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

Sensors ◽  
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.

scholarly journals Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

2018 ◽  
Vol 211 ◽  
pp. 17009
Author(s):  
Natalia Espinoza Sepulveda ◽  
Jyoti Sinha
Keyword(s):  
Machine Learning ◽  
Frequency Domain ◽  
Time Domain ◽  
Intelligent Systems ◽  
Learning Models ◽  
Machine Vibration ◽  
Vibration Data ◽  
Machine Learning Model ◽  
The Time Domain ◽  
Machine Learning Models

The development of technologies for the maintenance industry has taken an important role to meet the demanding challenges. One of the important challenges is to predict the defects, if any, in machines as early as possible to manage the machines downtime. The vibration-based condition monitoring (VCM) is well-known for this purpose but requires the human experience and expertise. The machine learning models using the intelligent systems and pattern recognition seem to be the future avenue for machine fault detection without the human expertise. Several such studies are published in the literature. This paper is also on the machine learning model for the different machine faults classification and detection. Here the time domain and frequency domain features derived from the measured machine vibration data are used separated in the development of the machine learning models using the artificial neutral network method. The effectiveness of both the time and frequency domain features based models are compared when they are applied to an experimental rig. The paper presents the proposed machine learning models and their performance in terms of the observations and results.

The Engine Fault Diagnosis Based on Time Domain and Frequency Domain

2014 ◽  
Vol 936 ◽  
pp. 2243-2246 ◽  
Author(s):  
Zhu Ting Yao ◽  
Hong Xia Pan
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Water Pressure ◽  
Cooling Water ◽  
Experimental System ◽  
Vibration Signal ◽  
Cylinder Block ◽  
Fuel Temperature ◽  
Multiple Faults

Engine is as a power machine, the operating status is good or bad, directly affects the working status of equipment. The status monitoring and fault diagnosis is very necessary to ensure that the equipment runs in its best, and improves equipment maintenance quality and efficiency. The engine failure shows the complexity and diversity of the interaction and complex relationship between the various subsystems of the engine, that is the fault of complexity, ambiguity, correlation, relativity and multiple faults coexistence. The available information are much in the engine diagnosis, for example, the vibration signal from bearings, cylinder head or cylinder block surface; oil, cooling water, pressure of intake, exhaust and fuel; temperature signal; noise, speed or oil-sample signals. In this paper, an engine as an example, engine fault diagnosis experimental system is built, the normal state, left one and right six cylinders off the oil, air filter blockage (inlet wood blockage is 30%, the inlet has screen cloth.) in the load of 2565Nm, and the speeds of 1500r/min, 1800r/min, 2200r/min are studied. The experimental results analysis, feature extraction and fault diagnosis are finished based on the time domain and frequency domain. Keywords: engine, fault diagnosis, time domain, frequency domain.

scholarly journals An Adaptive Spectral Kurtosis Method Based on Optimal Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
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.

Gearbox Fault Diagnosis Research Based on Wavelet Transform

2013 ◽  
Vol 274 ◽  
pp. 233-236
Author(s):  
G.B. Yu ◽  
Y. Zhao ◽  
J.F. Nie ◽  
B. Dai ◽  
X.W. Song
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Three Dimensional ◽  
Vibration Signal ◽  
Analysis Method ◽  
Failure Frequency ◽  
Gearbox Vibration ◽  
The Time Domain ◽  
Better Than

The principle and application of wavelet analysis and gearbox fault diagnosis are described in the paper. To the experimental measurement of gearbox vibration signal, the time-domain and frequency-domain signals are got by wavelet transform. Then it get a clearly gearbox vibration signal by wavelet de-noising. The residual signal processing and three-dimensional wavelet analysis, it get the failure frequency and determine the fault characteristics. By the experimental signal wavelet transform, it reflects that the wavelet analysis method is better than traditional Fourier transform, so it is an effective method of fault diagnosis.

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

2013 ◽  
Vol 805-806 ◽  
pp. 963-979 ◽  
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.

Vibration Test and Fault Diagnosis for Main Hoist Gearbox of Heavy Casting Crane

2014 ◽  
Vol 532 ◽  
pp. 374-377
Author(s):  
Zhang Li ◽  
Xing Dong Wang ◽  
Chang Yi Hu ◽  
Chi Zhong Chen ◽  
Li Ming
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Vibration Test ◽  
Signal Characteristic ◽  
Engineering Software ◽  
Fault Type ◽  
The Time Domain ◽  
Main Hoist

In view of the structure and running characteristics of gearbox of large and special crane, we have respectively carried out vibration test of fault and free-fault gearbox containing planetary gear in the work. With the help of Matlab engineering software, we can read and process the collected vibration signal of gearbox and draw the time-domain and frequency-domain graph. Through the comparative analysis of vibration information of gearboxes, we can determine the link between fault type and signal characteristic value, effectively realize the fault diagnosis of gearbox.

scholarly journals Fault Diagnosis of Planetary Gearbox Based on Motor Current Signal Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ziyuan Jiang ◽  
Qinkai Han ◽  
Xueping Xu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Signal Analysis ◽  
Current Signal ◽  
Learning Models ◽  
Planetary Gearbox ◽  
Motor Current ◽  
The Time Domain ◽  
Machine Learning Models

Planetary gearbox is one of the most widely used core parts in heavy machinery. Once it breaks down, it can lead to serious accidents and economic loss. Induction motor current signal analysis (MCSA) is a noninvasive method that uses current to detect faults. Currently, most MCSA-based fault diagnosis studies focus on the parallel shaft gearbox. However, there is a paucity of studies on the planetary gearbox. The effect of various signal processing methods on motor current and the performance of different machine learning models are rarely compared. Therefore, fault diagnosis of planetary gearbox based MCSA is conducted in this study. First, the effects of various faults on motor currents are studied. Specifically, the characteristic frequencies of a fault in sun/planet/ring gears and supporting bearings of the planetary gearbox are derived. Then, a signal preprocessing method, namely, singular spectrum analysis (SSA), is proposed to remove the supply frequency component in the current signal. Subsequently, four classical machine learning models, including the support vector machine (SVM), decision tree (DT), random forest (RF), and AdaBoost, are used for fault classifications based on the features extracted via principal component analysis (PCA). The convolutional neural network (CNN), which can automatically extract features, is also adopted. The dynamic experiment of the planetary gearbox with seven types of faults, including tooth chipping in sun/planet/ring gears, inner race spall in planet bearing, inner/outer races, and ball spalls in input support bearing, is conducted. Raw current signal in the time domain, reconstructed signal by SSA, and the current spectra in the frequency domain are used as the inputs of various models. The classification results show that the PCA-SVM is the best model for learned data while CNN is the best model for unlearned data on average. Furthermore, SSA mainly increases the accuracy of CNN in the time domain and exhibits a positive effect on unlearned data in the time domain. The classification accuracy increases significantly after transforming the time domain current data to the frequency domain.

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

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.

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

Entropy ◽  
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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