Autor: Hu, Yan - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Acoustic Matching Characteristics of Annular Piezoelectric Ultrasonic Sensor
Autorzy:: Li, Haoran
Hu, Yan
Li, Laibo
Xu, Dongyu
Tematy:: piezoelectric ceramics
ultrasonic sensors
acoustic matching characteristics; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2141675.pdf Link otwiera się w nowym oknie
Opis:: Using intelligent materials and sensors to monitor the safety of concrete structures is a hot topic in the field of civil engineering. In order to realize the omni-directional monitoring of concrete structural damage, the authors of this paper designed and fabricated an embedded annular piezoelectric ultrasonic sensor using the annular piezoelectric lead zirconate titanate (PZT) ceramic as a sensing element and epoxy resin as the matching and the backing layers. The influence of different matching and backing layers thickness on the acoustic characteristic parameters of the sensor were studied. The results show that the resonant frequency corresponding to the axial mode of annular piezoelectric ceramics moves toward the high frequency direction with the decrease of the height of piezoelectric ceramics, and the radial vibration mode increases as well as the impedance peak. With the thickness of the backing layer increases from 1 mm to 2 mm, the radial resolution of the annular piezoelectric ultrasonic sensor is enhanced, the pulse width is reduced by 39% comparing with the sensors which backing layer is 1 mm, and the head wave amplitude and −3 dB bandwidth are increased by 61% and 66%, respectively. When the matching layer thickness is 3 mm, the sensor has the highest amplitude response of 269 mV and higher sensitivity.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Hierarchical multiscale fluctuation dispersion entropy for fuel injection system fault diagnosis
Autorzy:: Shi, Qingguo
Hu, Yihuai
Yan, Guohua
Tematy:: hierarchical multiscale fluctuation dispersion entropy
fuel injection system
support matrix machine
fault diagnosis; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/32915909.pdf Link otwiera się w nowym oknie
Opis:: Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite different and important. Fault diagnosis in the fuel injection system is crucial to ensure the power, economy and emission of ME diesel engines, so we introduce hierarchical multiscale fluctuation dispersion entropy (HMFDE) and a support matrix machine (SMM) to realise it. We also discuss the influence of parameter changes on the entropy calculation’s accuracy and efficiency. The system simulation model is established and verified by Amesim software, and then HMFDE is used to extract a matrix from the features of a high pressure signal in a common rail pipe, under four working conditions. Compared with vectorised HMFDE, the accuracy of fault diagnosis using SMM is nearly 3% higher than that using a support vector machine (SVM). Experiments also show that the proposed method is more accurate and stable when compared with hierarchical multiscale dispersion entropy (HMDE), hierarchical dispersion entropy (HDE), multiscale fluctuation dispersion entropy (MFDE), multiscale dispersion entropy (MDE) and multiscale sample entropy (MSE). Therefore, the proposed method is more suitable for the modelling data. This research provides a new direction for matrix learning applications in fault diagnosis in marine two-stroke diesel engines.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Fault diagnosis of bearings based on SSWT, bayes optimisation and CNN
Autorzy:: Yan, Guohua
Hu, Yihuai
Shi, Qingguo
Tematy:: fault diagnosis
bearing
PMSM
bayesian optimisation
CNN; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/34610052.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Equivalent Model of the DC Resistance of Nonwoven-Based Embroidery Conductive Lines with Embroidery Parameters
Równoważny model rezystancji prądu stałego dla włóknin z liniami przewodzącymi uwzględniający parametry procesu haftowania
Autorzy:: Zhang, Yaya
Hu, Jiyong
Yan, Xiong
Tematy:: textiles
conductive line
embroidery parameter
direct current resistance
tekstylia
linia przewodząca
parametr haftu
rezystancja prądu stałego; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Powiązania:: https://bibliotekanauki.pl/articles/232238.pdf Link otwiera się w nowym oknie
Opis:: Although embroidering technology is generally used to manufacture electronic components, previous works only give the fitting relationship between embroidery parameters and their direct current (DC) resistance. However, to manufacture embroidered electronic components in scale, the relationship between their DC resistance and embroidery parameters must be known in the computer aided embroidery system. This study investigated the effect of embroidery parameters, including stitch spacing, stitch length and embroidery tension, on the DC resistance of embroidery conductive lines using a peripheral needle, and established their equivalent resistance model in terms of the properties of conductive yarns and embroidery parameters. To verify the model, conductive lines with different embroidery parameters were embroidered on polyester nonwoven, and their DC resistance were tested and fitted. The results show that DC resistance can be effectively controlled by adjusting embroidery parameters. The model proposed is verified and can be used to predict the DC resistance of conductive lines with predesigned parameters.
