375 results about "Failure type" patented technology

The invention discloses an internet log data-based software defect failure recognition method and system. The method comprises the following steps: in allusion to internet source system log data and user system source log data, taking the internet source system log data as a training set and extracting features from the training set, and generating a software defect failure log recognition prediction model through machine learning or similarity matching; and analyzing and recognizing the user system source log data to obtain log fragments which represent software defect failures, so as to a software defect failure types in allusion to user system logs. A cloud computing system failure recognition system comprises a multi-line log collection module, an internet source system log classifier and an online log analysis and failure recognition module. According to the method and system disclosed in the invention, the failures, caused by software defects, massive log information recognition can be realized, the failure reasons can be rapidly positioned, the failures during the operation can be recognized and the failure types can be diagnosed, so that the reliability and usability of the cloud computing system can be improved.

The invention provides a cell connection failure processing method and device. The method comprises the steps of receiving a change command for indicating change of a cell group; determining that connection with a source cell group and/or a target cell group is unsuccessful when the cell group is changed according to the change command; and determining a connection failure type and processing theconnection failure in a processing mode corresponding to the failure type. Through application of the method and the device, the problem that in related technologies, the wireless connection failure and data break occur in a wireless connection process is solved, and the effect that the connection is recovered and the data is transmitted relatively fast when the wireless link problem occurs in a user device is achieved.

The invention discloses an establishing method of a failure type prediction model. The establishing method comprises the following steps of: collecting a vibration signal of a fan, and meanwhile, collecting the audio signal of the fan; pretreating the vibration signal and carrying out feature extraction on the vibration signal after pretreating so as to obtain a vibration characteristic parameter, and pretreating the audio signal and carrying out feature extraction on the audio signal after pretreating so as to obtain an audio characteristic parameter; and substituting the vibration characteristic parameter to a failure type vibration template so as to obtain a failure type corresponding to the vibration characteristic parameter, and associating the audio characteristic parameter with the failure type so as to establish a failure type audio template. The invention also discloses an establishing system of the failure type prediction model and an establishing method and system of a malfunction probability prediction model as well as a fan monitoring pre-warning system and method. The fan monitoring early-warning system and method provided by the invention have the advantages that the sensitivity is high, and the failure monitoring is more exact.

A method for Fault Tree Map generation employs to transformation of Fault Trees of production installation, specific installation, technical system (Hardware and integrated Hardware/Software) to new Fault Tree diagram (Fault Tree Map), which permits drastically compact the Fault Tree depiction and facilitates performing of the Fault Tree qualitative analysis, including evaluation of combination of latent failures and evident failures, repeated events and critical events position influence, and failure propagation potentiality, besides facility of localization of each Fault Tree logical Gate and relevant failures in the fault tree printed report. Generation takes place using special symbols, which permit to reflect the Fault Tree logic, present all Fault Tree failures with graphically identification of the failure type, and show the failure repetition and also the failure critically (importance) to Fault Tree Top Event probability. The method presents exceptional advantages to analysis of large-scale, extended Fault Trees, allowing vastly decrease the time of analysis and elevate the analysis quality and Fault Tree perception, including for specialists, who are not the Fault Tree authors.

A method of processing a message in a short message service in which a router or alternatively a gateway makes a first attempt to deliver the message, the deliver attempt failing the router or gateway sends the message and other supporting information to a service center that can store the message and re-attempt delivery of the message. The supporting information provided by the router or gateway enables the service center to process the message in an efficient manner. The supporting information can include a failure type indicator, a charge indicator, a charge reference number, a virtual mobile indicator and a reply indicator.

