522 results about "Multiple fault" patented technology

A life support and monitoring apparatus with malfunction correction guidance is provided. The life support and monitoring apparatus of the present disclosure identifies the root cause or potential cause of a fault/failure and then prompts an operator to take appropriate steps to assure the continuance of life support and critical physiologic monitoring. When multiple faults/failures exist, the apparatus automatically prioritizes them based on risk to the patient and prompts the operator to do the most appropriate intervention to assure patient safety.

The invention discloses a TFDS fault automatic recognition method. The method is formed by a two-stage cascaded convolution neural network model based on a deep learning theory: the first stage is a network model with multi-class fault region synchronous positioning, and with the combination of space mutual position relation constraint of multi-fault regions, synchronous and accurate positioning of the multi-fault target regions is realized; and the second stage is a fault determination network model, and determination of faults and non-faults for the regions located by the first stage is realized. According to the TFDS multi-class fault synchronous and automatic recognition method, the design of recognition methods for different faults is not needed, features are adaptively extracted through a learning training mode by the convolution neural network theory in deep learning, synchronous positioning and fault determination of multiple faults can be performed, and high effectiveness and high robustness are achieved.

The invention discloses a network fault diagnosis method based on deep learning in a network virtualization environment. The network fault diagnosis method comprises the steps of: dividing a network into a physical network and a virtual network, combining the characteristics of occurrence of network faults, considering the time influencing factor, network topological connection characteristics and a mapping relation between the virtual network and the physical network, and comprehensively evaluating the network faults by means of a fault severity grading probability; regarding network characteristic parameters with influence degrees as a model learning resource, paying attention to the correspondence between variation trend of network historical data and fault tags, establishing a network fault diagnosis model with multiple fault grading probabilities in the network virtualization environment based on a viewing angle of deep learning, and training network parameters by using the network fault diagnosis model; and adjusting a fault prediction model in the training process, and utilizing an optimized and adjusted deep learning network to realize fault diagnosis in the network virtualization environment. The network fault diagnosis method can carry out deep analysis on the network parameters in the network virtualization environment, therefore the network fault diagnosis method has higher precision in predicting the network faults.

A method and system for generating a model function h(x,y,z) implicitly representing geologic horizons. Geological data representing a fault network and horizons automatically extracted from seismic data may be received. A 3D mesh may be generated and divided into a plurality of fault blocks by the fault network. A discrete function h(x,y,z) may be defined having values of the geological data representing horizons at discrete nodes of the mesh. Constraints may be installed on the discrete function h(x,y,z) defining surfaces representing horizons. Constraints may be installed on the discrete function h(x,y,z) to ensure the uniqueness of the function h(x,y,z). The discrete function h(x,y,z) may be interpolated at the nodes of the mesh to create a piecewise continuous function h (x,y,z) while honoring the constraints. The piecewise continuous horizon function h(x,y,z) may be synchronized across multiple fault blocks. A model of the piecewise continuous horizon function h(x,y,z) may be displayed.

The invention provides a driver for driving a plurality of light-emitting components and display equipment. According to the embodiment of the invention, a current matching function, a phase shift PWM (Pulse-Width Modulation) dimming function and multiple fault detection and protection functions are integrated into the driver. The embodiment of the invention also provides a high-precision matching circuit, a phase shift PWM dimming circuit and a multifunctional fault detection and protection circuit which are suitable for the driver. The scheme can ensure that the emitted light of the light-emitting components maintains uniform and consistent.

A method of performing diagnostics on a system comprises receiving a plurality of measurement parameters, each corresponding to one of a plurality of parameters at a time k, forming a deviation vector from the plurality of measurement parameters, calculating an initial deviation vector from an initial fault vector, calculating a multiple fault isolation deviation vector using the initial deviation vector and the deviation vector, determining if an event is in progress using the multiple fault isolation deviation vector, performing statistical data validity to set a present inhibit flag and a past inhibit flag, and performing module performance analysis according to the present inhibit flag and the past inhibit flag.

