85 results about "Fault detection algorithm" patented technology

The invention relates to a method for testing high impedance faults, which is based on transient traveling waves and belongs to the electric system protecting and controlling technique field. The method comprises faults actuating and faults testing, wherein the faults actuating adopts transient traveling waves electric quantity to judge actuating, The faults testing is actuated through comparing the transient traveling waves electric quantity and a threshold value. The fault testing adopts fault components to judge, faults testing results are output through an algorithm of fault detection. Three algorithms are adopted in high-resistance fault judgments, which include a high frequency algorithm, a harmonic algorithm, and a fundamental wave algorithm. The three algorithms can be used in either a single end form or in combination to judge high-resistance actuating. The method can test thoroughly each main frequency band information which may be produced when high resistance faults occur.

A combined arc fault circuit interrupter and leakage current detector interrupter. A current sensor, amplifier, and comparator are employed to detect the presence of leakage current in alternating current power conductors. Upon the detection of an amount of current leakage beyond a threshold level, a relay is opened, disconnecting a source of AC power from a power conductor being monitored, such as an appliance line cord. An arc sensor, envelope detector, amplifier, and microcontroller are employed to detect the presence of an arc fault in the alternating current power conductors. The arc fault detection algorithm implemented by the microcontroller is capable of discriminating between high frequency noise which is not caused by a parallel or series arc fault, with high frequency anomalies which are the result of arcing.

Systems and methods are disclosed herein for verifying the quality of global navigation satellite system (GNSS) measurements. The system includes a GNSS receiver, a wireless communications device, and a fault detection processor. The GNSS receiver includes a GNSS antenna for receiving signals from a plurality of global navigation satellites and a processor for calculating a ranging measurement for each of the global navigation satellites from the GNSS receiver to the global navigation satellite. The wireless communications device receives ranging measurements from at least one other GNSS receiver. The fault detection processor performs a fault detection algorithm to determine if there is an anomaly affecting the ranging measurements of the GNSS receiver and the at least one other GNSS receiver.

The invention discloses a fault-tolerance autonomous navigation method of a multi-sensor of a high-altitude long-endurance unmanned plane. The method comprises the following steps of: firstly, carrying out theory analysis on work environment and work characteristics of three navigation sensors including GPS (Global Position System), CNS (astronomy) and SAR (Synthetic Aperture Radar), and establishing an observation linearity measurement equation by combining a geography system based on a position combined observation principle of inertia / GPS, inertia / astronomy and inertia / SAR under an airborne geography system; then analyzing error characteristics of a navigation sensor and simulating the output during the fault of GPS, and establishing a corresponding fault detection algorithm unit to carry out fault detection and isolation on a filter; and finally, designing and completing an inertia / GPS combined navigation system mathematic model based on the assistance of astronomy and SAR, and optimally evaluating the error state of the inertia navigation by means of federated filtering. The invention has high navigation precision, and can fully play the role of evaluating the error state quantity of an airborne inertia navigation system by the combined navigation of the multi-sensor under the geography system.

The invention discloses a parameter identification based photovoltaic array fault diagnosis method. The method includes: identifying relevant parameters of a photovoltaic module according to a parameter identification algorithm; acquiring an output characteristic model of the photovoltaic module under different operating conditions; adopting a fault detection algorithm for fault judgment of a photovoltaic group; acquiring an optimal irradiance So and an optimal module temperature To from data acquired by information acquisition equipment, and inputting into the output characteristic model to obtain an optimal output characteristic equation of the current photovoltaic module, inputting branch current acquired by the information acquisition equipment into the optimal output characteristic equation to obtain an optimal output voltage of a current group, comparing with an actual voltage value of the group, and determining that the group has a hard shadow fault if a difference value exceedsa threshold for consecutive F times; judging a fault type according to a fault judging algorithm; displaying three-section positioning data and operating data of the faulted group on a user interfaceof an upper computer to instruct workers to timely check and maintain the photovoltaic module in fault.