Technologia haftowania jest szeroko stosowana do produkcji elementów elektronicznych, jednakże dotychczas opublikowane prace opisywały jedynie związek pomiędzy parametrami haftu a ich rezystancją prądu stałego (DC). Jednak, aby wyprodukować haftowane elementy elektroniczne musi być znany związek między ich rezystancją prądu stałego a parametrami haftu. W pracy przedstawiono wyniki badań wpływu parametrów haftu, w tym odstępu ściegu, długości ściegu i napięcia haftu, na rezystancję linii przewodzących. Ustalono równoważny model rezystancji z uwzględnieniem właściwości przędz przewodzących i parametrów procesu haftowania. Aby zweryfikować model na włókninie poliestrowej wyhaftowano linie przewodzące stosując różne parametry haftu, a następnie zbadano ich rezystancję na prąd stały. Wyniki pokazały, że rezystancję prądu stałego można skutecznie kontrolować poprzez dostosowanie parametrów haftu. Zaproponowany model został zweryfikowany i może być wykorzystany do przewidywania rezystancji prądu stałego linii przewodzących o wstępnie zaprojektowanych parametrach.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: A convolutional neural network-based method of inverter fault diagnosis in a ship’s DC electrical system
Autorzy:: Yan, Guohua
Hu, Yihuai
Shi, Qingguo
Tematy:: multi-energy hybrid ships
inverters
fault diagnosis
CNN; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/32898224.pdf Link otwiera się w nowym oknie
Opis:: Multi-energy hybrid ships are compatible with multiple forms of new energy, and have become one of the most important directions for future developments in this field. A propulsion inverter is an important component of a hybrid DC electrical system, and its reliability has great significance in terms of safe navigation of the ship. A fault diagnosis method based on one-dimensional convolutional neural network (CNN) is proposed that considers the mutual influence between an inverter fault and a limited ship power grid. A tiled voltage reduction method is used for one-to-one correspondence between the inverter output voltage and switching combinations, followed by a combination of a global average pooling layer and a fully connected layer to reduce the model overfitting problem. Finally, fault diagnosis is verified by a Softmax layer with good anti-interference performance and accuracy.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: A novel fault diagnosis method for marine blower with vibration signals
Autorzy:: Yan, Guohua
Hu, Yihuai
Jiang, Jiawei
Tematy:: fault diagnosis
marine blower
EEMD
correlation coefficient
AR spectrum
BPNN; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/32899234.pdf Link otwiera się w nowym oknie
Opis:: The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Fault diagnosis of me marine diesel engine fuel injector with novel IRCMDE method
Autorzy:: Shi, Qingguo
Hu, Yihuai
Yan, Guohua
Tematy:: marine diesel engine
fuel injector
improved refined composite multi-scale dispersion entropy
fault diagnosis; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/34608122.pdf Link otwiera się w nowym oknie
Opis:: As an important component of the fuel injection system, the fuel injector is crucial for ensuring the power, economy, and emissions for a whole ME (machine electronically-controlled) marine diesel engine. However, injectors are most prone to failures such as reduced pressure at the opening valve, clogged spray holes and worn needle valves, because of the harsh working conditions. The failure characteristics are non-stationary and non-linear. Therefore, to efficiently extract fault features, an improved refined composite multi-scale dispersion entropy (IRCMDE) is proposed, which uses the energy distribution of sampling points as weights for coarse-grained calculation, then fast correlation-based filter (FCBF) and support vector machine (SVM) are used for feature selection and fault classification, respectively. The experimental results from a MAN B&W 6S35ME-B9 marine diesel engine show that the proposed algorithm can achieve 92.12% fault accuracy for injector faults, which is higher than multiscale dispersion entropy (MDE), refined composite multiscale dispersion entropy (RCMDE) and multiscale permutation entropy (MPE). Moreover, the experiment has also proved that, due to the double-walled structure of the high-pressure fuel pipe, the fuel injection pressure signal is more accurate than the vibration signal in reflecting the injector operating conditions.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Simultaneous removal of phenol and Cu(II) from wastewater by tallow dihydroxyethyl betaine modified bentonite
Autorzy:: Hu, Xiangyang
Wang, Bao
Yan, Gengsheng
Ge, Bizhou
Tematy:: simultaneous adsorption
tallow dihydroxyethyl betaine
bentonite
Cu(II)