The invention relates to an online state monitoring and fault diagnosis device and an online state monitoring and fault diagnosis method for a rotary machine. The device comprises a data acquisition device 101, a feature extraction device 102, a data management device 103, a display device 106, a device 107 such as a mouse, a keyboard or the like for setting parameters and managing equipment by a user, a multi-model detector training device 104, and a multi-model fault diagnosis device 105. The method comprises the following steps of: acquiring signals by using the data acquisition device; storing the signals, and extracting features of the signals by using the standard feature array extraction device; training a detector by using the training device for the detector for identification; performing identification by adopting the trained hybrid model detector; and outputting and recording the identification result. The device and the method can diagnose common rotary machine faults such as shaft eccentricity, bearing eccentricity, rolling body abrasion and the like, and have the advantages of high automation degree, capability of identifying multiple fault types, capability of realizing early diagnosis, good fault database expansibility and the like.

The application discloses a method for fault analysis, comprising the following steps of: obtaining original alarm information of multiple fault alarm events; preprocessing the original alarm information to obtain standard alarm information; calculating the standard alarm information by using an Apriori algorithm, and obtaining a correlation degree between each fault alarm event; obtaining a corresponding correlation rule according to the correlation degree between the fault alarm events; and analyzing the target fault alarm events by using the correlation rule, and obtaining a fault analysisresult when the target fault alarm events are received. According to the method for the fault analysis, the fault alarm event can be effectively analyzed, so that the processing efficiency of the fault of the operation and maintenance is improved, and the reliability of the system is further enhanced. The application further discloses a device for fault analysis, equipment, and a computer readablestorage medium, which have the above beneficial effects.

A system and method for iterative condition monitoring and fault diagnosis of an electric machine, such as an electric motor, is configured to iteratively analyze a pattern of multiple fault signatures in a motor current signal under harsh, noisy conditions. As such, the present invention evaluates patterns of multiple fault signatures that are spread over the wide motor current spectrum, by effectively rejecting signal distortions, harsh noise, and interference from the dynamic operation of the electric machine.

A master node transmits a probe packet for checking a condition of a network in a ring in both directions thereof to detect the occurrence of a fault on the ring. A slave node increments a hop count included in the received probe packet, transmits it to an adjacent node configuring the ring, and transmits a hop count report packet to the master node. The master node detects a fault on the ring based upon a fact that the master node does not receive the probe packet transmitted by itself. Further, the master node detects the occurrence of multiple faults and the recovery of at least one of the multiple faults based upon the sum of maximum values of hop counts in the hop count report packets received from both directions of the ring and transmits a packet for prompting the update of the database for packet transfer.

A method and apparatus for fault management in a network. A network architecture is disclosed including at least one network switch, several network interface cards, several processors (host) and a network manager. Fault reporting and detection logic is implemented in each switch and in each network interface card. In addition, multiple fault reporting pathways are provided for each switch and each network interface card. As a result, utilizing message exchanges such as Fibre Channel exchange messages, the switch, the network interface cards and the processors (host) are able to autonomously generate and report faults to the network manager. The combined fault reporting enables the network manager to more accurately isolate faults. In addition, because of the autonomous nature of the fault reporting, faults may be detected and corrected prior to the initiation of a communication session.

The invention relates to an on-orbit autonomous management system for a satellite and an autonomous management method. Through a method of innovatively leading a bus data monitoring unit and performing state data issuing through unified scheduling for a sub-system on each satellite, state information is directly collected to the bus data monitoring unit, and data extraction by a satellite mission central computer is not required. The bus data monitoring unit directly combines related information for forming alarm information and transmits the alarm information to the satellite mission central computer, thereby saving time in time-sharing acquisition remote sensing and state information data extraction, buying expensive time for treating the fault in time, and improving a fault treating efficiency. A distributed data management method is used for optimizing the operation process of the satellite mission central computer and can simultaneously treat multiple fault alarms which simultaneously occur, thereby realizing intelligent operation for sequencing according to the damage degree and emergency degree of the fault, and maximally ensuring in-time recovery of key system states such as satellite energy and satellite gesture when multiple faults simultaneously occur.

