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    Wyświetlanie 1-13 z 13
    Tytuł:
    Novel hybrid algorithm using convolutional autoencoder with SVM for electrical impedance tomography and ultrasound computed tomography
    Nowy algorytm hybrydowy wykorzystujący autoenkoder konwolucyjny z SVM dla elektrycznej tomografii impedancyjnej i tomografii ultradźwiękowej
    Autorzy:
    Maciura, Łukasz
    Wójcik, Dariusz
    Rymarczyk, Tomasz
    Król, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/27315418.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    convolutional autoencoder
    SVM
    electrical impedance tomography
    ultrasound transmission tomography
    autoencoder konwolucyjny
    elektryczna tomografia impedancyjna
    ultradźwiękowa tomografia transmisyjna
    Opis:
    This paper presents a new hybrid algorithm using multiple support vector machines models with a convolutional autoencoder forelectrical impedance tomography, and ultrasound computed tomography image reconstruction. The ultimate hybrid solution uses multiple SVM models to convert input measurements to individual autoencoder codes representing a given scene then the decoder part of the autoencoder can reconstruct the scene.
    Artykuł przedstawia nowy hybrydowy algorytm który używa modeli maszyn wektorów nośnych wraz z autoenkoderem konwolucyjnym do rekonstrukcji obrazu z elektrycznej tomografii impedancyjnej oraz ultrasonograficznej tomografii transmisyjnej. Ostateczne rozwiązanie hybrydowe używa wielu modeli SVM do konwersji pomiarów wejściowych do pojedynczych kodów autoenkodera reprezentujących daną scenę a wtedy dekoder wyciętyz autoenkodera może zrekonstruować daną scenę.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2023, 13, 2; 4--9
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic
    Autorzy:
    Gabryel, Marcin
    Grzanek, Konrad
    Hayashi, Yoichi
    Powiązania:
    https://bibliotekanauki.pl/articles/1837413.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    browser fingerprint
    device fingerprint
    LSH algorithm
    autoencoder
    Opis:
    Web-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 243-253
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Detecting anomalies in advertising web traffic with the use of the variational autoencoder
    Autorzy:
    Gabryel, Marcin
    Lada, Dawid
    Filutowicz, Zbigniew
    Patora-Wysocka, Zofia
    Kisiel-Dorohinicki, Marek
    Chen, Guang Yi
    Powiązania:
    https://bibliotekanauki.pl/articles/2147149.pdf
    Data publikacji:
    2022
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    anomaly detection
    web traffic
    ad fraud
    variational autoencoder
    Opis:
    This paper presents a neural network model for identifying non-human traffic to a website, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 255--266
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Method of machining centre sliding system fault detection using torque signals and autoencoder
    Autorzy:
    Augustyn, Damian
    Fidali, Marek
    Powiązania:
    https://bibliotekanauki.pl/articles/2233649.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    condition monitoring
    torque signal
    machining centre
    anomaly detection
    autoencoder
    Opis:
    The sliding system of machining centres often causes maintenance and process problems. Improper operation of the sliding system can result from wear of mechanical parts and drives faults. To detect the faulty operation of the sliding system, measurements of the torque of its servomotors can be used. Servomotor controllers can measure motor current, which can be used to calculate motor torque. For research purposes, the authors used a set of torque signals from the machining centre servomotors that were acquired over a long period. The signals were collected during a diagnostic test programmed in the machining centre controller and performed once per day. In this article, a method for detecting anomalies in torque signals was presented for the condition assessment of the machining centre sliding systems. During the research, an autoencoder was used to detect the anomaly, and the condition was assessed based on the value of the reconstruction error. The results indicate that the anomaly detection method using an autoencoder is an effective solution for detecting damage to the sliding system and can be easily used in a condition monitoring system.
