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Wyszukujesz frazę "bearing fault" wg kryterium: Temat


Tytuł:
An improved feature extraction method for rolling bearing fault diagnosis based on MEMD and PE
Autorzy:
Zhang, H.
Zhao, L.
Liu, Q.
Luo, J.
Wei, Q.
Zhou, Z.
Qu, Y.
Powiązania:
https://bibliotekanauki.pl/articles/259770.pdf
Data publikacji:
2018
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
improved feature extraction method
rolling bearing fault diagnosis
MEMD
PE
Opis:
The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.
Źródło:
Polish Maritime Research; 2018, S 2; 98-106
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Experimental studies for bearings degradation monitoring at an early stage using analysis of variance
Autorzy:
Zarour, D.
Meziani, S.
Thomas, M.
Powiązania:
https://bibliotekanauki.pl/articles/328396.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
ANOVA
DOE
time descriptors
bearing fault
analiza wariancji
łożysko
usterka
deskryptory czasu
Opis:
This work presents a procedure for bearing degradation monitoring at an early stage. The analysis of variance (ANOVA) coupled with Tukey’s test is used to single out the suitable parameters to follow the fault size evolution ranging from 50 µm to 150µm. The Tukey's criterion is adopted in this case to study the ability of time and frequency indicators. The rotational speed, centrifugal load and fault size are considered as independent variables while the time and frequency indicators are taken as dependent variables. The experiments are performed on bearings having a fault on outer race. Based on the results of this study, the Kurtosis and Skewness show a good ability to assess the evolution of degradation in the bearings at an early stage. The paper discusses the weakness of the time and frequency indicators.
Źródło:
Diagnostyka; 2018, 19, 4; 81-87
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Improved EMD Method Based on Utilizing Certain Inflection Points in the Construction of Envelope Curves
Autorzy:
Kafil, Mohsen
Darabi, Kaveh
Ziaei-Rad, Saeed
Powiązania:
https://bibliotekanauki.pl/articles/31339815.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
empirical mode decomposition
EMD
interpolation points
envelope curve
inflection points
rolling element bearing fault diagnosis
Opis:
The empirical mode decomposition (EMD) algorithm is widely used as an adaptive time-frequency analysis method to decompose nonlinear and non-stationary signals into sets of intrinsic mode functions (IMFs). In the traditional EMD, the lower and upper envelopes should interpolate the minimum and maximum points of the signal, respectively. In this paper, an improved EMD method is proposed based on the new interpolation points, which are special inflection points (SIPn) of the signal. These points are identified in the signal and its first (n − 1) derivatives and are considered as auxiliary interpolation points in addition to the extrema. Therefore, the upper and lower envelopes should not only pass through the extrema but also these SIPn sets of points. By adding each set of SIPi (i = 1, 2, n) to the interpolation points, the frequency resolution of EMD is improved to a certain extent. The effectiveness of the proposed SIPn-EMD is validated by the decomposition of synthetic and experimental bearing vibration signals.
Źródło:
Archives of Acoustics; 2023, 48, 3; 389-401
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wibroakustyczna weryfikacja stanu technicznego łożysk tocznych
Vibroacoustic verification of the technical state of rolling bearings
Autorzy:
Peruń, G.
Hornik, A.
Powiązania:
https://bibliotekanauki.pl/articles/312155.pdf
Data publikacji:
2016
Wydawca:
Instytut Naukowo-Wydawniczy "SPATIUM"
Tematy:
łożyska toczne
metoda wibroakustyczna
diagnostyka uszkodzeń łożysk tocznych
roller bearings
vibroacustics method
rolling bearing fault diagnosis
Opis:
W artykule omówiony został problem oceny stanu technicznego łożysk tocznych za pomocą metod wykorzystujących pomiary drgań i hałasu. Łożyska toczne są generatorem drgań, co wynika m.in. ze zmiennej ich sztywności. Procesy resztkowe wywołane z tego powodu mają jednak zdecydowanie mniejszy poziom od tych, których źródłem są uszkodzenia elementów łożyska i z tego powodu nie będą one w artykule analizowane. Wykorzystanie metod wibroakustycznych wraz z odpowiednimi metodami przetwarzania sygnałów często pozwala na poprawną ocenę stanu technicznego łożysk podczas ich pracy. Stanowi to ogromną zaletę takiej metody diagnozowania.
In the article was discussed a problem of the technical state assessment of rolling bearings with results of vibration and noise measurements. Bearings are vibration generators, which results, among others the variable stiffness. The use of vibro-acoustic methods with appropriate signal processing methods often allows a correct assessment of the technical condition of the bearing during operation. This is a great advantage of this method of diagnosis.