A system and method for combining conclusions from multiple fault detection techniques to isolate likely faults in a turbine engine is provided. The system and method provide the ability to effectively deal with multiple concurrent faults in the engine. Additionally, the embodiments of the invention provide the ability to correctly characterize multiple conclusions generated from evidence having different levels of interdependence. In one embodiment, the conclusions based on device data with high dependency are aggregated using a high dependency aggregation rule, and the resulting high-dependency sets are then further aggregated using a weak dependency rule. Finally, any conclusions based on independent evidence can be aggregated using an independent combination rule. The resulting aggregation determines which fault(s) are most likely indicated by the plurality of conclusions, taken into account the dependency of the device data used to generate the conclusions.

The invention discloses a hierarchical cluster aviation pump multiple fault diagnostic method based on frequency multiplication relative energy sum. In the method, firstly fault mode effect influence analysis technology is used for carrying out sensor optimal distribution on an aviation hydraulic pump and acquiring sensor signal; then common faults of the aviation hydraulic pump are classified by hierarchical cluster to determine the sequence and hierarchy of fault diagnosis; and finally fault diagnosis of a first diagnosis layer, a second diagnosis layer and a third diagnosis layer is carried out to complete the multiple fault diagnosis on the aviation pump. The method realizes the multiple fault diagnosis of the aviation pump with high accuracy; a extraction method of fault feature and frequency doubling relative energy sum is designed aiming that typical gradual fault of the aviation hydraulic pump is extremely weak at fault initial signature, the method can effectively realize the fault feature extraction of weak signal, and the method is proved to be accurate and efficient by plenty of experiments.

A fault location estimation system comprises single-fault-assumed diagnostic means that assumes a single fault and stores fault candidates, fault types, and detected error-observation nodes at which an error arrives from the fault candidates; error-observation node basis candidate classification means that classifies error propagating fault candidates into groups according to error-observation nodes using the fault candidates and the error-observation nodes and stores the groups as fault candidate groups; inclusion fault candidate group selection means that acquires a relation between each fault candidate group and a fault output, calculates an inclusion relation among the fault candidate groups, and, if path information on one fault candidate group includes path information on another fault candidate group, deletes the inclusion fault candidate group; inter-pattern overlapping means that calculates combinations of fault candidate groups that can reproduce a test result in all test patterns by referencing the fault candidates and the fault candidate classification result and extracts fault candidates that are common to fault candidate groups, calculated in multiple patterns, to create a new fault candidate group; and multiple-fault simulation checking means that selects fault candidates from the combinations of fault candidate groups, one from each fault candidate group, performs multiple-fault simulation, compares the simulation result with the test result, and outputs a combination of fault candidate groups which matches relatively well with the test result and has a relatively high fault possibility.

The invention discloses an oil-immersed transformer fault diagnosis method based on a fault probability distribution model, aiming to provide the probability of various faults of the oil-immersed transformer according to analysis of dissolved gas in oil. The oil-immersed transformer fault diagnosis method includes the steps of collecting massive analysis data of the dissolved gas in the oil of the transformer and normalizing the data, utilizing a Sigmoid function to realize probability output on the basis of a support vector machine, computing according to the collected massive analysis data of the dissolved gas in the oil to acquire optimal parameters of the support vector machine and the Sigmoid function, fusing the probability output of multiple support vector machines into probability output of multi-class problems, and obtaining a diagnosis result by referring to the maximum probability classification and standard deviation. The oil-immersed transformer fault diagnosis method based on the fault probability distribution model has high accuracy without error diagnosis, can be used for processing multiple fault conditions effectively and has high practicability.

