254results about How to "Implement fault detection" patented technology

The invention discloses a serial arc-fault circuit interrupter, comprising a power circuit, a current sensor, a current sensing circuit, an amplifying and filtering circuit, a voltage zero-crossing comparison circuit, a microcontroller, a trip trigger circuit. A current transformer collects current signals in a circuit; the current signals are converted into voltage signals through the current sensing circuit, and are input into the microcontroller through the amplifying and filtering circuit; the voltage zero-crossing comparison circuit inputs a voltage zero-crossing pulse signal into the microcontroller; the microcontroller judges arc emergence through arc characteristics analysis and is disconnected with a load circuit by the trip trigger circuit. The protection method transforms a one-dimensional time signal into a two-dimensional time signal by utilizing short-time Fourier transform, analyzes changes of fundamental component, even-order harmonic and odd harmonic component of eachcyclic wave, extracts and judges the serial arc-fault characteristics. The method has the advantages that multiple characteristics are extracted to realize comprehensive judgment of fault arc, missedtrip and balk generated in serial arc detection in small load are prevented, and the reliability of judgment is improved effectively.

The invention discloses a fault location method based on a residual and a double-stage Elman neural network for a hydraulic servo system, comprising the following steps of: obtaining the input / output signals of the hydraulic servo system in a normal working state, an electronic amplifier fault state and a leakage fault state, training a fault observer by virtue of the input / output signal in the normal state, and obtaining a real-time residual signal by the fault observer at first, and then training a state follower in real time and on line to obtain a network connection weight corresponding to the real-time signal, and training an RBF (radial basis function) fault locator by using the time-domain characteristic value of the residual signal and the network connection weight as the training input samples of the RBF fault locator. Both of the fault observer and the state follower are realized by the improved Elman network. Whether the system has a fault or not at present can be judged by comparing the time-domain characteristic value with a fault threshold, and the type of the fault can be obtained by the fault locator. The fault location method disclosed by the invention realizes fault location for the hydraulic servo system, and has high location accuracy and engineering applicability.

The invention discloses a fan fault detecting method based on a depth belief network model. The fan fault detecting method comprises that a fan flow forecast model is established by using depth beliefnetwork, state parameters related to the fan flow are selected to be used as the input variable of the model, and the flow of a fan is forecasted. The depth belief network is formed by staking multilayer continuous type limit Boltzmann machines, and an adaptive step length method is used to accelerate the algorithm training process. In addition, residual distribution characteristics are calculated according to a sliding window, when the mean value or standard deviation of the residual exceeds the threshold, alarm is issued. By means of the fan fault detecting method based on the depth beliefnetwork model, the flow of the fan can be accurately forecasted, meanwhile, the abnormal working state of the fan can be detected, and fault detection on the fan is achieved.

The invention relates to an autopilot flight control system. The autopilot flight control system comprises a flight control panel, a radio station, a servo mechanism, a receiver and a remote controller, wherein the radio station is communicated with the flight control panel in a two-way mode on remote control and telemetering, the servo mechanism receives servo control instructions of the flight control panel, the receiver transmits manual control instructions to the flight control panel, and the remote controller transmits remote manual control instructions to the receiver. The flight control panel further comprises a central processing module, a communication module which communicates with the central processing module in a two-way mode, a sensor module which communicates with the central processing module in the two-way mode, and a servo output module which receives instructions of the central processing module, wherein the communication module of the flight control panel receives instructions of the receiver and communicates with the radio station in the two-way mode. The radio station communicates in the two-way mode with a ground control system outside the autopilot flight control system. The servo output module transmits servo information to the servo mechanism. The autopilot flight control system is small in structure, simple and clear in system design, high in computation efficiency and reliability and very good in expansibility and portability.