    Źródło:
    Acta Mechanica et Automatica; 2023, 17, 3; 445--451
    1898-4088
    2300-5319
    Pojawia się w:
    Acta Mechanica et Automatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Roller damage detection method based on the measurement of transverse vibrations of the conveyor belt
    Autorzy:
    Bortnowski, Piotr
    Król, Robert
    Ozdoba, Maksymilian
    Powiązania:
    https://bibliotekanauki.pl/articles/2172035.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    belt conveyor
    conveyor belt
    belt vibrations
    diagnostics
    roller
    idler
    LSTM
    autoencoder
    Opis:
    The article presents the detection of damage to rollers based on the transverse vibration signal measured on the conveyor belt. A solution was proposed for a wireless measuring device that moves with the conveyor belt along of the route, which records the signal of transverse vibrations of the belt. In the first place, the research was conducted in laboratory conditions, where a roller with prepared damage was used. Subsequently, the process of validating the adopted test procedure under real conditions was performed. The approach allowed to verify the correctness of the adopted technical assumptions of the measuring device and to assess the reliability of the acquired test results. In addition, an LSTM neural network algorithm was proposed to automate the process of detecting anomalies of the recorded diagnostic signal based on designated time series. The adopted detection algorithm has proven itself in both laboratory and in-situ tests.
    Źródło:
    Eksploatacja i Niezawodność; 2022, 24, 3; 510--521
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection
    Autorzy:
    Brunner, Csaba
    Kő, Andrea
    Fodor, Szabina
    Powiązania:
    https://bibliotekanauki.pl/articles/2147134.pdf
    Data publikacji:
    2022
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    intrusion detection
    neural network
    ensemble classifiers
    hyperparameter optimization
    sparse autoencoder
    NSL-KDD
    machine learning
    Opis:
    Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoencoder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 149--163
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel method of health indicator construction and remaining useful life prediction based on deep learning
    Autorzy:
    Zhan, Xianbiao
    Liu, Zixuan
    Yan, Hao
    Wu, Zhenghao
    Guo, Chiming
    Jia, Xisheng
    Powiązania:
    https://bibliotekanauki.pl/articles/27312791.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    stacked sparse autoencoder
    health indicator
    long short-term memory network
    remaining useful life prediction
    Opis:
    The construction of health indicators (HI) for traditional deep learning requires human training labels and poor interpretability. This paper proposes an HI construction method based on Stacked Sparse Autoencoder (SSAE) and combines SSAE with Long short-term memory (LSTM) network to predict the remaining useful life (RUL). Extracting features from a single domain may result in insufficient feature extraction and cannot comprehensively reflect the degradation status information of mechanical equipment. In order to solve the problem, this article extracts features from time domain, frequency domain, and time-frequency domain to construct a comprehensive original feature set. Based on monotonicity, trendiness, and robustness, the most sensitive features from the original feature set are selected and put into the SSAE network to construct HI for state partitioning, and then LSTM is used for RUL prediction. By comparing with the existing methods, it is proved that the prediction effect of the proposed method in this paper is satisfied.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 171374
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Generative modelling of vibration signals in machine maintenance
    Autorzy:
    Puchalski, Andrzej Adam
    Komorska, Iwona
    Powiązania:
    https://bibliotekanauki.pl/articles/28086927.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    time-frequency analysis
    condition monitoring
    anomalies detection
    deep generative models
    variational autoencoder
    data distribution
    Opis:
    The exponential development of technologies for the acquisition, collection, and processing of data from real-world objects is creating new perspectives in the field of machine maintenance. The Industrial Internet of Things is the source of a huge collection of measurement data. The performance of classification or regression algorithms needs to take into account the random nature of the process being modelled and any incomplete observability, especially in terms of failure states. The article highlights the practical possibilities of using generative artificial intelligence and deep machine learning systems to create synthetic measurement observations in monitoring the vibrations of rotating machinery to improve unbalanced databases. Variational AutoencoderVAE generative models with latent variables in the form of high-level input features of time-frequency spectra were studied. The mapping and generation algorithm was optimised and its effectiveness was tested in the practical solution of the task of diagnosing the three operating states of a demonstration gearbox.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 173488