The invention provides equipment failure early-warning system and method. The equipment failure early-warning system has reliable operation, high performance-price ratio and visual interface, can transmit accurate equipment failure early-warning signals in advance and has failure type self-learning function. The equipment failure early-warning method comprises the following steps of: arranging proper types of sensors on proper positions of equipment to collect the parameters, such as bearing temperature, pressure, body vibration waveform, current variation waveform, and the like of the equipment; digitizing signals collected by the sensors by adopting a low-speed data collecting module and a high-speed data collecting module; developing specific computer software to process, store and analyze the various kinds of collected data; realizing the timely time-domain frequency-domain exchange and analysis of the waveforms, such as vibration, electric current, and the like by the software by using the mathematical methods of Fourier transformation, Wigner-Ville distribution, Hilbert-Huang transform, wavelet analysis, and the like; evaluating the various detected parameters by using a scoring method of an established file; and discovering and diagnosing the early exception of the equipment, thereby early warning the equipment failure. The equipment failure early-warning system adopts a modularized framework and comprises four modules of a sensor module, a data collecting module, a data processing module and a self-learning intelligent early-warning platform, the four modules are connected by a data transmission network, and a detection mode can adopt an on-line monitoring mode and a portable point detection mode.

A sterilizable flexible pouch package includes a non-woven barrier web, a perforated plastic intermediate web, and an imperforate plastic upper web. Line heat seals bonding the webs from a cavity into which a sterilizing fluid may enter after passing through the barrier and intermediate webs. The line heat seal bonding the upper and intermediate webs is of the delamination failure type. An access opening is provided for insertion of an object to be sterilized into the cavity after which the access opening is sealed, enclosing the object in the cavity. After sterilizing, the package is opened by peeling apart the upper web from the intermediate web which causes a delamination failure of the line heat seal. Preferred materials enable steam sterilization at temperature of 270° F.

The invention discloses a rolling bearing failure diagnosis method base on vibration temporal frequency analysis. The method comprises the following steps: utilizing a vibration acceleration sensor to collect vibration signals of the rolling bearing under a normal condition and a failure condition; utilizing a modified inherent time scale resolving method to resolve the collected vibration signals, and generating a plurality of inherent time scale components and residual signals; calculating relativity of the time scale components and the vibration signals, selecting the inherent time scale components of which the relativity is ranked top 5 as related components, and rejecting noise signals and false components; calculating Wigner distribution of the related components respectively, and conducting linear stack to obtain the Wigner temporal frequency figure of the original signal; extracting difference fractal box dimensionality of the Wigner temporal frequency figure and the image entropy as failure characteristics; utilizing mahalanobis distance to build mapping relation of the failure characteristics and failure types to realize failure diagnosis. According to the invention, interference of Wigner distribution cross terms is avoided; two kinds of representative failure characteristics of the difference fractal box dimensionality and the image entropy are confirmed.

Techniques for classifying failure messages received from email messages that are sent are provided. A plurality of rules are created that are used to classify a plurality of failure types for email messages. Email messages are sent to an email address associated with an Internet service provider. When an email message fails, a failure message is determined. A rule that applies to the failure message is then determined and based on the rule, a failure type associated with the rule is determined. Once a failure type is determined, an action may be performed based on the failure type. For example, the email address associated with the email message may be marked as invalid.

The invention discloses an elevator failure recognition method based on a convolutional neural network. The elevator failure recognition method comprises the steps that 1, elevator motion data are collected and are converted into a time-spectrogram as a sample set through wavelet transform; 2, the time-spectrogram in the sample set is divided into a training set and a test set, failure types and failure degrees of samples in the training set are marked as known labels of data samples; 3, the convolutional neural network is built, the time-spectrogram in the training set is input to the convolutional neural network, and the characteristics of the previous layer are extracted and classified; 4, according to the labels given in the step 2 and the characteristics extracted in the step 3, a multi-class SVM classifier is trained; 5, after training, the prediction rates, for all types of failures, obtained through the SVM classifier are obtained; and 6, detection and recognition are conducted. The elevator failure recognition method which is novel in angle, consistent to actual situation and high in accuracy is achieved, the requirement for hardware is low, and portability is high.