The invention discloses an intelligent LED street lamp monitor system based on the ZigBee and Wi-Fi technology. The intelligent LED street lamp monitor system comprises an upper computer, a wireless router, a Wi-Fi module, a ZigBee coordinator and a plurality of sets of monitor terminals, wherein the upper computer is in wireless communication connection with the Wi-Fi module through the wireless router; the Wi-Fi module is in bidirectional communication connection with the ZigBee coordinator through a serial port; the ZigBee coordinator is in wireless communication connection with each ZigBee terminal. Each set of monitor terminals at least comprise one ZigBee terminal, an LED street lamp driver, an LED street lamp and a detection module, wherein the ZigBee terminal is connected with the LED street lamp driver, the LED street lamp and the detection module sequentially in series. The intelligent LED street lamp monitor system has the advantages of energy conservation, intelligent control, convenient maintenance, easy on-spot wiring, low cost, high comprehensive function and the like.

The invention discloses a principal component tracking based industrial fault monitoring method and application thereof, and belongs the technical field of industrial process monitoring and diagnosing. The method includes decomposing industrial collected data into a low rank matrix including complete process operation information and a sparse matrix including sensor noise and process faults according to a principal component tracking method; performing fault detection in the low rank matrix by the aid of a statistical magnitude T2, and performing fault detection in the sparse matrix by the aid of a statistical magnitude of a mean value correlation coefficient. Compared with the prior art, the principal component tracking based industrial fault monitoring method has the advantages that according to the characteristic that the matrixes are decomposed by the principal component tracking method, different statistical magnitudes are adopted for the matrixes with different characteristics, effective information included in the data are fully utilized, and accordingly, high accuracy in industrial fault detection is achieved.

The invention provides an unknown input observer based realization method of fault diagnosis for a minitype unmanned aerial vehicle distributed formation. According to a hierarchical concept of a distributed control system, controller designs of the single unmanned aerial vehicle and multi unmanned aerial vehicle formation are studied to ensure the flight stability of the minitype unmanned aerialvehicle distributed formation. When a fault of a single unmanned aerial vehicle final controlling element occurs, an effective unknown input observer distributed system fault detection method is provided, which completely decouples interference and completely eliminates adverse effects of the external interference on a fault diagnosis process. Furthermore, a method to estimate the fault by extending a state vector and a fault vector into an augmented vector is provided. According to the invention, the conventional unknown input observer is combined with an augmented system to realize fault detection, separation and estimation of the minitype unmanned aerial vehicle distributed formation. According to known minitype unmanned aerial vehicle model parameters, a numerical simulation can be established to carry out fault diagnosis on the final controlling element. The fault diagnosis method is used for fault diagnosis of a minitype unmanned aerial vehicle distributed formation.

The invention discloses a fault processing method of network equipment. The method comprises the following steps of: reading operation state information of each single board in the network equipment; generating and storing a log file of the corresponding single board according to the operation state information of each single board in the network equipment; reading and analyzing the log file in real time or according to trigger of a user; and performing fault detection and analysis on the corresponding single board according to the log file. In the fault processing method of the network equipment, provided by the invention, fault detection of the network equipment is realized by automatically reading and statistically analyzing data of a key position, a recoverable fault can be automatically recovered, and an unrecoverable fault is reported to a network administrator, so that the participation of the administrator can be avoided in an extremely short time, common equipment faults can be eliminated under the condition that a user does not sense, and fault time is greatly shortened.

A distribution network protection method taking the randomness of a photovoltaic power station into consideration mainly solves the problems that conventional distribution network protection equipment is seriously affected in protection range, selectivity, sensitivity and reliability after being accessed into a distributed power supply. The method comprises the following steps: dividing the distribution network into areas, and real-timely monitoring the current output by each area; building a dynamic Thevenin equivalent parameter model of the distributed power supply; detecting faults of the distribution network; positioning and separating the fault area; and reclosing to resume power supply. The method is not affected by the access number, capacity and positions of the distributed power supplies, such as the photovoltaic power station, can realize fault detection, fault area positioning and separating, reclosing and power supply resuming quickly and reliably, is not only suitable for the requirement of large-capacity DG injection in the future and can reliably work when large-capacity DG is injected, but also suitable for the distributed power supply with large output random variation, and has the characteristic of shorter time in cutting off instant faults.