Systems and methods are described that facilitate using end-user feedback to automatically distinguish between a normal behavior and a device failure which can be a hard failure (e.g., a device malfunction) or a soft failure. For instance, upon detection of a usage switch from a first device to a second device by a user, a survey message is sent to the user to solicit information regarding the reasons for the switch. If the switch was triggered by a device malfunction, the detected device failure is verified and an alert is sent to an administrator and / or potentially impacted users. If the switch was triggered by the user's need for functionality (e.g., color printing, collation, etc.) not provided by the first device, which is otherwise functioning properly, then the detected failure is determined to be a failure and the failure detection algorithm is updated accordingly.

A method of countering side-channel attacks on an elliptic curve cryptosystem (ECC) is provided. The method comprises extending a definition field of an elliptic curve of the ECC to an extension ring in a first field; generating a temporary ciphertext in the extension ring and countering attacks on the ECC; and generating a final ciphertext for the first field if a fault injection attack on the ECC is not detected. The countering of attacks on the ECC may comprise countering a power attack on the ECC. Checking if there is a fault injection attack on the ECC may be performed by determining if the temporary ciphertext satisfies a second elliptic curve equation. The fault detection algorithms takes place in a small subring of the extension ring, not in the original field, to minimize the computational overhead. The method can improve the stability of the ECC and reduce computational overhead of the ECC.

The invention relates to an arc fault detection method and a protection device. The arc fault detection protection device comprises a current transformer, an I / V conversion circuit, a rectifying circuit, an integral circuit and a microprocessor, wherein the I / V conversion circuit, the rectifying circuit, the integral circuit and the microprocessor are connected in sequence. The current transformer detects current signals of a protected circuit, current signals are converted into voltage signals through the I / V conversion circuit arranged at the secondary side of the current transformer, the voltage signals are sent into the rectifying circuit to be rectified through a voltage follower, wave signals obtained after rectification are provided for the integral circuit, the integral circuit provides the voltage level cumulant in a preset time period for the microprocessor, the microprocessor captures the periodical integral value through timing A / D conversion and stores the periodical integral value, the current waveform change of the protected circuit is detected through an arc fault detection algorithm, arc fault judgment is conducted, disturbance caused by arc fault, or caused by normal starting of a load or caused by power regulation can be effectively distinguished, mistaken disengagement actions of the device can be reduced, and reliability is higher.

The invention relates to a fault detectable degree analytical method for an unmanned aerial vehicle flight control system. Unmanned aerial vehicle fault detection algorithm design and sensor configuration need to quantitatively analyze the difficulty level of fault detection. An unmanned aerial vehicle flight control system model is established according to an unmanned aerial vehicle closed loop nonlinearity flight control system and fault types; the flight process is segmented according to different flight states of an unmanned aerial vehicle, and small perturbation linearized models are established at all periods; faults and derivatives of the faults serve as extension states, and a dimension expansion piecewise linear time-invariant system model is established; a state observable degree index based on singular value decomposition serves as a quantitative evaluation index of fault detectable degree, according to the quantitative evaluation index, the difference of detectable degrees of the faults of the unmanned aerial vehicle in different flight states is quantitatively analyzed from the fault estimation view, and a reference basis is provided for the fault detection algorithm design and unmanned aerial vehicle sensor configuration.

The invention discloses an improved PSO and SVM based encoder fault diagnosis system and method. The system is characterized in that one path of an output value of a PID controller is inputted into animproved PSO-SVM prediction model, an acceleration value measured by an inertial navigation module is outputted to the improved PSO-SVM prediction model, an output value of the improved PSO-SVM prediction model is divided into two paths, one path is outputted to a fault-tolerant switching mechanism module, a difference of the other path and an actual speed measurement value of an encoder is madeto obtain a residual signal, the residual signal is detected in real time according to an SPRT fault detection algorithm, the fault-tolerant switching mechanism module cuts off a measurement value ofthe failure encoder when the encoder is detected to have a fault and replaces the actual measurement value of the encoder with a predicted output value of the improved PSO-SVM prediction model to serve as a feedback signal which is fed back to the PID controller; and after the system returns to normal, the fault-tolerant switching mechanism module uses the actual measurement value of the encoder as a feedback signal which is fed back to the PID controller, thereby finally achieving a purpose of soft closed-loop safety fault-tolerant control.