Źródło:
Autobusy : technika, eksploatacja, systemy transportowe; 2016, 17, 12; 1280-1283
1509-5878
2450-7725
Pojawia się w:
Autobusy : technika, eksploatacja, systemy transportowe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of time-frequency distributions in diagnostic signal processing problems: a case study
Zastosowanie dystrybucji czasowo-częstotliwościowych w diagnostycznym przetwarzaniu sygnałów: studium przypadku
Autorzy:
Katunin, A.
Powiązania:
https://bibliotekanauki.pl/articles/329456.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
time-frequency distributions
signal processing
multicomponent identification
bearing fault
diagnosis
dystrybucja czasowo-częstotliwościowa
przetwarzanie sygnałów
identyfikacja wieloskładowa
diagnostyka
łożysko toczne
Opis:
In this paper, the author analyzed an applicability of selected types of time-frequency distributions that belong to Cohen’s class and their reassignments for signals similar to those obtained during machinery diagnostics. At the first step of performed studies a synthetic multicomponent signal that contains both stationary and non-stationary components was analyzed using algorithms based on various time-frequency distributions. This allows for evaluating effectiveness of identification of particular components by applied time-frequency distributions and selecting a group of the most effective algorithms. At the second step, the selected time-frequency distributions were applied for analysis of signals acquired during diagnosis of rolling bearings in order to verify the effectiveness of identification of components responsible for a priori known faults occurred in bearings.
W niniejszym artykule autor analizuje stosowalność wybranych typów dystrybucji czasowoczęstotliwościowych, które należą do klasy Cohena i ich wersji redefiniowanych dla sygnałów zbliżonych do takich, które są otrzymywane podczas diagnostyki maszyn. W pierwszym kroku przeprowadzonych badań syntetyczny wieloskładowy sygnał, zawierający zarówno stacjonarne jak i niestacjonarne składowe, był analizowany z wykorzystaniem algorytmów opartych na różnych dystrybucjach czasowoczęstotliwościowych. Pozwoliło to na ocenę efektywności identyfikacji poszczególnych składowych przez zastosowane dystrybucje czasowo-częstotliwościowe oraz wybór grupy najefektywniejszych algorytmów. W drugim kroku wybrane dystrybucje czasowo-częstotliwościowe zostały zastosowane do analizy sygnałów pozyskanych podczas diagnostyki łożysk tocznych w celu weryfikacji efektywności identyfikacji składowych odpowiedzialnych za wystąpienie uszkodzeń w łożyskach, znanych a priori.
Źródło:
Diagnostyka; 2016, 17, 2; 95-103
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault diagnosis of bearings based on SSWT, bayes optimisation and CNN
Autorzy:
Yan, Guohua
Hu, Yihuai
Shi, Qingguo
Powiązania:
https://bibliotekanauki.pl/articles/34610052.pdf
Data publikacji:
2023
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
fault diagnosis
bearing
PMSM
bayesian optimisation
CNN
Opis:
Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.
Źródło:
Polish Maritime Research; 2023, 3; 132-141
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Condition monitoring of induction motor bearing based on bearing damage index
Autorzy:
Patel, R. K.
Giri, V. K.
Powiązania:
https://bibliotekanauki.pl/articles/140535.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
bearing damage index (BDI)
inner raceway fault (IRF)
outer raceway fault (ORF)
fault severity
vibration signal
Opis:
The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become “transient” in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an ‘index’ representing the health condition of rotating machines.
Źródło:
Archives of Electrical Engineering; 2017, 66, 1; 105-119
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bearing fault detection and diagnosis based on densely connected convolutional networks
Autorzy:
Niyongabo, Julius
Zhang, Yingjie
Ndikumagenge, Jérémie
Powiązania:
https://bibliotekanauki.pl/articles/2105995.pdf
Data publikacji:
2022
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
bearing
deep learning
machine learning
transfer learning
fault detection
fault diagnosis
CWRU dataset
Opis:
Rotating machines are widely used in today’s world. As these machines perform the biggest tasks in industries, faults are naturally observed on their components. For most rotating machines such as wind turbine, bearing is one of critical components. To reduce failure rate and increase working life of rotating machinery it is important to detect and diagnose early faults in this most vulner-able part. In the recent past, technologies based on computational intelligence, including machine learning (ML) and deep learning (DL), have been efficiently used for detection and diagnosis of bearing faults. However, DL algorithms are being increasingly favoured day by day because of their advantages of automatically extracting features from training data. Despite this, in DL, adding neural layers reduces the training accuracy and the vanishing gradient problem arises. DL algorithms based on convolutional neural networks (CNN) such as DenseNet have proved to be quite efficient in solving this kind of problem. In this paper, a transfer learning consisting of fine-tuning DenseNet-121 top layers is proposed to make this classifier more robust and efficient. Then, a new intelligent model inspired by DenseNet-121 is designed and used for detecting and diagnosing bearing faults. Continuous wavelet transform is applied to enhance the dataset. Experimental results obtained from analyses employing the Case Western Reserve University (CWRU) bearing dataset show that the proposed model has higher diagnostic performance, with 98% average accuracy and less complexity.