The invention discloses a network abnormal event sensing method and system. The network abnormal event sensing method comprises the following steps: inserting INT detection headers into all messages,sampling the messages, and labeling the INT detection headers of sampling messages; INT related operation is carried out on the message, the INT related operation comprises adding INT metadata to themessage, and the INT metadata records state information when the switch processes the message and is added behind the INT detection header; Screening a message containing the INT metadata of the at least one hop switch, and copying and uploading the message; Carrying out preliminary summarization on the uploaded message to generate a sub-event; And further summarizing the generated sub-events to obtain a final summarization of the events. According to the network abnormal event sensing method and system, full-amount, accurate and real-time network events are obtained, and the method and systemare used for fault diagnosis, topology assessment and the like.

The invention discloses a distribution network single-phase ground fault detection method using transient and steady feature fusion. When single-phase ground faults occur under different fault conditions of a resonant ground distribution network system, the steady and transient features of a normal feeder line and a fault feeder line have different time-frequency distribution characteristics, thetransient features are obvious under strong faults, and faults in the distribution network are accurately and reliably detected by using fault transient zero-sequence current time-frequency characteristics. Under weak faults, fault detection is achieved by a zero-sequence current steady quantity feature after the faults. The method solves the technical problem in the prior art that transient zero-sequence current sudden change amount, energy, amplitude and polarity are used for the transient quantity fault line selection method so as to improve line selection effects to different extents; however, after a ground fault, the effect of an arc suppression coil makes the zero-sequence current weak, especially when the intermittent arc grounding occurs, the arc extinguishing is uncertain such that the detection method in the prior art is no longer reliable.

The embodiment of the invention discloses a vehicle fault detection method and device, a vehicle and a storage medium. The method comprises the following steps of obtaining a vacuum pump starting state, a braking pedal state, the vacuum pressure of a vacuum pump sensor and the environmental pressure of an environment sensor; according to the vacuum pump starting state and the braking pedal state,determining a preset pressure change tendency, and according to the vacuum pressure and the pressure change tendency, detecting the faults of the vacuum pump sensor; and when the vacuum pump sensor isunder a normal working state, obtaining pre-stored vacuum pump ultimate pressure, and according to the ultimate pressure and the environmental pressure, detecting the faults of the environment sensor. By use of the technical scheme of the embodiment of the invention, the fault of a vacuum system is detected when the voltage of the vacuum sensor is under a normal range, the fault detection accuracy of the vacuum pump system is improved, and vehicle driving safety can be enhanced.

The invention discloses a fault detection method based on a dimensional variable type independent component analysis model. The method comprises the following steps: in an offline modeling phase, according to differences of values of various column vector elements of a separation matrix in a traditional independent component analysis (ICA) model, various variables are firstly and correspondingly endowed with different weights to reflect differences of dimensions; afterwards, because each column vector in the separation matrix represent a difference of the corresponding measurement variable in a projection direction, the dimensions have a plurality of variable forms, and a plurality of ICA fault detection models can be established correspondingly; and, when online monitoring in performed, the multiple ICA models are called to calculated corresponding monitoring statistics, and a final probability type monitoring index is obtained by utilizing Bayesian reasoning in order to provide convenience for fault decision-making. Compared with a traditional method, the method is advantageous in that modeling considers uncoordinated importance of the measurement variables, and fault detection is implemented by utilizing the multiple modes at the same time. According to the method, description for a normal data characteristic is comprehensive, and an excellent fault detection effect is obtained through utilization.