The invention discloses implementation of an H- / Hinfinity robust fault detection algorithm for pure electric car window anti-pinch, which comprises the following steps: (1) constructing a pure electric car window motor control model on the basis of fault diagnosis theories of an analytic model; (2) designing an optimal robust fault diagnosis observer based on H- / Hinfinity performance index, and carrying out iteration solution by using an LMI (linear matrix inequality); and (3) designing thresholds and anti-pinch fault occurrence determination standards according to the obtained optimal performance index. The window motor mathematical model is solved by using the H- / Hinfinityperformance index optimizing algorithm, thereby better adapting to the system complexity and noise disturbance uncertainty, and well inhibiting noise, external disturbance and other factors, so that the mistaken anti-pinch probability is reduced, and the sensitivity for anti-pinch fault is higher; and thus, the mistaken anti-pinch fault can be detected in time 0.15 second after the anti-pinch occurs, thereby enhancing the use safety of the pure electric car window to some extent.

Systems and methods are described that facilitate using end-user feedback to automatically distinguish between a normal behavior and a device failure which can be a hard failure (e.g., a device malfunction) or a soft failure. For instance, upon detection of a usage switch from a first device to a second device by a user, a survey message is sent to the user to solicit information regarding the reasons for the switch. If the switch was triggered by a device malfunction, the detected device failure is verified and an alert is sent to an administrator and / or potentially impacted users. If the switch was triggered by the user's need for functionality (e.g., color printing, collation, etc.) not provided by the first device, which is otherwise functioning properly, then the detected failure is determined to be a failure and the failure detection algorithm is updated accordingly.

The invention relates to a wireless sensor fault detection algorithm based on node pre-states. For each node in a network, firstly according to a characteristic of time correlation, a state of the sensor node is preliminarily diagnosed by processing historical data of the sensor node; and then according to a spatial correlation principle among the nodes, the data is compared with data sensed by the neighbor node to determine a final state of the node, and the state of the node in a normal test state is diffused to other nodes in the network. When the node has a fixed value fault or a transientfault and the fault rate is excessively high, high diagnosis rate can be achieved. Simulation results show that the fault diagnosis algorithm provided by the invention effectively reduces the communication cost and the energy consumption among the nodes, so that the life cycle of the wireless sensor network is prolonged.

The invention discloses an artificial neural network (ANN)-based overhead flexible straight power grid fault detection method. The method comprises the following steps of: first, extracting a high frequency component by using wavelet transform, and processing a relation of positive and negative voltage change amounts by using phase mode transformation, so as to acquire an input characteristic quantity of an artificial neural network; further, designing quantities of nerve cells of a hidden layer and an output layer, and training and testing the artificial neural network through massive samples; and finally, realizing fault detection and fault selection functions according to an online detection result of the artificial neural network. The method overcomes defects of setting difficulty andcalculation complexity of a traditional protection algorithm, makes the function of the fault detection algorithm more perfect, has both a fault recognition function and a fault selection function, improves protection reliability, and is not affected by AC fault, running mode and transition resistance.

The invention provides a bearing interior and end face fault detection algorithm combining genetic algorithm optimization parameters and machine vision, and belongs to the technical field of analysisand measurement control. The bearing interior and end face fault detection algorithm comprises bearing interior fault detection and bearing end face fault detection. Firstly, the energy of each sub-band after vibration signal wavelet packet decomposition is used as a fault detection feature, and multi-modal fusion parameters are optimized in a training process of a support vector machine (SVM) byusing a genetic algorithm (GA) to achieve the bearing interior fault detection; and image collection is performed on an end face of a bearing, filtering processing is performed on a collected image byusing the machine vision technology, edge detection is completed by using a Canny operator, an optimal threshold value of a circular ring area is calculated by an Otsu algorithm to realize defect segmentation, and finally the bearing end face fault detection is realized. The accuracy of bearing fault detection is improved to a certain extent.

A method of electrical fault determination using the phase angle information of all the currents entering and leaving a protection zone, to determine whether a fault, if any, is internal or external to the protection zone.