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes
    Autorzy:
    Huang, Lei
    Ren, Hao
    Chai, Yi
    Qu, Jianfeng
    Powiązania:
    https://bibliotekanauki.pl/articles/1838177.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    fault detection
    sparse autoencoder
    sparse restricted Boltzmann machine
    hybrid industrial processes
    detekcja błędów
    autoenkoder
    proces przemysłowy
    Opis:
    This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid industrial processes. The method is mainly built on the basis of a sparse auto-encoder and a sparse restricted Boltzmann machine (SAE-SRBM), so as to take advantages of their adaptive extraction and fusion on strong nonlinear symptoms. In the present work, SAEs are employed to reconstruct inputs and accomplish feature extraction by unsupervised mode, and their outputs present a knotty problem of an unknown probability distribution. In order to solve it, SRBMs are naturally used to fuse these unknown probability distribution features by transforming them into energy characteristics. The contribution of this method is the capability of further mining and learning of nonlinear features without considering the nonstationary problem. Also, this paper introduces a method of constructing labeled and unlabeled training samples while maintaining time series features. Unlabeled samples can be adopted to train the part for feature extraction and fusion, while labeled samples can be used to train the classification part. Finally, a simulation on the Tennessee Eastman process is carried out to demonstrate the effectiveness and excellent performance on fault detection for nonstationary and stationary hybrid industrial processes.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 29-43
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes
    Autorzy:
    Huang, Lei
    Ren, Hao
    Chai, Yi
    Qu, Jianfeng
    Powiązania:
    https://bibliotekanauki.pl/articles/1838184.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    fault detection
    sparse autoencoder
    sparse restricted Boltzmann machine
    hybrid industrial processes
    detekcja błędu
    autoenkoder
    proces przemysłowy
    Opis:
    This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid industrial processes. The method is mainly built on the basis of a sparse auto-encoder and a sparse restricted Boltzmann machine (SAE-SRBM), so as to take advantages of their adaptive extraction and fusion on strong nonlinear symptoms. In the present work, SAEs are employed to reconstruct inputs and accomplish feature extraction by unsupervised mode, and their outputs present a knotty problem of an unknown probability distribution. In order to solve it, SRBMs are naturally used to fuse these unknown probability distribution features by transforming them into energy characteristics. The contribution of this method is the capability of further mining and learning of nonlinear features without considering the nonstationary problem. Also, this paper introduces a method of constructing labeled and unlabeled training samples while maintaining time series features. Unlabeled samples can be adopted to train the part for feature extraction and fusion, while labeled samples can be used to train the classification part. Finally, a simulation on the Tennessee Eastman process is carried out to demonstrate the effectiveness and excellent performance on fault detection for nonstationary and stationary hybrid industrial processes.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 29-43
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Proces rekonstrukcji obrazu tomograficznego w oparciu o sieć Variational Autoencoder
    The process of reconstruction in a CT image based on an variational autoencoder network
    Autorzy:
    Podolszańska, Jolanta
    Powiązania:
    https://bibliotekanauki.pl/articles/2146807.pdf
    Data publikacji:
    2021
    Wydawca:
    Indygo Zahir Media
    Tematy:
    tomografia komputerowa
    obrazowanie medyczne
    autokoder wariacyjny
    przetwarzanie obrazu
    computed tomography
    medical imaging
    variational autoencoder
    VAE
    image processing
    Opis:
    Artykuł ma na celu zapoznanie się z rekonstrukcją i odszumianiem obrazu za pomocą sieci neuronowej typu VAE (Variational Auto-Encoder). W pracy zostanie dokonana analiza porównawcza pod kątem błędów rekonstrukcji i występujących na obrazie anomalii. Posłużono się zbiorem obrazów TK mózgu (Visible Female CT), aby pokazać, jak wygląda rekonstrukcja i odszumianie metodą Variational Autoencoder.
    This paper aims to learn about image reconstruction and de-noising using Variational Encoder (VAE) neural network. The paper will make a comparative analysis in terms of reconstruction errors and anomalies present in the image. A collection of brain CT images (Visible Female CT) is used to show how reconstruction and de-noising by Variational Autoencoder method.