The invention belongs to the field of control, and provides a method and system for detecting and controlling a network control system. Based on a continuous time system model of a controlled object, a controller acquires output signals collected through a sensor and passing through an analog-digital converter and the sampling time delay of network transmission, and the controller and a fault detector are designed. The fault detector detects fault detection signals, and meanwhile the controller outputs control signals to achieve closed-loop control over the controlled object. Therefore, the method and system for detecting and controlling the network control system avoids the model error brought by discretization modeling, fault information is taken into consideration with the design of an initiative fault-tolerant controller, and the needs of detecting faults accurately of the fault detector are further taken into consideration. Preferably, a cone complementarity linearization technology is called in the integral design process of the fault detector and the controller depending on a time-lag network control system, the solution of designing the controller is translated into a nonconvex optimization problem under a linear matrix inequality, and conservative properties are reduced greatly compared with lag irrelevance. Therefore, the method and system for detecting and controlling the network control system achieves fault detection and fault tolerance control.

The invention relates to a fault detection method of an unmanned aerial vehicle flight control system. Online detection of the fault of the actuator and the sensor of the fixed-ring unmanned aerial vehicle flight control system can be realized by the method. An unmanned aerial vehicle flight control system continuous nonlinear fault model is established on the basis of the unmanned aerial vehiclekinetic equation and the fault type; and then the unmanned aerial vehicle flight control system continuous nonlinear fault model is converted into an unmanned aerial vehicle flight control discrete time-varying fault model with the help of a nonlinear observer, and unmanned aerial vehicle flight control system fault detection is realized by using an equivalent space method. The robustness indicator is designed by aiming at the problem of poor robustness of the conventional equivalent space method under unknown interference so as to enhance the robustness of system fault detection. The extendedKalman filtering equation is utilized in the Krein space to realize recursive computation of the residual evaluation function to reduce the computational burden by aiming at the problem of high computational burden of online fault detection of the conventional equivalent space method. According to the method, quick alarm of the fault of the sensor and the actuator of the unmanned aerial vehicle can be realized so as to provide an effective basis for fault detection of the unmanned aerial vehicle flight control system.

The invention discloses a method, a system and a medium for detecting faults of a modular multilevel converter sub-module. The detection method comprises the following steps of: aiming at a target bridge arm of the modular multilevel converter, sampling a voltage transformer of the target bridge arm at the beginning of a kth control period to obtain a voltage sampling value; obtaining a theoretical calculation value of the sum of output voltages of all submodules of the target bridge arm at the beginning of the kth control period; determining a fault judgment basis lambda according to the difference between the sampling value and the theoretical calculation value of the voltage transformer; and judging whether the absolute value of lambda is less than or equal to a preset threshold value so as to judge whether the target bridge arm has a sub-module fault or not, and the sub-module with the fault can be further positioned. According to the invention, the fault detection of the sub-module can be realized on the basis of not adding other hardwares under the condition that a single bridge arm of the modular multilevel converter is only provided with one voltage transformer.

The invention discloses a fault detection device and method of a finished automobile controller. The method comprises that a communication module writes a diagnosis mode program in the finished automobile controlled in a detection scene, writes a work mode program in the finished automobile controlled in a work scene, and provides data communication between the finished automobile controller and a host computer controller in the detection process; the host computer controller determines and emits a detection instruction according to a channel between each pin and a microprocessor, and determines the fault type and position according to detection result signals from the communication module or a signal processing and collecting module, corresponding detection instruction and the corresponding pins; and the signal processing and collecting module receives the detection instruction emitted by the host computer controller, generates a detection signal, sends the detection signal to corresponding pins at the input end of the finished automobile controller, collects the detection result signals from the finished automobile controller, and returns the detection result signals to the host computer controller. The device and method of the invention can improve the accuracy and efficiency of fault detection.