phenol; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2203123.pdf Link otwiera się w nowym oknie
Opis:: An organobentonite modified with an amphoteric surfactant, tallow dihydroxyethyl betaine (TDHEB), was used as an adsorbent to simultaneously remove Cu(II) and phenol from wastewater. The characteristic of the organobentonite (named TDHEB-bentonite) was analyzed by X-ray diffraction, Fourier-transform infrared spectra and nitrogen adsorption-desorption isotherm. Batch tests were conducted to evaluate the adsorption capacities of TDHEB-bentonite for the two contaminants. Experiment results demonstrated that the adsorption of both contaminants is highly pH-dependent under acidic conditions. TDHEB-bentonite had about 2.0 and 5.0 times higher adsorption capacity toward Cu(II) and phenol, respectively, relative to the corresponding raw Na-bentonite. Adsorption isotherm data showed that the adsorption processes of both contaminants were well described by Freundlich model. Kinetic experiment demonstrated that both contaminants adsorption processes correlated well with pseudo-second-order model. Cu(II) had a negative impact on phenol adsorption, but not vice versa. Cu(II) was removed mainly through chelating with the organic groups (-CH2CH2OH and -COO-) of TDHEB. Otherwise, partition into the organic phase derived from the adsorbed surfactant was the primarily mechanism for phenol removal. Overall, TDHEB-bentonite was a promising adsorbent for removing Cu(II) and phenol simultaneously from wastewater.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Deep learning-based fault diagnosis for marine centrifugal fan
Autorzy:: Li, Congyue
Hu, Yihuai
Jiang, Jiawei
Yan, Guohua
Tematy:: CEEMDAN
fault diagnosis
lightweight neural network
marine centrifugal fan; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/32917700.pdf Link otwiera się w nowym oknie
Opis:: Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require much calculation and have low operating efficiency. Only shallow fault features can be extracted. As a result, the diagnosis accuracy is not high. It is difficult to realize the end-to-end fault diagnosis. Combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and lightweight neural network, a fault classification method is proposed. First, the CEEMDAN can decompose the vibration signal into several intrinsic modal functions (IMF). Then, the original signals can be transformed into 2-D images through pseudocolour coding of the IMFs. Finally, they are fed into the lightweight neural network for fault diagnosis. By embedding a convolutional block attention module (CBAM), the ability of the network to extract critical feature information is improved. The results show that the proposed method can adaptively extract the fault characteristics of a marine centrifugal fan. While the model is lightweight, the overall diagnostic accuracy can reach 99.3%. As exploratory basic research, this method can provide a reference for intelligent fault diagnosis systems on ships.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Design and mechanical analysis of a composite T-type connection structure for marine structures
Autorzy:: Li, Xiaowen
Zhu, Zhaoyi
Li, Yan
Hu, Zhe
Tematy:: T-type connection structure
composite structure design
test
mechanical analysis
marine structure; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/260625.pdf Link otwiera się w nowym oknie
Opis:: A new T-type connection structure consisting of composite sandwich plates, reinforced cores and adhesive was proposed for the construction of lightweight ships to resolve connection problems between bulkheads and decks of composite lightweight ship superstructures. Based on the design principles and mechanical properties of composite structures, the mechanical behaviour of the structure under a dangerous loading condition was investigated. In addition, the ultimate bearing capacities and damage modes were examined, the results of which demonstrated that the strength of the structure is weak, and that the adhesive and reinforced core between the face plate and the web plate is the primary weakness of the structure. A numerical simulation method was verified using the results of the mechanical tests, and five characteristic paths at the connection area were established. The stresses and displacements along the five paths were calculated using the numerical method. Then, variations in the geometric parameter and the strength and weight of the connection were summarised. The optimal angle of the adhesive bonding area is approximately 60°, which supports the optimal design and practical application of the lightweight ship adhesive-bonded connection structure.
Dostawca treści:: Biblioteka Nauki

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