The embodiment of the invention discloses a method, an apparatus for repairing vehicle system failures, a device, a medium, and a vehicle. The method comprises the steps of: acquiring vehicle parameter information when a vehicle system fails; transmitting vehicle parameter information to a server, so that the server determines a failure type according to a pre-trained failure classification decision model and vehicle parameter information; receiving a repair solution corresponding to the failure type delivered by the server, and executing the repair solution. The method solves the problem thatthe repair process is cumbersome and costly when the vehicle failures are completely dependent on the manual repair, and the convenient repair of the vehicle failure is realized, and the maintenancecost is reduced.

The invention provides a method and a system for monitoring power equipment failures. The method includes the steps of acquiring status indicator data of power equipment; determining whether the status indicator data are abnormal or not according to a preset status indicator threshold and acquiring abnormal status indictor data; determining the types of power equipment failures according to the abnormal status indicator data and a preset failure judgment model, and acquiring preset processing instructions for the failure types. The equipment status indicator data include online detection status indicator data and observation status indicator data. The failure judgment model includes association between the status indicator data and the failure types, weight of the association, and processing instructions for the failure types. The method and system can be used for diagnosing, processing and preventing power equipment failures, and power equipment is safer.

The invention belongs to the field of cable electrical failure simulation analysis and particularly relates to a distributive optical fiber temperature measurement based cable electrical failure simulation analysis method. The method comprises building a cable failure model, setting line parameters, determining failure types, performing failure simulation and obtaining the current value during the failure through electromagnetic transient simulation software; building a cable transient thermal path model according to IEC 60287 standards, and calculating the cable transient temperature during the failure; building a cable and temperature measurement optical fiber thermodynamic finite element model through finite element simulation software, and simulating the steady temperature field distribution before the failure and the transient temperature field distribution and changes after the failure of the cable and the temperature measurement optical fiber; obtaining cable electrical failure recognition and location criteria according to the temperature distribution and change rule of the temperature measurement optical fiber in simulation results. According to the method, the problems of poor accuracy, difficulties in experiment and the like during cable electrical failure analysis through temperature measurement optical fiber temperature distribution data are solved. The method has the advantages of being flexible in failure setting, high in work efficiency and the like.

The invention relates to a robot inspection system and inspection method for a cold region transformer substation, and belongs to the robot inspection field for a transformer substation. The robot inspection system and inspection method for a cold region transformer substation solve the problem that an existing manual inspection mode for a transformer substation is low in efficiency and bad in the detection quality. The robot inspection system for a cold region transformer substation comprises a main control system, a motion control system, a sensing detection system, a wireless communication system and a failure warning system, wherein the main control system is used for task scheduling of an inspection robot, and is also used for receiving an environment perception signal, orientation data, temperature data, environmental images and pointer instrument images, and sends a motion control signal according to the environment perception signal and the orientation data; by means of comparison between the collected temperature data and a set temperature threshold, when the collected temperature data exceeds the set temperature threshold, an warning control signal is transmitted; the images in a failure type model base are matched with the received environmental images; and if the matching is successful, the instrument data of the transformer substation can be read according to the received pointer instrument images. The robot inspection system and inspection method for a cold region transformer substation are used for inspection of a transformer substation.

A method for selectively refreshing data in a nonvolatile memory array based on failure type detected by an error correction code. If the page is determined to be error-free, no refresh operation takes place. Otherwise, if single-error words on a page contain erased and programmed bit errors, then a refresh operation, consisting of an erase and program, takes place. The erase operation is skipped if single-error words on a page solely contain a program failure.

The invention relates to the application field of computers, provides a storage system failure predicting method and a storage system failure predicting device, and solves the problem that existing processing methods cannot meet high requirements of high availability storage systems. The storage system failure predicting method includes collecting failure information of different failure types in a system; preprocessing the failure information of all the failure types respectively to obtain sub failure types under each failure type and failure occurrence time series corresponding to all the sub failure types; and predicting the next occurrence time of a failure corresponding to all the sub failure types. The technical scheme of the storage system failure predicting method and the storage system failure predicting device is suitable for the high availability storage systems, and high accuracy failure prediction is realized.