The invention discloses an overhead flexible direct current power grid fault detection method based on current limiting reactor voltage difference. The overhead flexible direct current power grid fault detection method based on the current limiting reactor voltage difference comprises the steps that (1) direct voltage change rates of each of direct current lines in a power grid are continuously monitored, and when the direct voltage change rates are greater than a preset first threshold value, turning into a step (4) is carried out; (2) at one ends of the direct current lines, voltage time products of positive pole current limiting reactors and negative pole current limiting reactors within a target time period are correspondingly obtained and specific values are determined so as to carryout fault pole selection; (3) the sum of the voltage time products of the positive pole current limiting reactors and the negative pole current limiting reactors is obtained, and a second threshold value corresponding to a fault type is obtained, if the sum of the voltage time products is greater than the second threshold value, an occurred fault is determined as a fault inside a zone; if not, theoccurred fault is determined as a fault outside the zone or no fault; and (4) at the two ends of the direct current lines, the step(2)-step(3) are correspondingly carried out to achieve the fault detection. The overhead flexible direct current power grid fault detection method based on the current limiting reactor voltage difference has a high tolerating transition resistance capacity and a highcapacity of resisting disturbance, and can identify various fault types.

The invention discloses a power cable fault positioning platform, including a cable fault detector arranged on a power cable, a signal back-transmission mechanism, a mobile monitoring station and a mobile monitoring unit. The mobile monitoring unit comprises an overhead track and a mobile mechanism wherein the mobile mechanism is provided with a monitoring terminal; the signal back-transmission mechanism comprises a communication line arranged in a cable conduit and a number of wireless receivers; the fault detector is provided with a first wireless communication module; the monitoring terminal comprises a control module, a second wireless communication module, a storage module, a sensor module, a video monitoring module and wireless emitters arranged correspondingly to the wireless receivers; and the mobile mechanism is provided with a plurality of fault positioning mechanisms through an ejection mechanism. The platform of the invention performs comprehensive monitoring to the cable fault through the real-time acquisition of online current by the fault detector and through the fault point positioning, allows the workmen to quickly find the specific fault position so as to realize the rapid maintenance to the fault point.

The invention discloses a fault detection method and a device, including that a detection message is transmitted to a second device according to the preset message transmit cycle; the source address and the destination address of the detection message are the device address of the device which transmits the detection message; the next hop address is the address of the second device; when no detection message returns within the preset detection cycle, the communication between the device which transmits the detection message and the second device fails. In the fault detection method and the device, both of the BFD session sides are not needed to support the BFD bilaterally; the BFD session can be created on one side for the BFD fault detection, realizing the fault detection between the device which supports the BFD and the device which can not support the BFD.

The invention discloses a power cable fault monitoring platform. The power cable fault monitoring platform comprises a fault detector arranged on a cable line, a signal return mechanism arranged in a cable duct, a monitoring center and a movable monitoring unit connected with the monitoring center, wherein the movable monitoring unit comprises an overhead track formed in the cable duct, a moving mechanism arranged on the overhead track and a monitoring terminal arranged on the moving mechanism, the signal return mechanism comprises a communication line arranged in the cable duct and a plurality of data interface modules formed in the communication line, and the fault detector is provided with a first wireless communication module. The fault detection of the whole cable and monitoring environment information and device information in the cable duct can be achieved only by using a small number of sensors, an online current mode is acquired in real time through the fault detector to conduct comprehensive monitoring on line faults, and fault judgment is more timely and accurate.

The invention provides an MOSFET (Metal-Oxide -Semiconductor Field Effect Transistor) parallel current-sharing circuit based on fault diagnosis. The MOSFET parallel current-sharing circuit comprises a main control unit and at least two paths of MOSFET parallel branch circuits, wherein the main control unit is connected with each path of MOSFET parallel branch circuit through a fault detection unit; each MOSFET parallel branch circuit is used for finishing signal acquisition of each branch circuit; the main control unit is used for judging whether the working of the branch circuits is normal or not according to an acquired signal and sending a corresponding driving signal to each branch circuit; the fault detection unit is used for carrying out overcurrent judgment according to a received current signal, and determining a finally output driving signal according to an overcurrent judgment result, a received driving under-voltage signal and a driving signal integrity detection signal. According to the MOSFET parallel current-sharing circuit, each MOSFET parallel branch circuit has the independent driving signal and the independent fault detection unit, and whether branch circuit voltage, temperature and gate driving are under-voltage and the driving signal is complete or not can be detected. By adopting the MOSFET parallel current-sharing circuit, parallel branch circuits current sharing is realized and fault detection of the parallel branch circuits is also realized; the reliability is higher when the plurality of parallel branch circuits works.

