control Robotic Hand

Iraqi Journal of Information & Communications Technology ◽

10.31987/ijict.1.3.35 ◽

2019 ◽

Vol 1 (3) ◽

pp. 31-41 ◽

Cited By ~ 1

Author(s):

Mohammed Z. Al-Faiz ◽

Ammar A. Al-hamadani

Keyword(s):

Feature Extraction ◽

Dimensionality Reduction ◽

Frequency Domain ◽

Error Rate ◽

Time Domain ◽

Channel Selection ◽

Extraction Methods ◽

Robotic Hand ◽

Selection Algorithm ◽

Absolute Value

In this paper, (i) time domain, frequency domain and spatial domain feature extraction methods were investigated. (ii) Two dimensionality reduction methods were proposed, implemented and compared. (iii) The method pair (feature extraction + dimensionality reduction) that owns the lowest classification error rate will be used to learn a machine learning algorithm to control robotic hand in offline mode. Two classes EEG dataset of three bipolar channels was used. The extracted feature vectors were fed into Support Vector Machine with Radial Basis Function kernel (SVM-RBF) to train the classifier. The experimented time domain feature extraction methods were: Mean Absolute Value (MAV), integrated Absolute Value (IAV), Zero Crossing (ZC), Root Mean Square (RMS), Waveform Length (WL) and Slope Sign Change (SSC). Frequency domain feature was the Autoregressive Feature (AR). Finally, the spatial domain feature was the Common Spatial Patterns (CSP). Matlab codes for Principal Component Analysis (PCA) and channel selection algorithm were designed and used to reduce the dimensionality of the features vector. Results showed that CSP features got the lowest error rate for both dimensionality reduction technique with 2.14%. Results recommends to use channel selection algorithm over PCA since it owns the lowest processing time of 8.2s over 8.5s for PCA.

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