Abstract: Disclosed are a high-voltage three-phase four-bridge-arm topology structure and an inverter. The topology structure includes four bridge-arm circuits and a converter; one end of first bridge-arm circuit is connected to an A-phase high voltage line and the other end is connected to a first conversion module, one end of second bridge-arm circuit to a B-phase high voltage line and the other end to a second conversion module; one end of third bridge-arm circuit to a C-phase high voltage line and the other end to a third conversion module; one end of fourth bridge arm circuit to a ground line and the other end to a fourth conversion module, and the fourth bridge arm circuit is used to perform voltage compensation on the output voltages of the first bridge arm circuit, second bridge arm circuit, and third bridge arm circuit when the three phase high voltage lines are unbalanced.

Abstract: Disclosed are a high-voltage three-phase four-bridge-arm topology structure and an inverter. The topology structure includes four bridge-arm circuits and a converter; one end of first bridge-arm circuit is connected to an A-phase high voltage line and the other end is connected to a first conversion module, one end of second bridge-arm circuit to a B-phase high voltage line and the other end to a second conversion module; one end of third bridge-arm circuit to a C-phase high voltage line and the other end to a third conversion module; one end of fourth bridge arm circuit to a ground line and the other end to a fourth conversion module, and the fourth bridge arm circuit is used to perform voltage compensation on the output voltages of the first bridge arm circuit, second bridge arm circuit, and third bridge arm circuit when the three phase high voltage lines are unbalanced.

Abstract: The present invention provides an unsupervised fault diagnosis method for mechanical equipment based on an adversarial flow model, main steps including: data preprocessing, converting a mechanical vibration signal into a frequency domain signal, and normalizing the amplitude value of the signal into a range of [0, 1]; prior distribution designing: designing a mixture of Gaussian distribution with K subdistributions, wherein K is determined by the number of mechanical equipment status; model construction: constructing an unsupervised fault diagnosis model by combining an autoencoder, a flow model, and a classifier; model training: training the unsupervised fault diagnosis model by using various classes of status data, along with the designed prior distribution, preset training steps, loss functions, and an optimization algorithm; and fault diagnosis: inputting status data of mechanical equipment into the trained unsupervised fault diagnosis model to obtain a data clustering result and a fault diagnosis result.

Abstract: The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the

Abstract: The present invention discloses a method for predicting bearing life based on a hidden Markov model (HMM) and transfer learning, including the following steps: (1) acquiring an original signal of full life of a rolling bearing; and extracting a feature set including a time domain feature, a time-frequency domain feature, and a trigonometric function feature; (2) inputting the feature set into an HMM to predict a hidden state, to obtain a failure occurrence time (FOT); (3) constructing a multilayer perceptron (MLP) model, obtaining a domain invariant feature and an optimal model parameter, and obtaining a neural network life prediction model; and (4) inputting the remaining target domain feature sets into the neural network life prediction model, and predicting the remaining life of the bearing. In the present invention, MLP-based transfer learning is used to resolve distribution differences in a source domain and a target domain caused by different operating conditions.

Abstract: The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.

Abstract: The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the

Abstract: The present invention discloses a fault diagnosis method under a convergence trend of a center frequency, including: (1) acquiring a dynamic signal x(t) of a rotary machine equipment; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) by using the variational model with the set initial decomposition parameters, and traversing a signal analysis band and performing iterative decomposition on the dynamic signal x(t) under the guidance of a convergence trend of a center frequency, to obtain optimized modals {m1 . . . mn . . . mN} and corresponding center frequencies {?1 . . . ?n . . . ?N}; (4) searching a fault related modal mI, guiding parameter optimization by using a center frequency ?I of the fault related modal mI, and retrieving an optimal target component mI including fault information; and (5) performing envelopment analysis on the optimal target component mI, and diagnosing the rotary machine equipment according to an envelope spectrum.

Abstract: The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.

Abstract: A molecular sieve has a silica/alumina molar ratio of 100-300, and has a mesopore structure. One closed hysteresis loop appears in the range of P/P0=0.4-0.99 in the low temperature nitrogen gas adsorption-desorption curve, and the starting location of the closed hysteresis loop is in the range of P/P0=0.4-0.7. The catalyst formed from the molecular sieve as a solid acid not only has a good capacity of isomerization to reduce the freezing point, but also can produce a high yield of the product with a lower pour point. The process for preparing the catalyst involves steps including crystallization, filtration, calcination, and hydrothermal treatment.

Abstract: The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.

Abstract: The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.