The invention discloses an integrated directional non-contact cable fault detection positioning sensing device which comprises a magnet ring, two conductor loops, an insulting outer sleeve and a cushioning pad. The magnet ring, the conductor loops, the insulting outer sleeve and the cushioning pad are of an annular tubular type structure in main body. The device is formed by two symmetrical semi-circular structures respectively in a tight connection mode. The magnet ring and the two conductor loops are tightly embedded into the insulting outer sleeve in sequence, and the cushioning pad is used for being filled between the magnet ring and the conductor loops and a cable to be measured. According to the integrated directional non-contact cable fault detection positioning sensing device, a tubular type capacitor formed by the conductor loops and the cable to be measured is used so that non-contact coupling transmission of high-frequency signals in a cable fault positioning device through a reflection method is achieved, and electrical connection between the fault positioning device and the cable to be measured is avoided; by the adoption of the magnet ring, the inductance of the section of the cable to be measured is strengthened, the signal transmission direction can be controlled, and thus directional detection of the cable to be measured is achieved; the device can be applied to fault positioning of a complex cable network system and has the advantages of being convenient and fast to mount, high in security and the like.

The invention discloses a method for detecting faults of an integrated circuit. The method comprises the following steps of: obtaining an output probability density function (of each element) within a nominal parameter tolerance range through Monte Carlo simulation one by one; respectively calculating together with an output probability density function of a tested integrated circuit under each element nominal parameter so as to obtain a mutual entropy value, wherein the maximum value of the mutual entropy value is used as the maximum mutual entropy of the circuit, and the minimum value of the mutual entropy value is used as the minimum mutual entropy of the circuit; subsequently calculating an actual measurement output probability density function of a non-fault tested integrated circuit with an actual measurement output probability density function of an uncertain fault tested integrated circuit so as to obtain an actual measurement mutual entropy value; and finally comparing the actual measurement mutual entropy with the maximum mutual entropy and the minimum mutual entropy of the circuit so as to determine whether the integrated circuit has faults or not. Compared with the prior art, the method has fewer testing nodes and is simple and convenient in testing process, low in testing cost and insensitive to noise.

The invention relates to an information fusion-based series battery pack SOC (state of charge) estimation method and belongs to the technical field of battery management. The method comprises the following steps that: S1, a series battery pack to be tested is selected, the technical parameters of the series battery pack to be tested are acquired, and the CMM+CDM model of the series battery pack isestablished, and model parameters required by the SOC estimation of the series battery pack are determined; S2, a trickle current charge / discharge test and an HPPC (hybrid pulse power characterization) test are carried out, so that a database of charge / discharge OCVs (open-circuit voltage) and circuit model parameters with respect to SOCs is established, and a DST (dynamic stress test) and a UDDSare simulated, and a working condition test experiment database is established; S3, an OCV-SOC relation curve is obtained, and the electrical characteristic parameters of the CDM+CDM model are obtained; and S4, the CDM+CDM model is combined with a decentralized filter, and a CMM and the SOCs of single cells are obtained on the basis of an optimal algorithm and the quantitative relation of the charge / discharge characteristic parameters with respect to the SOCs, and the SOCs of each cell are fused through weight distribution, so that the SOC of the series battery pack is realized.

