Abstract: A cooling assembly and a heat-not-burn cartridge are provided. The cooling assembly includes a cooling member, a first closing member, and a second closing member. The cooling member has a first end and a second end opposite to the first end, and defines a cooling passage penetrating through the first end and the second end. The first closing member is fixed to the first end. The first closing member defines at least one first through-hole. The at least one first through-hole is in communication with the cooling passage. The second closing member is fixed to the second end. The second closing member defines at least one second through-hole. The at least one second through-hole is in communication with the cooling passage.

Abstract: A deep learning fault diagnosis method includes the following steps: a fault diagnosis data set X is processed based on sliding window processing, to obtain a picture-like sample data set {tilde over (X)}, and obtain an attention matrix A of the picture-like sample data set {tilde over (X)}; and a 2D-CNN model is constructed to process the picture-like sample data set {tilde over (X)} to obtain a corresponding feature map F, and in the meantime, the feature map F is processed based on channel-oriented average pooling and channel-oriented maximum pooling to obtain an output P1 of the average pooling and an output P2 of the maximum pooling, and a weight matrix W is obtained based on the attention matrix A, the output P1 of the average pooling, and the output P2 of the maximum pooling, so that an output of the model is a feature map {tilde over (F)} based on an attention mechanism, where {tilde over (F)}=WF.

Abstract: Disclosed are an accelerated test data analysis method and apparatus based on a grey forecast model, a device.

Abstract: A performance degradation-based product reliability weak link evaluation method includes: acquiring a performance degradation parameter of each component of a to-be-evaluated product corresponding to each test time point; evaluating the performance degradation parameter of each component corresponding to each test time point through a fault time evaluation model, to obtain a pre-fault operating duration of each component; evaluating the pre-fault operating duration of each component through a confidence-based unreliability evaluation method, to obtain an unreliability evaluation result of each component; generating at least one component service life distribution model corresponding to each component according to the unreliability evaluation result, and selecting a corresponding target component service life distribution model; and evaluating mean time between failures corresponding to each component according to the target component service life distribution model, and selecting a reliability weak link of th

Abstract: A method for assembling a cartridge is provided. The method for assembling the cartridge includes the following. Multiple outer tubes are provided, where each of the multiple outer tubes has a sealing portion at a first end of a corresponding outer tube. Tobacco is loaded from a second end of the outer tube, where the second end is opposite to the first end. A closed end of a cooling tube is identified, and the cooling tube is mounted into the outer tube from the second end to make the closed end adjacent to the tobacco, where the cooling tube has the closed end and an open end opposite to the closed end. A filter member is mounted into the outer tube from the second end to make the filter member adjacent to the cooling tube.

Abstract: The present disclosure discloses a method and apparatus for estimating product lifetime based on a multi-stress accelerated test, and a device. This method can be applied in the field of data processing technologies, specifically including: determining, in response to a lifetime estimation request for a target product, a target characteristic lifetime of the target product based on a target stress condition for the target product and correspondence relationships between candidate stress conditions and candidate characteristic lifetimes; and determining, based on an optimal lifetime model for the type to which the target product belongs and the target characteristic lifetime, a target average life of the target product.

Abstract: A filter assembly and a cartridge are provided. The filter assembly includes a filter member and a functional member. The filter member defines a receiving space in a radial direction of the filter member. The functional member is fixed in the receiving space. The filter member is in contact with part of an outer surface of the functional member.

Abstract: The present disclosure relates to a method and an apparatus for processing accelerated test data based on multiple performance degradation, and a device.

Abstract: The present disclosure relates to a method and an apparatus for reliability evaluation based on multiple performance degradation, a computer device, a storage medium, and a computer program product. The method includes: for each of the multiple performances of a target product, obtaining a plurality of candidate functions that the performance possibly follows, determining a target function that satisfies a preset selection condition from the plurality of candidate functions, and determining a reliability function corresponding to the performance based on the target function; determining coupling relation information between the multiple performances of the target product, and obtaining redundancy information of the multiple performances of the target product; and performing a reliability evaluation on the target product based on the coupling relation information, the redundancy information, and the reliability function of each performance.

Abstract: The present disclosure relates to a method and an apparatus for processing accelerated test data based on multiple performance degradation, and a device.