The invention provides a rail wagon brake shoe fault detection method based on deep learning. The rail wagon brake shoe fault detection method comprises the following steps: acquiring a train image to be detected; constructing a brake shoe fault data set, wherein the fault data set is divided into a training set, a verification set and a test set; constructing a target detection and fault identification network by using a deep learning technology; pre-training the target detection and fault identification network on a public data set, and optimizing network parameters as a final algorithm model; and applying the algorithm model to the railway wagon to detect the brake shoe fault. Compared with a traditional railway wagon brake shoe fault detection algorithm, the method can overcome the problems of picture illumination conditions, shooting angles and the like, and has high robustness and accuracy; meanwhile, the fault can be accurately identified and positioned only by using a single-stage target detection method through a confrontation training method and the improvement of a Fusion structure, the real-time performance and the accuracy of detection are considered, and the labor intensity of detection personnel is reduced.

Rapid channel failure detection and recovery in wireless communication networks is needed in order to meet, among other things, carrier class Ethernet channel standards. Thus, resilient wireless packet communications is provided using a physical layer link aggregation protocol with a hardware-assisted rapid channel failure detection algorithm and load balancing, preferably in combination. This functionality may be implemented in a Gigabit Ethernet data access card with an engine configured accordingly. In networks with various topologies, these features may be provided in combination with their existing protocols.

The invention discloses an arbitration system and method oriented to critical applications, and belongs to the technical field of system arbitration. The arbitration system at least comprises a self-detection module, a heartbeat detection module and an arbitration management module. According to the self-detection module, a detection mechanism arranged in the system and a fault detection algorithm are used for detecting the self faults of a host system of a node, and the detected fault information is transmitted to the arbitration management module. The heartbeat detection module is used for detecting the state information of an opposite end node, and transmitting the detected fault information of other nodes to the arbitration management module. The arbitration management module is used for conducting final voting on double-machine nodes through an arbitration algorithm of the arbitration management module according to the fault information transmitted by the self-detection module and the heartbeat detection module. The invention further discloses the arbitration system oriented to the critical applications. According to the arbitration system and method oriented to the critical applications, a high fault coverage rate is achieved, and the requirement for high usability of the system is met.

The invention relates to a Prony algorithm and ill-conditioned data analysis-based power grid low-frequency oscillation on-line detection algorithm. According to the algorithm, signals obtained after preprocessing are inputted into a buffer window of 6 to 20 points; a least squares matrix coefficient is calculated; obtained matrix eigenvalues are compared with each other; whether a matrix is an ill-conditioned matrix is judged according to relationships between the eigenvalues; if the matrix is an ill-conditioned matrix, the data are filtered out, otherwise, the least squares method is adopted to obtain a matrix, and a generalized solution is further solved; the parameter of low-frequency oscillation signals is solved according to the generalized solution; and the amplitude, phase angle and attenuation coefficient of the low-frequency oscillation of a power grid can be estimated accurately in real time; and signals estimated by using the parameters can be applied to the linear prediction and suppression of low-frequency oscillation. The method has the advantages of high operability, insensitivity to signal noises, high corresponding speed and economic and practical value.

The invention relates to a least square positioning algorithm for distribution network faults. The method comprises steps of S1: setting a plurality of fault detection points on a distribution network line and synchronously acquiring current and voltage waveforms; S2: selecting any line having faults at two ends, and correcting parameters per line unit length; S3: performing Karrenbauer transform three-phase decoupling on the current and voltage waveforms of the fault detection points; S4: multiplying the corrected parameters per line unit length by the line length to get corrected parameters of the line; S5: combining the corrected parameters of the line, and establishing the voltage and current equations of the fault point and each fault detection point; and S6: using the least square algorithm to solve the system of equations to obtain the best estimation value of the fault distance. The algorithm utilizes the voltage and current waveforms before the fault occurs to correct the line parameters. After the fault occurs, the voltage and current equations between the fault point and the fault detection point are established through the fault analysis method according to the line parameters to realize the precise fault location.