The application relates to a digital radio-frequency signal device. The digital radio-frequency signal device comprises a monitoring device and a signal conversion device; and a control terminal is connected to a digital processing device and a loop switching device of the monitoring device separately. The loop switching device is used to sequentially control the self-loop of the digital processing device and the signal conversion device according to the control of the monitoring device. The digital processing device is used to perform self-inspection on a first self-loop when the loop switching device controls a transmitting port and a receiving port to be connected to form the first self-loop link according to the control of the monitoring device, and is used to perform self-inspection on a second self-loop link when the loop switching device disconnects the self-loop of the digital processing device and controls a downlink output port and an uplink input port of the signal conversion device to be connected to form the second self-loop link, so as to generate respective self-inspection results corresponding to the self-loop links. The monitoring device is used to determine whether the respective self-loop links are faulty or not according to the respective self-inspection results. Therefore, the efficiency of signal link self-inspection is improved.

The invention discloses a winding fault locating method for a power transformer based on fusion of the deep convolutional neural network and visual identification. The method comprises the steps of 1)enabling a power transformer to be equivalent to a two-port network, establishing a winding equivalent circuit, and calculating a transfer function of the winding equivalent circuit; 2) for a circuitwith normal circuit parameters, setting a sine wave excitation source with frequency change at one end of a power transformer winding, and obtaining an amplitude-frequency characteristic curve of each section of winding nodes in the normal state; 3) carrying out sweep frequency response analysis on the circuit in various fault states to extract amplitude-frequency characteristics, and calculatingto obtain the amplitude-frequency characteristics of each section of winding node; 4) establishing a characteristic matrix for the acquired amplitude-frequency characteristics; 5) carrying out frequency sweep response analysis on the to-be-diagnosed power transformer to form a characteristic matrix, (6) converting the characteristic matrix into an image, taking simulation and historical detectiondata as a training set, taking fault types and positions as labels, and inputting the training set and the labels into the deep convolutional neural network for training, and (7) carrying out fault classification and location on the to-be-diagnosed transformer.

The invention discloses a grey fault diagnosis method applied to a telecom bearing network. The grey fault diagnosis method comprises the following steps: step 1, acquiring information of all paths inthe whole telecom bearing network; step 2, sending UDP detection packets on all the paths to measure the packet loss conditions of the paths; and step 3, for each interface, link and equipment in allthe paths, respectively analyzing the packet loss condition of all the paths passing through the interfaces, links and equipment, and diagnosing the gray fault occurrence position in the telecom bearing network according to the packet loss condition. The method does not need any hardware update, can be quickly deployed in a telecom bearing network environment, and can quickly find and position gray faults in the network.

The invention discloses a method and a device for detecting a multi-link fault. The method comprises the steps as follows: under the situation that a plurality of communication links exist between a transmitting apparatus and an opposite terminal receiving device, the transmitting apparatus transmits a detection message according to a preset first period at fixed time, copies the detection message to all links communicated with the opposite terminal receiving device and simultaneously sends the detection messages; the opposite terminal receiving device counts the quantity of the detection messages received on each link according to a preset second period, and collects the quantity of the detection messages received on each communication link when the second period is finished; the opposite terminal receiving device judges whether the quantity of the detection messages on each link counted within the detection period is more than or a set threshold Ccmp, if so, whether the link state is normal is judged, or else, the communication link is judged to be in failure. By the method and the device, a multi-link fault detection mechanism between a plurality of network devices is simple, and the system overhead is small.

The invention discloses an aircraft fault tolerant navigation estimation method of analytic redundancy. The method comprises the following steps: firstly estimating acceleration and angular acceleration information of the aircraft by utilizing a kinetic model of a rotor aircraft; secondly, constructing an angular accelerometer / kinetic model / IMU fault detection filter and an IMU / magnetic sensor / GPS / barometer fault detection filter; and then constructing a fault positioning strategy of two fault detection filters, thereby realizing the fault positioning on the navigation sensor; and finally, isolation the fault sensor according to the fault detection result, thereby accomplishing the navigation information resolving. By importing the kinetic model and the angular accelerometer, the fault detection on the angular accelerometer, the kinetic model, the IMU and the measurement sensor is realized, and the kinetic model can be used for completely replacing the fault IMU to perform navigation resolving, and the fault tolerance performance of the navigation system is improved.