The invention provides a method for diagnosing faults of a multi-model flight control system based on expected model expansion, comprising the following steps: making a statistic of various faults of the flight control system, and building a basic model collection; forecasting the probability of the multiple fault models at the current time, and building an expected model collection; combining the basic model collection with the expected model collection to build a fault model collection at the current time; filtering each fault model in the model collection at the current time, and updating the probability; if the probability of certain fault model in the model collection at the current time is more than or equal to the preset probability threshold value, judging that the flight control system has the fault corresponding to the fault model. The invention further provides a device for diagnosing the faults of the multi-model flight control system based on expected model expansion, comprising a basic model collection building module, an expected model collection building module, a model collection at the current time building module, a filtering and probability updating module and a fault judging module. The invention further provides a flight control system.

Device and method for the detection and/or diagnosis of faults in a process, equipment and sensors used to measure and control variables of a process, either for single faults or multiple faults. The detection and/or diagnosis are performed on the basis of residual calculation between measured values and values calculated by a plurality of parallel linear models, built up from the existing correlations between the measured variables by the process sensors and by different equations that rule the process, such as mass and heat balances, quality relationships, etc. A fault in a model (i.e. the obtainment of a residual anomaly high by comparing the model estimation and the measurement), increases the probability that some of the parameters or faults associated to that model are failing. When all models in which a participating parameter fail or show a high probability of failure, the anomaly rate of said parameters is maximum. Using said rates and a description of cause-consequence relationship between the parameters a fault probability is obtained, where the root cause of the problem is indicated, indicating the consequences produced in the process. The device comprises data storage means; pre-processing means for filtering data; means for generation and storage of multiple behavior models; residual calculation means for calculating the difference between the measured and the predicted values of the variables; analysis means for determining the need to communicate an anomalous situation; communication means for presenting a process status report.

The present invention is an apparatus and method that identifies and localizes fiber breaks or faults automatically, utilizing a fiber optic data transceiver that has μOTDR functionality. The transceiver of the present invention is a single wavelength bi-directional transceiver that during normal operation sends and receives optical data streams in the same wavelength window using any protocol and reports the distance to the fault or multiple faults nearly instantaneously when the transfer of data is disrupted, without the need to have fiber lines dedicated for this purpose or physically connect and reconnect each fiber line to check for faults and eliminates the need to map out the distance to the remote transceiver.

The invention discloses power transformer fault intelligent diagnosis technology, method and device, and relates to power transformer fault intelligent diagnosis technology and method and the intelligent diagnosis device based on the technology and the method. The intelligent diagnosis device comprises a data acquisition and transmission part and a management and display part. The intelligent diagnosis device is characterized in that power transformer fault intelligent diagnosis technology and method comprise a dissolved gas in oil analysis and diagnosis technology, a partial discharge online monitoring and diagnosis technology, an infrared temperature measurement diagnosis technology, a vibration analysis and diagnosis method, a frequency response analysis and diagnosis method, a winding temperature indication diagnosis method and a low voltage impulse response test diagnosis method, and fault intelligent diagnosis is carried out. According to the invention, a multi-parameter state monitoring method is used, and multiple fault intelligent diagnosis technologies and methods are integrated; valuable data which can be used to diagnose a fault and find out the position are given; the cause of the defect is eliminated; the shutdown time and the cost are reduced; and the purposes of power transformer intelligent monitoring and intelligent diagnosis are realized.

The invention discloses a fault segment location method for distribution network based on random forest algorithm, and belongs to the technical field of the distribution network and automation thereof. The method defines a feeder switch state of the distribution network and a feeder segment code, and determines an original fault sample set. Sample preprocessing is performed by oversampling and a feature selection method based on a learning model by considering the high dimensional imbalance characteristics of the original fault sample set. The method is based on the preprocessed sample set, proposes a random forest algorithm to mine the relationship between a fault attribute and a fault section, and constructs a fault location prediction model. The method achieves segment positioning of single and multiple faults in the distribution network, and has strong fault tolerance to distortion information.