A circuit interrupter for interrupting current on a line conductor is provided. The circuit interrupter includes separable contacts, a trip mechanism, a bimetal, a microprocessor, a series arc detection sequence, a low-pass filter circuit, and a high-pass filter circuit. The trip mechanism selectively opens the separable contacts when activated. The series arc detection sequence is resident on the microprocessor and includes a plurality of series fault detection algorithms. The low-pass filter circuit provides a low-pass signal to the series arc detection sequence. The high-pass filter circuit provides a high-pass signal to the series arc detection sequence. The sequence selects a particular algorithm from the plurality of algorithms based on the low-pass signal. The sequence calculates a plurality of statistical features from the high-pass signal and sends an output signal to activate the trip mechanism based on a comparison of the plurality of statistical features to the particular algorithm.

The invention discloses a transformer charged detection system and a method based on infrared temperature measurement. A controller controls the shutter time of an infrared planar array detector and the focusing action of a lens; the infrared planar array detector acquires infrared data of the transformer; the controller receives the signals acquired by the infrared planar array detector; the signals are subjected to digital-to-analog conversion to obtain digital voltage signals; the digital voltage signals are filtered and the data are cached to a memory; an upper computer reads the data after filtering; a fault identification algorithm is used for carrying out early warning on fault possibly happening in the transformer, and the possible fault is imaged on an image for marking. The uncooled infrared focal plane array detector based on a microbolometer is used for reading voltage signals generated by the circuit, the working condition of the transformer is judged in real time through the fault detection algorithm, early warning is carried out before possible fault, and charged detection is realized.

The invention provides a power distribution network intelligent sensor fusing a gateway function and a fault detection algorithm. The power distribution network intelligent sensor comprises a first collection unit, a second collection unit and a collection and summarizing integrated unit, wherein the first collection unit, the second collection unit and the collection and summarizing integrated unit are installed on three phase lines respectively; the first collection unit and the second collection unit are used for collecting fault recording data of the corresponding phase lines; the collection and summarizing integrated unit is used for collecting fault recording data of the corresponding phase line, collecting the fault recording data of the other two phase lines and performing summarizing processing on the collected fault recording data of the three phase lines for fault detection; and the collection and summarizing integrated unit is also used for sending the collected fault recording data of the three phase lines to a monitoring master station for fault detection. Through the power distribution network intelligent sensor, precise sensing of voltages, currents and other parameters can be realized, and the sensor has the advantages of self-energy-taking and low power consumption; and moreover, the sensor fuses the gateway function and can realize remote transmission and sharing of line monitoring information autonomously without the need for relying on a communication terminal, and therefore working stability and reliability are improved.

The invention discloses an SDN multi-controller fault detection algorithm based on a gray Markov algorithm. The SDN multi-controller fault detection algorithm comprises four steps of establishing a bidirectional fault detection mechanism, constructing a gray prediction model, establishing a gray Markov model and self-adapting heartbeat information arrival time. According to the invention, resources of the SDN controller are considered and utilized, a bidirectional fault detection mechanism is established; a fault point can be accurately determined through a bidirectional fault detection mechanism; because the data flow in the SDN control plane is in a real-time changing state, a gray Markov algorithm is proposed to predict the arrival time of next heartbeat information, and the time interval of sending the heartbeat information is adaptively adjusted, thereby effectively improving the fault detection efficiency of the controller.

The invention discloses an arc grounding fault modeling and analyzing method based on piecewise linear equivalence. A high-precision nonlinear arc light high-resistance grounding fault model is established based on a logarithmic arc model. Expression of arc nonlinear volt-ampere characteristics at a fault point is achieved through piecewise linear equivalence, solving of a nonlinear arc light high-resistance grounding fault state equation is achieved based on piecewise linear equivalence, and analytical expressions of fault zero-mode voltage and zero-mode current at a feeder line outlet underthe underdamping and overdamping conditions are given; the arc light high-resistance grounding fault model established by the invention is high in precision; the piecewise linear equivalence providedby the invention can realize the nonlinear expression of the arc, can obtain the analytical expression of the fault electrical quantity, and provides sufficient theoretical support for the subsequentdesign of the arc light high-resistance grounding fault detection algorithm of the neutral point arc suppression coil grounding power distribution system with both reliability and sensitivity.