Abstract: The present invention discloses a fault diagnosis method under a convergence trend of a center frequency, including: (1) acquiring a dynamic signal x(t) of a rotary machine equipment; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) by using the variational model with the set initial decomposition parameters, and traversing a signal analysis band and performing iterative decomposition on the dynamic signal x(t) under the guidance of a convergence trend of a center frequency, to obtain optimized modals {m1 . . . mn . . . mN} and corresponding center frequencies {?1 . . . ?n . . . ?N}; (4) searching a fault related modal mI, guiding parameter optimization by using a center frequency ?I of the fault related modal mI, and retrieving an optimal target component mI including fault information; and (5) performing envelopment analysis on the optimal target component mI, and diagnosing the rotary machine equipment according to an envelope spectrum.

Abstract: An engineered collection medium for use in mineral separation is described. The engineered collection medium has a solid phase body configured with a three-dimensional open-cell structure like foam or sponge to provide collection surfaces. The three-dimensional surface structure is made of a hydrophobic material which is a reaction product of isocyanate and polyol promotes the attraction of mineral particles to the collection surfaces as a hydrophobic foam. The hydrophobic foam can be in the form of a cube, sphere, or sheet and can be used in a filter or conveyor belt in a processor.

Abstract: The present invention discloses a method for predicting bearing life based on a hidden Markov model (HMM) and transfer learning, including the following steps: (1) acquiring an original signal of full life of a rolling bearing; and extracting a feature set including a time domain feature, a time-frequency domain feature, and a trigonometric function feature; (2) inputting the feature set into an HMM to predict a hidden state, to obtain a failure occurrence time (FOT); (3) constructing a multilayer perceptron (MLP) model, obtaining a domain invariant feature and an optimal model parameter, and obtaining a neural network life prediction model; and (4) inputting the remaining target domain feature sets into the neural network life prediction model, and predicting the remaining life of the bearing. In the present invention, MLP-based transfer learning is used to resolve distribution differences in a source domain and a target domain caused by different operating conditions.

Abstract: A molecular sieve has a silica/alumina molar ratio of 100-300, and has a mesopore structure. One closed hysteresis loop appears in the range of P/P0=0.4-0.99 in the low temperature nitrogen gas adsorption-desorption curve, and the starting location of the closed hysteresis loop is in the range of P/P0=0.4-0.7. The catalyst formed from the molecular sieve as a solid acid not only has a good capacity of isomerization to reduce the freezing point, but also can produce a high yield of the product with a lower pour point. The process for preparing the catalyst involves steps including crystallization, filtration, calcination, and hydrothermal treatment.

Abstract: An inspection positioning prostatic capsule expansion catheter includes a catheter main body. The catheter main body is internally provided with a catheterization cavity, a rinse cavity, a front bag cavity, and a rear bag cavity. The four cavities are each provided with a front-end opening. At the rear of the front-end openings of the catheterization cavity and the rinse cavity is the front bag cavity. At the rear of the front bag cavity is the rear bag cavity arranged in parallel. The catheter main body at the tail of the rear bag cavity is provided with a miniature visual probe. The miniature visual probe is mounted on a semi-circular protrusion at the tail of the rear bag cavity.

Abstract: Disclosed herein are biosensor systems and related methods for detecting analytes in aqueous and biologic environments. A biosensor system for detecting binding of an analyte of interest may include a detector configured to detect a change in an electrical property on a surface thereof. The detector may be a FET. The system also may include a passive layer disposed on a top surface of the detector. Further, the system may include a hydrophobic layer disposed on the passive layer. The system also may include a receptor-attachment material configured for binding to an analyte. A receptor may bind to the analyte, and the receptor may be attached to the receptor-attachment material. The binding of the analyte to the receptor can cause the change of the electrical property at the surface. In response to the change for example, a current may change for indicating the binding of the analyte to the receptor.

Abstract: An inspection positioning prostatic capsule expansion catheter includes a catheter main body. The catheter main body is internally provided with a catheterization cavity, a rinse cavity, a front bag cavity, and a rear bag cavity. The four cavities are each provided with a front-end opening. At the rear of the front-end openings of the catheterization cavity and the rinse cavity is the front bag cavity. At the rear of the front bag cavity is the rear bag cavity arranged in parallel. The catheter main body at the tail of the rear bag cavity is provided with a miniature visual probe. The miniature visual probe is mounted on a semi-circular protrusion at the tail of the rear bag cavity.

Abstract: Disclosed herein are biosensor systems and related methods for detecting analytes in aqueous and biologic environments. A biosensor system for detecting binding of an analyte of interest may include a detector configured to detect a change in an electrical property on a surface thereof. The detector may be a FET. The system also may include a passive layer disposed on a top surface of the detector. Further, the system may include a hydrophobic layer disposed on the passive layer. The system also may include a receptor-attachment material configured for binding to an analyte. A receptor may bind to the analyte, and the receptor may be attached to the receptor-attachment material. The binding of the analyte to the receptor can cause the change of the electrical property at the surface. In response to the change for example, a current may change for indicating the binding of the analyte to the receptor.