Źródło:
Acta Mechanica et Automatica; 2022, 16, 2; 130--135
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Journal Bearing Fault Detection Based on Daubechies Wavelet
Autorzy:
Narendiranath, B. T.
Himamshu, H. S.
Prabin, K. N.
Rama, P. D.
Nishant, C.
Powiązania:
https://bibliotekanauki.pl/articles/176955.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
journal bearing
fault diagnosis
Debauchies wavelet
artificial neural network
Opis:
Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for automated bearing fault detection.
Źródło:
Archives of Acoustics; 2017, 42, 3; 401-414
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of advanced signal-processing methods for roller bearing faults detection
Autorzy:
Urbanek, J.
Barszcz, T.
Uhl, T.
Powiązania:
https://bibliotekanauki.pl/articles/221168.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fault detection
condition monitoring
rolling element bearing
wind turbine
Opis:
Wind turbines are nowadays one of the most promising energy sources. Every year, the amount of energy produced from the wind grows steadily. Investors demand turbine manufacturers to produce bigger, more efficient and robust units. These requirements resulted in fast development of condition-monitoring methods. However, significant sizes and varying operational conditions can make diagnostics of the wind turbines very challenging. The paper shows the case study of a wind turbine that had suffered a serious rolling element bearing (REB) fault. The authors compare several methods for early detection of symptoms of the failure. The paper compares standard methods based on spectral analysis and a number of novel methods based on narrowband envelope analysis, kurtosis and cyclostationarity approach. The very important problem of proper configuration of the methods is addressed as well. It is well known that every method requires setting of several parameters. In the industrial practice, configuration should be as standard and simple as possible. The paper discusses configuration parameters of investigated methods and their sensitivity to configuration uncertainties.
Źródło:
Metrology and Measurement Systems; 2012, 19, 4; 715-726
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic Fault Classification for Journal Bearings Using ANN and DNN
Autorzy:
Narendiranath Babu, T.
Aravind, A.
Rakesh, A.
Jahzan, M.
Rama Prabha, D.
Ramalinga Viswanathan, M.
Powiązania:
https://bibliotekanauki.pl/articles/177579.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
journal bearing
fault classification
artificial neural networks
deep neural networks
Opis:
Journal bearings are the most common type of bearings in which a shaft freely rotates in a metallic sleeve. They find a lot of applications in industry, especially where extremely high loads are involved. Proper analysis of the various bearing faults and predicting the modes of failure beforehand are Essentials to increase the working life of the bearing. In the current study, the vibration data of a journal Bering in the healthy condition and in five different fault conditions are collected. A feature extraction metod is employed to classify the different fault conditions. Automatic fault classification is performed using artificial neural networks (ANN). As the probability of a correct prediction goes down for a higher number of faults in ANN, the method is made more robust by incorporating deep neural networks (DNN) with the help of autoencoders. Training was done using the scaled conjugate gradient algorithm and the performance was calculated by the cross entropy method. Due to the increased number of hidden layers in DNN, it is possible to achieve a high efficiency of 100% with the feature extraction method.
Źródło:
Archives of Acoustics; 2018, 43, 4; 727-738
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Rolling Bearing Fault Diagnosis Method Based on Improved Variational Mode Decomposition and Information Entropy
Autorzy:
Ge, Liang
Fan, Wen
Xiao, Xiaoting
Gan, Fangji
Lai, Xin
Deng, Hongxia
Huang, Qi
Powiązania:
https://bibliotekanauki.pl/articles/38883308.pdf
Data publikacji:
2022
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
rolling bearing
gray wolf optimization
fault diagnosis
variable mode decomposition
Opis:
Due to the complex randomness and nonlinearity of rolling bearing vibration signal, it is challenging to extract fault features effectively. By analyzing the vibration mechanism of rolling bearing, it is found that the vibration signal of local damage defects of rolling bearing has the characteristics of periodic impact and amplitude modulation. The variational mode decomposition (VMD) algorithm has a good advantage in dealing with nonlinear and nonstationary signals and decomposing a signal into different modes. However, VMD has the problem of parameter selection, which directly affects the performance of VMD processing, and causes mode aliasing. Therefore, a rolling bearing fault diagnosis method based on improved VMD is proposed. A new fitness function combining differential evolution (DE) algorithm with gray wolf optimization (GWO) algorithm is proposed to form a new hybrid optimization algorithm, named DEGWO. The simulation results show that the improved VMD method based on DEGWO can adaptively remove the noise according to the characteristics of the signal and restore the original characteristics of the vibration signal. Finally, in order to verify the advantages of the research, the information entropy is extracted from the data of 1000 samples in the bearing database of Case Western Reserve University as the feature set, which is input into support vector machine (SVM) for fault diagnosis test. The results show that the diagnostic accuracy of this method is 96.5%, which effectively improved the accuracy of rolling bearing fault diagnosis.