Abstract: Disclosed are a fault prediction method and apparatus for a power conversion device, and a power conversion system. The method includes: acquiring multiple output voltages of a detecting coil in a preset time period, wherein an electromagnetic induction is generated between the detecting coil and a switching-on circuit of a switching transistor in the power conversion device; extracting each output frequency corresponding to each output voltage of the detecting coil; predicting time when the power conversion device fails according to a change trend of each output frequency. The apparatus includes a detecting coil and a data processing device; the detecting coil is connected to the data processing device and is a closed coil; the data processing device is configured to: acquire an output voltage of the detecting coil, extract corresponding output frequency, and predict time when the power conversion device fails according to a change trend of each output frequency.

Abstract: A deep learning fault diagnosis method includes the following steps: a fault diagnosis data set X is processed based on sliding window processing, to obtain a picture-like sample data set {tilde over (X)}, and obtain an attention matrix A of the picture-like sample data set {tilde over (X)}; and a 2D-CNN model is constructed to process the picture-like sample data set {tilde over (X)} to obtain a corresponding feature map F, and in the meantime, the feature map F is processed based on channel-oriented average pooling and channel-oriented maximum pooling to obtain an output P1 of the average pooling and an output P2 of the maximum pooling, and a weight matrix W is obtained based on the attention matrix A, the output P1 of the average pooling, and the output P2 of the maximum pooling, so that an output of the model is a feature map {tilde over (F)} based on an attention mechanism, where {tilde over (F)}=WF.

Abstract: An industrial robot motion accuracy compensation method includes: establishing a motion parameter database, wherein the motion parameter database includes a plurality of different reference operating conditions and a motion parameter of the industrial robot corresponding to each reference operating condition, and each reference operating condition is formed by combining each element in each set in a total set of operation conditions; acquiring a current operating condition of the industrial robot; determining whether there is a reference operating condition matched with the current operating condition in the motion parameter database; if yes, taking a motion parameter corresponding to the matched reference operating condition as a motion parameter corresponding to the current operating condition; if no, performing an interpolation on a motion parameter corresponding to the current operating condition, and taking an interpolation result as the motion parameter corresponding to the current operating condition.

Abstract: An industrial robot motion accuracy compensation method includes: establishing a motion parameter database, wherein the motion parameter database includes a plurality of different reference operating conditions and a motion parameter of the industrial robot corresponding to each reference operating condition, and each reference operating condition is formed by combining each element in each set in a total set of operation conditions; acquiring a current operating condition of the industrial robot; determining whether there is a reference operating condition matched with the current operating condition in the motion parameter database; if yes, taking a motion parameter corresponding to the matched reference operating condition as a motion parameter corresponding to the current operating condition; if no, performing an interpolation on a motion parameter corresponding to the current operating condition, and taking an interpolation result as the motion parameter corresponding to the current operating condition.

Abstract: A predictive maintenance system and method for intelligent manufacturing equipment is provided. The predictive maintenance system includes a first-stage predictive maintenance module, a second-stage predictive maintenance module, and a maintenance decision module. The first-stage predictive maintenance module includes an acquisition module, a human-computer interaction module, a calculation module, and a storage module. The second-stage predictive maintenance module includes a communication module, a setting module, and a prediction module. The maintenance decision module is configured to receive a first-stage remaining service life calculated by the first-stage predictive maintenance module and a second-stage remaining service life predicted by the second-stage predictive maintenance module, and determine a predictive maintenance strategy of the intelligent manufacturing equipment according to the first-stage remaining service life and the second-stage remaining service life.

Abstract: The present invention relates to a static compliance performance testing device applied to an industrial robot, comprising a loading direction adjusting component and a loading force adjusting component, wherein the force applying end of the loading direction adjusting component is connected with an end mechanical interface of the industrial robot, and is configured to adjust a force bearing direction of the end mechanical interface. The loading force adjusting component comprises a lever, a first-stage weight and a second-stage weight, the lever is provided with a fixing part and is rotatable around the fixing part, the force bearing end of the loading direction adjusting component is connected with the lever, the first-stage weight is suspended on the lever, the second-stage weight is suspended on the lever and is movable along the lever, the weight of the first-stage weight is larger than the weight of the second-stage weight.

Abstract: The present invention relates to a static compliance performance testing device applied to an industrial robot, comprising a loading direction adjusting component and a loading force adjusting component, wherein the force applying end of the loading direction adjusting component is connected with an end mechanical interface of the industrial robot, and is configured to adjust a force bearing direction of the end mechanical interface. The loading force adjusting component comprises a lever, a first-stage weight and a second-stage weight, the lever is provided with a fixing part and is rotatable around the fixing part, the force bearing end of the loading direction adjusting component is connected with the lever, the first-stage weight is suspended on the lever, the second-stage weight is suspended on the lever and is movable along the lever, the weight of the first-stage weight is larger than the weight of the second-stage weight.