Abstract: Embodiments may include a coated granule for roofing systems. The coated granule may include an aluminum silicate granule and a coating disposed on the aluminum silicate granule. The coating may include a copolymer and a siloxane-based or a silane-based compound. The copolymer may be a cationic fluorinated (meth)acrylic copolymer. The aluminum silicate granule may have a particle size in a range from 0.2 mm to 2.4 mm. The aluminum silicate granule may have a 65% or greater reflectivity. The coated granule may repel oil and maintain its reflectivity better than with other techniques.

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: Provided is a polymer blend composition comprising linear low density polyethylene, a polypropylene polymer, generally comprising a random polypropylene copolymer, and a compatibilizer. In one embodiment, there is provided a polymer blend composition comprising 15 to 35 wt % of a polypropylene polymer, 30-60 wt % of a linear low density polyethylene, and a compatibilizer polymer composition comprising 21-50 wt % of the polymer blend. The polymer blend is quite useful in a roofing membrane.

Abstract: Embodiments may include a coated granule for roofing systems. The coated granule may include an aluminum silicate granule and a coating disposed on the aluminum silicate granule. The coating may include a copolymer and a siloxane-based or a silane-based compound. The copolymer may be a cationic fluorinated (meth)acrylic copolymer. The aluminum silicate granule may have a particle size in a range from 0.2 mm to 2.4 mm. The aluminum silicate granule may have a 65% or greater reflectivity. The coated granule may repel oil and maintain its reflectivity better than with other techniques.

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: Embodiments may include a coated granule for roofing systems. The coated granule may include an aluminum silicate granule and a coating disposed on the aluminum silicate granule. The coating may include a copolymer and a siloxane-based or a silane-based compound. The copolymer may be a cationic fluorinated (meth)acrylic copolymer. The aluminum silicate granule may have a particle size in a range from 0.2 mm to 2.4 mm. The aluminum silicate granule may have a 65% or greater reflectivity. The coated granule may repel oil and maintain its reflectivity better than with other techniques.

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 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: Systems and methods for making a waterproofed concrete wall assembly, such as for a basement wall. A form is constructed. At least a first side of the form is made of one or more boards having a polymer foam and a fibrous facer. At least some of the boards have perimeter edges shaped such that some adjacent boards overlap at the shaped perimeter edges. Concrete is poured into the gap between the sides of the form, and is allowed to harden. The other side of the form may be removed, while leaving the boards of the first side of the form in place adjacent the hardened concrete. Some of the concrete is infused into the fibrous facers of at least some of the boards.

Abstract: Embodiments may include a coated granule for roofing systems. The coated granule may include an aluminum silicate granule and a coating disposed on the aluminum silicate granule. The coating may include a copolymer and a siloxane-based or a silane-based compound. The copolymer may be a cationic fluorinated (meth)acrylic copolymer. The aluminum silicate granule may have a particle size in a range from 0.2 mm to 2.4 mm. The aluminum silicate granule may have a 65% or greater reflectivity. The coated granule may repel oil and maintain its reflectivity better than with other techniques.

Abstract: Embodiments may include a coated granule for roofing systems. The coated granule may include an aluminum silicate granule and a coating disposed on the aluminum silicate granule. The coating may include a copolymer and a siloxane-based or a silane-based compound. The copolymer may be a cationic fluorinated (meth)acrylic copolymer. The aluminum silicate granule may have a particle size in a range from 0.2 mm to 2.4 mm. The aluminum silicate granule may have a 65% or greater reflectivity. The coated granule may repel oil and maintain its reflectivity better than with other techniques.

Abstract: Systems and methods for making a waterproofed concrete wall assembly, such as for a basement wall. A form is constructed. At least a first side of the form is made of one or more boards having a polymer foam and a fibrous facer. At least some of the boards have perimeter edges shaped such that some adjacent boards overlap at the shaped perimeter edges. Concrete is poured into the gap between the sides of the form, and is allowed to harden. The other side of the form may be removed, while leaving the boards of the first side of the form in place adjacent the hardened concrete. Some of the concrete is infused into the fibrous facers of at least some of the boards.

Abstract: Systems and methods for making a waterproofed concrete wall assembly, such as for a basement wall. A waterproof liner is present at the pouring of the concrete of the wall assembly, and has a fibrous layer into which concrete can infuse before hardening. The waterproof liner may include one or more boards including a polymer foam. The boards may line a form used to make the wall assembly, or may make up part of the form.

Abstract: Embodiments may include a coated granule for roofing systems. The coated granule may include an aluminum silicate granule and a coating disposed on the aluminum silicate granule. The coating may include a copolymer and a siloxane-based or a silane-based compound. The copolymer may be a cationic fluorinated (meth)acrylic copolymer. The aluminum silicate granule may have a particle size in a range from 0.2 mm to 2.4 mm. The aluminum silicate granule may have a 65% or greater reflectivity. The coated granule may repel oil and maintain its reflectivity better than with other techniques.

Abstract: A membrane roofing system that includes a waterproof layer that protects an insulation layer and a granule coupled to the waterproof layer. The granule has a 60% or greater reflectivity that reduces transmission of ultraviolet light to the waterproof layer. The granule is coated in a fluorinated acrylic copolymer that resists adsorption and absorption of asphaltic chemicals by the granule from the waterproof layer.

Abstract: Fiberizing bushings for fiberizing molten materials including molten glass are heated by applying a voltage drop across the bushings wherein molten material flows through an array of hollow tips attached to, or integral with, a tip plate having orifices therein that generally align with channels through the hollow tips to form fibers. The uniformity of the diameter of the fibers produced is much improved by using tips of different lengths and/or tips having channels of differing ID's to compensate for unequal electrical heating and/or cooling effects of drawn-in ambient air that cools the tips on the extreme outer periphery and/or cooling or heating effects of external supports or cooling members running through the array of tips that cools or heats adjacent tips more than the interior tips.