The invention relates to a log similarity based failure processing method and device. In one implementation mode, the log similarity based failure processing method comprises the steps of making response to acquire multiple log files of at least one system having failures and extracting log modes from the multiple log files, wherein the log modes describe regular expressions corresponding to log messages in the log files in the multiple log files; utilizing the log modes to establish a mode memory bank; mapping each log file in the multiple log files to an n-dimensional vector; clustering multiple n-dimensional vectors where the multiple log files are mapped to at least one group, wherein each group in at least one group indicates one failure type of at least one system. In another implementation mode, the corresponding device is provided. By means of the log similarity based failure processing method and device, manual working amount during failure processing can be reduced.

The invention provides an equipment failure prediction method based on point polling data and DCS (Data Communication System) online data, which comprises the steps of: establishing an equipment overhauling information base, wherein the overhauling information base comprises equipment point polling data and DCS online data as well as equipment overhauling data; carrying out relevance storage on the equipment point polling data, the DCS online data and equipment overhauling data; and then periodically carrying out data matching on data of equipment in current state and data in the overhauling information base, when the data of the equipment in the current state are matched with some overhauling data in the overhauling information base, predicting a failure type of the equipment according to the data in the overhauling information base and predicting the failure generating time. With the data accumulation of the data in the equipment overhauling information base, most of equipment failure types are predicted, an effective basis is provided for the predictable overhauling of the equipment and the basic protection is provided for the normal production.

The embodiment of the invention provides a failure positioning method and a failure positioning device, and relates to the technical field of internet. The failure positioning method comprises the following steps: acquiring failure complaining information of target service; acquiring a target log matched with the failure complaining information from a terminal burying point log, wherein the terminal burying point log is generated by a terminal for providing the target service to a user and is used for recording the operating information of the target service; analyzing the target log and positioning the failure type and the failure occurrence range corresponding to the failure complaining information; determining a server log generated by a back-end server of the target service corresponding to the target terminal, wherein the target terminal is the terminal which generates the target log; and analyzing the determined server log and positioning the failure reason corresponding to the failure complaining information. Compared with the prior art, the scheme provided by the embodiment of the invention has the following advantages: the hidden danger that the failure is difficult to position rapidly can be eliminated, the duration of the influence on users by the failure can be reduced and the user experience can be improved.

The present invention relates to a universal sensor self-diagnosis device and to a diagnosis method therefor. In more detail, the device of the present invention includes: a sensor signal input unit for receiving a sensor signal from at least one sensor; an input data processing unit for processing the received sensor signal from the sensor signal input unit as a value available for a diagnosis device; a modeling unit for calculating a diagnosis modeling value by modeling data processed by the input data processing unit; a memory for storing a reference modeling value for the sensor signal of the sensor, which is modeled in advance when the sensor is in a normal state, and a failure type determining unit for determining a failure type of the sensor; a failure diagnosis unit for diagnosing a failure of a corresponding sensor by assessing the degree of correlation between the diagnosis modeling value and a reference modeling value of the corresponding sensor stored in the memory; and a failure type determining unit for determining a failure type of the corresponding sensor by assessing the degree of correlation between the diagnosis modeling value and the reference modeling value using the failure type determining data if it is determined by the failure diagnosis unit that there is a failure in the corresponding sensor.

The invention relates to a power battery pack security prevention and control method and system for an electric vehicle and a computer readable storage medium. The method comprises the steps that state information of a battery pack is obtained in real time; whether or not a battery thermal runaway early failure occurs is judged according to the state information of the battery pack; if yes, the failure type and failure location are determined; according to the determined failure type and failure location, thermal runaway early treatment action is executed; according to the stage of the electric vehicle battery safety accidents, the characteristics of the battery system accidents are monitored in real time, and early warning is performed in time. Aiming at the characteristics of the batterysystem accidents, corresponding elements and mechanisms are additionally arranged in the design process, and the occurrence or spreading process of the accidents is delayed or prevented. According tothe method, a comprehensive prevention and control strategy can be provided for the actual situation of the particular accidents, the prevention and control mechanism is started accurately, the security prevention and control effect is maximized, and the safety of passengers of the electric vehicle is guaranteed.