    Źródło:
    Inżynier i Fizyk Medyczny; 2021, 10, 1; 61--64
    2300-1410
    Pojawia się w:
    Inżynier i Fizyk Medyczny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A nested autoencoder approach to automated defect inspection on textured surfaces
    Autorzy:
    Oz, Muhammed Ali Nur
    Kaymakci, Ozgur Turay
    Mercimek, Muharrem
    Powiązania:
    https://bibliotekanauki.pl/articles/2055170.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    autoencoder
    defect detection
    automatic visual inspection
    deep learning
    autoenkoder
    wykrywanie defektów
    inspekcja wizyjna
    inspekcja automatyczna
    uczenie głębokie
    Opis:
    In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 515--523
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning
    Diagnostyka pittingu kół zębatych na podstawie surowego sygnału emisji akustycznej w oparciu o głębokie uczenie maszynowe
    Autorzy:
    Li, Xueyi
    Li, Jialin
    He, David
    Qu, Yongzhi
    Powiązania:
    https://bibliotekanauki.pl/articles/301093.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    gear pitting fault diagnosis
    autoencoder
    one-dimensional convolutional neural network
    acoustic emission signal
    diagnostyka pittingu kół zębatych
    autoenkoder
    jednowymiarowa splotowa sieć neuronowa
    sygnał emisji akustycznej
    Opis:
    Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals. To overcome the problem of low gear pitting fault recognition rate using AE signals and convolutional neural networks, this paper proposes a new method named augmented convolution sparse autoencoder (ACSAE) for gear pitting fault diagnosis using raw AE signals. First, the proposed method combines sparse autoencoder and one-dimensional convolutional neural networks for unsupervised learning and then uses the reinforcement theory to enhance the adaptability and robustness of the network. The ACSAE method can automatically extract fault features directly from the original AE signals without time and frequency domain conversion of the AE signals. AE signals collected from gear test experiments are used to validate the ACSAE method. The analysis result of the gear pitting fault test shows that the proposed method can effectively performing recognition of the gear pitting faults, and the recognition rate reaches above 98%. The comparative analysis shows that in comparison with fully-connected neural networks, convolutional neural networks, and recurrent neural networks, the ACSAE method has achieved a better diagnostic accuracy for gear fitting faults.
    Pitting kół zębatych stanowi jedno z najczęstszych uszkodzeń przekładni mechanicznych. Do wykrywania takich uszkodzeń stosuje się sygnały emisji akustycznej (AE), które, ze względu na niższą wrażliwość na hałas otoczenia, stanowią skuteczniejsze narzędzie diagnostyczne niż tradycyjne sygnały wibracyjne. Wykrywalność zużycia guzełkowatego (pittingu) kół zębatych przy użyciu sygnałów AE i splotowych sieci neuronowych jest jednak niska. Aby rozwiązać ten problem, w niniejszym artykule zaproponowano nową metodę diagnozowania uszkodzeń kół zębatych za pomocą surowych sygnałów AE, którą nazwano augmented convolution sparse autoencoder (konwolucją rozszerzoną z wykorzystaniem autoenkodera rzadkiego, ACSAE). Jest to metoda samouczenia jednowymiarowych splotowych sieci neuronowych realizowanego za pomocą autoenkodera rzadkiego. Metoda ta wykorzystuje teorię wzmocnienia do zwiększania adaptacyjności i odporności sieci. Metoda ACSAE pozwala na automatyczne wyodrębnianie cech degradacji bezpośrednio z oryginalnych sygnałów AE bez konieczności ich konwersji do domeny czasu i częstotliwości. Walidację metody przeprowadzono na podstawie sygnałów AE otrzymanych w badaniach kół zębatych. Analiza wyników badań pittingu kół zębatych wskazuje, że proponowana metoda pozwala na skuteczną detekcję tego typu uszkodzeń, przy wskaźniku wykrywalności powyżej 98%. Analiza porównawcza pokazuje, że metoda ACSAE cechuje się większą trafnością diagnostyczną w wykrywaniu błędów montażowych kół zębatych w porównaniu z sieciami neuronowymi w pełni połączonymi, splotowymi i rekurencyjnymi.
    Źródło:
    Eksploatacja i Niezawodność; 2019, 21, 3; 403-410
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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