The invention discloses an industrial process monitoring method based on a missing variable PCA model, so as to use a missing variable processing method to online estimate principal component information and realize the purpose of executing monitoring on estimation errors. Firstly, the principal component is estimated by assuming that each measurement variable data is missing one by one; and then, with the estimation error of the principal component and the error estimation value of the PCA model as monitored objects, online process monitoring is executed. Although sampling data under a normal working condition do not necessarily satisfy the Gauss distribution hypothesis, the estimation errors generally obey the Gauss distribution. From the point of view, although the method is based on the PCA algorithm, the method does not require hypothetical training data to obey or approximately obey the Gauss distribution, and the application range of the traditional PCA-based process monitoring method is extended to a certain extent. Besides, the method also has the advantage of a high multi-model generalization capability as multiple fault detection models are adopted.

The invention provides a novel fault injection method. According to the method, a platform for fault injection of an auxiliary power system is designed based on dSPACE, injection of a single fault and a plurality of faults can be configured freely, configuration management and parameter adjustment can be carried out fast through Simulink modeling and a Control Desk, a semi-physical simulation system is formed by an auxiliary power system multi-fault injection model and a physical electronic controller, the probability of generating multiple faults under a ground test and onboard environment is simulated through multi-fault injection, the defects of hardware and software in the system can be found fast in the earlier development process, then effective measures are taken, and the reliability of the system is improved.

The invention discloses an induction motor bearing fault diagnosis system and diagnosis method based on wavelet-spectral kurtosis. The induction motor bearing fault diagnosis system based on the wavelet-spectral kurtosis comprises a wavelet-spectral kurtosis analysis module, a time domain parameter statistical analysis module, an FFT analysis module, a Hilbert envelope demodulation analysis module, a spectral kurtosis analysis module, and the like; the wavelet-spectral kurtosis analysis module includes two data preprocessing methods of wavelet and wavelet packet, which can realize the identification and feature extraction of multiple faults in a motor bearing coupled fault vibration signal; the time domain parameter statistical analysis module monitors whether the motor bearing is abnormalor not by calculating kurtosis values of time domain sensitive parameters; and a spectrum analysis module and the Hilbert envelope demodulation analysis module mainly determine the feature extractionof the single bearing fault of the motor. The induction motor bearing fault diagnosis system based on the wavelet-spectral kurtosis realizes the integrated design of the induction motor bearing faultdiagnosis method, and can quickly and effectively process and analyze the single and composite fault signals of the motor bearing and obtain accurate diagnosis results.

The present invention discloses a multiple fault coupling wheel case analog experimental table in the field of rotating machinery vibration monitoring and diagnosing, a plurality of typical faults of the gear equipment can be comprehensively, objectively and conveniently simulated, such as eccentricity, pitting, stripping, uniform abrasion on the tooth surface, tooth root fatigue crack, tooth breaking and the like; through dialing the assemble arrangement result of the gear with the fork spreader converting mechanism, the coupling fault signal and the signal varying process of the gear from normalization to fault generation can be continuously observed in operation. Besides, by arranging fault on the bearing the bearing fault and a bearing coupling fault of the gear can be simulated. The researching proofing procedure for the fault diagnosing method by the scientific researcher is greatly facilitated, the time from theory to the practice is shortened and the time and energy spent for arranging the experiment by the scientific researcher are saved. Besides, the multiple fault coupling wheel case analog experimental table can also be applied in the teaching practice for improving the learning and recognizing level.

The invention discloses an independent integrity detection method of a receiver capable of simultaneously detecting and identifying multiple faults. The method comprises the steps of firstly determining a satellite position according to a navigation message, eliminating a satellite with a relatively low elevation angle, and determining an observation matrix which only contains a clock correction term; carrying out parity decomposition on the observation matrix to obtain a parity vector p and a multi-epoch cumulative parity vector P, calculating test statistics separately and carrying out weighting to obtain new test statistics sigma, and comparing the new test statistics sigma with a threshold T to obtain a fault detection result; and carrying out fault mode assumption according to the quantity of faults, and detecting the faults in various fault modes to identify multiple faults. The method is simple, the detection and recognition rate is high, abrupt faults and multiple faults can be detected and recognized, and the method also has a very high detection and recognition rate on small soft faults.