3results about How to "Improve failure rate" patented technology

The invention relates to a side channel compressor (1) for a fuel cell system (31) for conveying and / or compressing a gaseous medium, in particular hydrogen, comprising: a housing (3) and a drive (6); a compressor chamber (30) which extends in the housing (3) about an axis of rotation (4) and which has at least one circumferential side channel (19, 21); the invention relates to a side-channel compressor (1) having a housing (3), a rotor chamber (44) in the housing (3), and a compressor wheel (2) in the rotor chamber, which compressor wheel is rotatably arranged about the axis of rotation (4) and is driven by means of the drive (6), the side-channel compressor (1) having at least one bearing (27, 47) on a cylindrical bearing tongue (12), and wherein the corresponding bearing (27, 47) is arranged in the rotor chamber (44). 47) has a bearing outer ring (24), a bearing inner ring (22) and a bearing inner chamber (40) which is sealed from the rotor chamber (44) by means of at least one sealing element (26). According to the invention, the bearing inner chamber (40) can be separated from the rotor chamber (44) by a sealing element (26) in such a way that either a frictional seal or a contactless seal can be established between the bearing inner ring (22) and the sealing element (26) depending on the operating state of the side channel compressor (1). The present application also relates to a fuel cell system (31) having the side channel compressor (1) of the present application.

The application discloses a failure probability prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining product parameters corresponding to a target product, constructing a product simulation model corresponding to the target product according to the product parameters, the target product comprising a screen module, and the screen module comprising one or more components; performing drop simulation testing on the product simulation model based on one or more drop directions to obtain simulation test results corresponding to each drop direction; the simulation test results comprising maximum strain data corresponding to each component and a target component area corresponding thereto; analyzing the simulation test results corresponding to each drop direction by using a constructed failure probability prediction model to obtain a target failure probability corresponding to the target product. Through the failure probability prediction method, device, electronic equipment and storage medium, the probability of display screen breakage of the electronic equipment in the case of drop can be accurately evaluated.

This invention discloses a method and system for reliability assessment of computing power networks considering functional self-healing, belonging to the field of computing power network reliability assessment technology. The invention includes: acquiring operational data of a computing power network, wherein the computing power network includes a main component and an auxiliary subsystem composed of multiple auxiliary components; constructing a reliability model based on the operational data, wherein the reliability model describes the closed-loop coupling relationship between the main component and the auxiliary subsystem, wherein the failure rate of the auxiliary components increases with the increase of the number of failed auxiliary components, and the self-healing rate of the main component decreases with the increase of the number of failed auxiliary components; solving the time-domain reliability function based on the reliability model, and determining the reliability index of the computing power network. This invention, by establishing a closed-loop reliability model of the main-auxiliary coupling structure, characterizes the resource competition relationship between load-sharing failure and state-dependent self-healing, achieving accurate assessment of computing power network reliability and providing a quantitative basis for network operation and maintenance decisions.