The invention discloses an alarm processing method and an alarm processing system. The method comprises the following steps of: 202, judging whether a reported alarm is an event type alarm or a failure type alarm; and 204, setting a buffer queue according to a judgment result. Through the method and the system, the failure type alarm and the event type alarm are distinguished and different processing is performed according to judgment results respectively, so that the processing speed of a relative rule is increased for the failure type alarm, a flexible processing strategy is provided, alarm processing speed is increased, and alarm processing efficiency is improved.

The invention discloses an indoor distribution system antenna feeder monitoring method. The indoor distribution system antenna feeder monitoring method comprises information source equipment and an antenna feed system, wherein the antenna feed system is connected with at least one antenna feed monitoring terminal, and the information source equipment is connected with an equipment monitoring relay. The antenna feed monitoring terminal and the equipment monitoring relay are connected with each other through the antenna feed system, and the equipment monitoring relay is further connected with a monitoring center platform. The antenna feed monitoring terminal collects a signal level at an antenna position of the antenna feed system and then conveys the collected signal level to the equipment monitoring relay through the antenna feed system in a carrier wave mode. The equipment monitoring relay transmits self-sampled signal level data and signal level data of the antenna feed monitoring terminal to the monitoring center platform. The monitoring center platform conducts statistic analysis and logical operation respectively to the signal level data collected by the equipment monitoring relay and the antenna feed monitoring terminal, so that whether the signal levels of the information source equipment, each antenna feeder and each antenna port of an indoor distribution system are normal can be obtained, or the failure type and the failure range can be obtained.

The invention provides a method for improving wind turbine generator gearbox failure level judgment precision. The method comprises the steps that A, a failure type is determined according to the vibration signal characteristic value of a gearbox bearing; B, the distribution condition of all currently-monitored parameters corresponding to all monitored objects of a wind turbine generator gearbox in a failure level matrix of the failure type is determined, the failure level matrix comprises set failure levels, and each failure level comprises the value ranges of the parameters corresponding to the monitored objects of the wind turbine generator gearbox under the failure level; C, the occurrence probability of each failure level is calculated according to the distribution condition, and the current failure level is determined according to the occurrence probability. Therefore, schematic diagram convergence analysis can be carried out based on different weights of multiple kinds of failure signals, the blindness of locating the failure through a single failure parameter is avoided, the false alarm rate is lowered, and the wind turbine generator gearbox failure level judgment precision is improved.

The invention discloses an intelligent diagnosis system for a track switch machine. The intelligent diagnosis system comprises a plurality of sensors mounted on a track switch and used for acquiring various real-time electric signals of key parts of the track switch to form actual action curves of the track switch machine, a front-end processing device connected with the sensors and used for pre-processing the actual action curves, a first switch connected with the front-end processing device and used for performing switch processing on the processed actual action curves and transmitting processing results, and a data management center connected with the first switch through a router and including a second switch connected with the router and a database server used for storing failure rules, wherein the database server comprises a failure rule base used for storing the failure rules. The invention further discloses an intelligent diagnosis method for the track switch machine. By adoption of the intelligent diagnosis system and method, various failure types can be immediately diagnosed in advance, the trouble shooting speed and comprehensiveness are improved, and the rail transit security is improved.

The present invention provides a method of optimizing network mobility. The method comprises the steps of measuring and recording the failure types, the signal quality of the service cell and adjacent cell signal quality, the position information, the time stamp information, the cell information and the Pcell information of an MeNB when a UE generates the wireless link failure or SCG change failure in a Pscell, then reporting to a network side to be used as the input data of the network side optimization. By utilizing the method of the present invention, the network optimization data under a DC scene can be collected by a commercial terminal, thereby perfecting the problems, such as the network optimization processing, the problem positioning, etc.