Źródło:
Engineering Transactions; 2022, 70, 1; 23-51
0867-888X
Pojawia się w:
Engineering Transactions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism
Autorzy:
Li, Xueyi
Su, Kaiyu
He, Qiushi
Wang, Xiangkai
Xie, Zhijie
Powiązania:
https://bibliotekanauki.pl/articles/24200832.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
fault diagnosis
Bi-LSTM
attention
highway
deep learning
Ball Bearing
Opis:
Deep groove ball bearings are widely used in rotary machinery. Accurate for bearing faults diagnosis is essential for equipment maintenance. For common depth learning methods, the feature extraction of inverse time domain signal direction and the attention to key features are usually ignored. Based on the long short term memory(LSTM) network, this study proposes an attention-based highway bidirectional long short term memory (AHBi-LSTM) network for fault diagnosis based on the raw vibration signal. By increasing the Attention mechanism and Highway, the ability of the network to extract features is increased. The bidirectional LSTM network simultaneously extracts the raw vibration signal in positive and inverse time-domains to better extract the fault features. Six deep groove ball bearings with different health conditions were used to validate the AHBi-LSTM method in an experiment. The results showed that the accuracy of the proposed method for bearing fault diagnosis was over 98%, which was 8.66% higher than that of the LSTM model. The AHBi-LSTM model is also better than other relevant models for bearing fault diagnosis.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 162937
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault Detection Enhancement in Rolling Element Bearings Using the Minimum Entropy Deconvolution
Autorzy:
Barszcz, T.
Sawalhi, N.
Powiązania:
https://bibliotekanauki.pl/articles/177746.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
rolling bearing
fault detection
Minimum Entropy Deconvolution (MED)
wind turbine
Opis:
Minimum Entropy Deconvolution (MED) has been recently introduced to the machine condition mon- itoring field to enhance fault detection in rolling element bearings and gears. MED proved to be an excellent aid to the extraction of these impulses and diagnosing their origin, i.e. the defective component of the bearing. In this paper, MED is revisited and re-introduced with further insights into its application to fault detection and diagnosis in rolling element bearings. The MED parameter selection as well as its combination with pre-whitening is discussed. Two main cases are presented to illustrate the benefits of the MED technique. The first one was taken from a fan bladed test rig. The second case was taken from a wind turbine with an inner race fault. The usage of the MED technique has shown a strong enhancement for both fault detection and diagnosis. The paper contributes to the knowledge of fault detection of rolling element bearings through providing an insight into the usage of MED in rolling element bearings diag- nostic. This provides a guide for the user to select optimum parameters for the MED filter and illustrates these on new interesting cases both from a lab environment and an actual case.
Źródło:
Archives of Acoustics; 2012, 37, 2; 131-141
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel, machine learning-based feature extraction method for detecting and localizing bearing component defects
Autorzy:
Cherif, Bilal Djamal Eddine
Seninete, Sara
Defdaf, Mabrouk
Powiązania:
https://bibliotekanauki.pl/articles/2106416.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
induction motor
bearing
fault
outer race
inner race
ENV
RF
Opis:
Vibration analysis for conditional preventive maintenance is an essential tool for the industry. The vibration signals sensored, collected and analyzed can provide information about the state of an induction motor. Appropriate processing of these vibratory signals leads to define a normal or abnormal state of the whole rotating machinery, or in particular, one of its components. The main objective of this paper is to propose a method for automatic monitoring of bearing components condition of an induction motor. The proposed method is based on two approaches with one based on signal processing using the Hilbert spectral envelope and the other approach uses machine learning based on random forests. The Hilbert spectral envelope allows the extraction of frequency characteristics that are considered as new features entering the classifier. The frequencies chosen as features are determined from a proportional variation of their amplitudes with the variation of the load torque and the fault diameter. Furthermore, a random forest-based classifier can validate the effectiveness of extracted frequency characteristics as novel features to deal with bearing fault detection while automatically locating the faulty component with a classification rate of 99.94%. The results obtained with the proposed method have been validated experimentally using a test rig.
Źródło:
Metrology and Measurement Systems; 2022, 29, 2; 333--346
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł

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