Vibration fault diagnosis through genetic matching pursuit optimization

Ștefănoiu, Dan; Culita, Janetta; Ionescu, Felicia

doi:10.1007/s00500-018-3450-0

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Rauber_2021 [219] Samuel_2005 * [33] Vives_2020 [216] Random Forest Rauber_2021 [219] Li_2016b [249] Fuzzy predictive model Hadroug_2021 [250] Malla_2019 * [3] Str ączkiewicz_2015 [251] Saravanan_2009 [252] Da Silva_2017 [253] Sharma_2021 * [25] Decision Trees (DTs) Lipinski_2020 [254] Joshuva_2017a [112] Tabaszewski_2020 [255] Yang_2005 [256] Yang_2000 [257] Song_2018 [181] Dempster-Shafer (D-S) evidence theory Khazaee_2014 [258] Khazaee_2012 [259] Multi-Sensor Data fusion Safizadeh_2014 [260] Khazaee_2012 [259] Stief_2017 [261] Sharma_2021 * [25] Hybrid classifier based on SVM and ANN Sharma_2021 * [25] Hybrid classifier based on Principal Component Analysis (PCA) and ANN Liu_2008 [262] Devendiran_2015 [104] De Moura_2011 [263] Bendjama_2010 [264] Others Stefanoiu_2019 [265] Yan_2019 [199] Liu_2014 [266] Zhang_2021b [267] Avendaño-Valencia_2017 [268] Jayaswal and Wadhwani, in 2009 [31], reviewed the techniques successfully implemented for the automated fault diagnosis of bearings until that time, and refer to expert systems developed with multilayer perceptron (MLP), radial basis function (RBF) and probabilistic neural network (PNN). More recently, Tao et al, in 2019 [222], adopted a multilayer gated recurrent unit (MGRU) method for gear fault diagnosis; a comparison with long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM) LSTM, MLSTM, GRU, and SVM models, based on an experimental analysis, revealed improved accuracy with the MGRU network.…”

Section: Multiscale Convoluted Neural Network (Mscnn)mentioning

confidence: 99%

“…With reference to the problem of labels, Zhang et al in [267] propose a method that combines self-supervised learning with supervised learning, making full use of unlabeled data to learn fault features, and transforms the data into three-channel vibration images. Stefanoiu et al,in [265], present a method based on the matching pursuit algorithm (MPA), a new finding in signal processing, which proves the possibility of performing fault diagnosis of bearings, even in case of multiple defects. Yan et al in [199] show the results of the application of the AdaBoost algorithm (which belongs to fusion algorithms class) to diagnose faults of bearings.…”

Section: Multiscale Convoluted Neural Network (Mscnn)mentioning

confidence: 99%

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A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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Section: Multiscale Convoluted Neural Network (Mscnn)mentioning

confidence: 99%

Section: Multiscale Convoluted Neural Network (Mscnn)mentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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“…Commonly used methods of population selection are roulette wheel selection [18], stochastic universal sampling [19], and tournament selection method [20]. To ensure the population diversity in the search process, this paper adopts the tournament selection method for population selection operations.…”

Section: The Population Selection Operationmentioning

confidence: 99%

A QoS-driven Service Selection Optimization Algorithm for Internet of Things

Zhang

Geng

et al. 2020

Preprint

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Currently, the smart devices have been widely deployed in Internet of Things (IoT). With the scale of IoT continues to increase, it brings big challenges for service composition in a large-scale IoT. For solving this problem, a QoS-driven service selection method based on the enhanced Genetic algorithm (EGS ~ QoS) is proposed in this paper. To decrease the scale of service composition, we use the lexicographic optimization approach and QoS constraint relaxation technique to find the candidate service with height QoS. Then, the IoT service composition problem is transformed into a single-objective optimization problem adopting a simple weighting method, and the final composite service meeting the user's QoS needs is found from the candidate service. Compared with the related algorithm, the simulation results show that EGS ~ QoS can efficiently and quickly select a composite service satisfying user's QoS needs, and is more suitable for solving the service composite problem in large-scale IoT services.

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“…Evolutionary pursuit is an algorithm built by Ferreira da Silva, 36,37 which combines the features of the matching pursuit (MP) algorithm (an extensive review of MP is done by Mallat and Zhang 38 ) and the capabilities of genetic algorithms (GAs; addressed by Gen and Cheng 39 ) by adapting dictionary atoms to achieve atomic decomposition. Genetic MP (GMP) was the name assigned by Stefanoiu and Lonescu 40 and Stefanoiu et al 41 to refer to similar algorithmic strategies, but because we use little know atoms, we call GMP-Hermite atoms (GMP-HAs) our proposed algorithm. A similar algorithm, using Gabor atoms, 42 was programmed to achieve a sparser decomposition of bearing vibrational signals.…”

Section: Introductionmentioning

confidence: 99%

Matching Pursuit for Denoising Raman Spectra, Based on Genetic Algorithm and Hermite Atoms

Vazquez-Osorio,

Castro-Ramos,

Sánchez-Escobar

2023

Appl Spectrosc

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Due to its various advantages, Raman spectroscopy has become a powerful tool in different fields of science and engineering; however, in specific applications, this technique's limiting factor is closely related to the inherent noise of the Raman spectra. To eliminate the noise of a Raman spectrum, preserving its position, intensity, and width characteristic, we propose using a genetic matching pursuit-Hermite atoms (GMP-HAs) algorithm in this work. This algorithm helps recover Raman spectra immersed in Gaussian noise with the least number of atoms. The noise-free Raman signal is reconstructed with the GMP-HAs algorithm, transforming the typical best-matching atom search into an optimization problem. Specifically, we maximize the fitness function, defined as the correlation between current residual and Hermite atoms, with the genetic algorithm MI-LXPM encoded in a real domain and avoiding local maxima, by adding a stopping criterion based on an exponential adjustment according to the algorithm's behavior in the presence of noise. Simulated and biological Raman spectra are used to evaluate the proposed algorithm and compare its performance with typically known methods for denoising, such as the Savitzky– Golay filter (SG) and basis pursuit denoising. Using the signal-to-noise ratio (S/N)metric resulted in a 0.31 dB advantage in the S/N product for the proposed algorithm with respect to SG. Additionally, it is shown that the algorithm uses only 25.3% of the number of atoms needed by the matching pursuit algorithm. The results indicate that the GMP-HAs algorithm has better denoising capabilities, and at the same time, the Raman spectra are decomposed with fewer atoms compared to known sparse algorithms.

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Vibration fault diagnosis through genetic matching pursuit optimization

Cited by 5 publications

References 21 publications

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A QoS-driven Service Selection Optimization Algorithm for Internet of Things

Matching Pursuit for Denoising Raman Spectra, Based on Genetic Algorithm and Hermite Atoms

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