550 results about "Fault free" patented technology

An adaptive switch mode LED driver provides an intelligent approach to driving multiple strings of LEDs. The LED driver determines an optimal current level for each LED channel from a limited set of allowed currents. The LDO driver then determines a PWM duty cycle for driving the LEDs in each LED channel to provide precise brightness control over the LED channels. Beneficially, the LED driver minimizes the power dissipation in the LDO circuits driving each LED string, while also ensuring that the currents in each LED string are maintained within a limited range. A sample and hold LDO allows PWM control over extreme duty cycles with very fast dynamic response. Furthermore, fault protection circuitry ensures fault-free startup and operation of the LED driver.

A method monitors non-faulty application traces of a computer application with the runtime environment during fault-free activities to create non-faulty runtime signatures for the computer application. Once obtained, the method stores the non-faulty runtime signatures. The method then detects a faulty application trace of the computer application and compares the faulty application trace with the non-faulty runtime signatures by comparing a call graph of the faulty application trace with call graphs of non-faulty application traces of the application signatures to identify differences between the two (e.g., fault attributes). Then the invention outputs the fault attributes to allow the user to identify a fault cause.

A method and a device for operating an internal combustion engine having an intake manifold and a ventilation system may allow a reliable and fault-free diagnosis of a fault in the ventilation. The ventilation system may be a tank ventilation or a crankcase ventilation. The ventilation duct is conducted to the intake manifold of the internal combustion engine. At least one pressure differential may be determined between an intake-manifold pressure or an ambient pressure, on the one hand, and a pressure in the ventilation duct, on the other hand. A fault in the ventilation may be diagnosed as a function of the at least one pressure differential.

The present invention is a system and method to improve the reliability and performance of existing enterprise IP networks which have dual-homed (or multi-homed) network architectures. In one aspect of the invention packets related to a selected category of transmission (e.g., VoIP) are duplicated at an edge router and sent over both (multiple) service providers. After traversing the service provider networks, only the first-to-arrive packets are kept and the later-arriving copies are discarded. In so doing, the result is better protection against node failures, link failures, and packet errors, and also better QoS performance under normal (fault-free) operation.

An image display unit (1) with a front frame (2) and with a sensor device (6) provided with a sensor (9). The sensor (9) is provided for detecting the luminance and/or the color location of an image that displayed on a display (7) of the image display unit (1). To detect luminance, known image display units have a sensor support that, in a non-operating position, is placed in a recess of a frame of a flat screen in an area that cannot be seen by a viewer. Suitable drives make it possible for the sensor support to swivel out from its non-operating position into a position for detecting the luminance. The production of an image display unit of this type is complex. In addition, the drives are susceptible to malfunction, making continuous and fault-free luminance detection impossible. The disclosed arrangement avoids these drawbacks.

A machine learning method and a machine learning device, and a fault prediction device and a fault prediction system. fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

The invention provides a data protection method and system capable of combining with a snapshot function based on distributed block storage. The data protection method comprises the following steps: directly or indirectly mounting a ceph block device on a production server; deploying a backup client on the production server, and installing an I/O drive program which realizes backup processing between a product server data volume and the ceph block device; through a backup server, establishing a mirror image relationship between the product server data volume and the ceph block device; and when a snapshot task is started, simultaneously writing data generated by the application of the production server into the product server data volume and a mirror image volume on the ceph block device, executing a snapshot on the mirror image volume on the ceph block device, and recording a snap time point and snap information to the backup server to bring convenience for subsequent recovery. The data protection method and system capable of combining with the snapshot function based on the distributed block storage can provide the backup data of which the fault-free rate is above 99.9999%, guarantees that the data can be recovered at any time, and provides data safety guarantee for users.

The invention discloses an asynchronous motor fault monitoring and diagnosing method based on a model. The method comprises the following steps: firstly, acquiring a three-phase input voltage signal and a three-phase output current signal of an asynchronous motor which can be normally operated, establishing a mathematical model to serve as a fault-free model; carrying out parallel running on the fault-free model under driving of a same input voltage u to obtain a residual signal d; then carrying out time domain analysis on the residual signal d, determining a threshold value eta of the residual signal of the asynchronous motor according to the 3 sigma principle, and judging whether a fault occurs or not by monitoring whether a residual effective value dRMS exceeds the threshold value eta or not when the asynchronous motor is stably operated; carrying out frequency domain analysis on the residual signal d again, and determining the fault type according to a fault feature frequency component fF appeared in a residual spectrum. The monitoring and diagnosing method disclosed by the invention can effectively weaken adverse effects on motor fault monitoring and diagnosis by an input voltage and improve the signal-to-noise ratio of a fault signal, thereby improving the sensitivity of the motor fault monitoring and the reliability of the fault diagnosis.

The invention relates to a three-level inverter multi-mode fault diagnosis circuit and a diagnosis method thereof. Voltage signals of an upper bridge arm, a middle bridge arm and a lower bridge arm of each phase of bridge arms of an NPC three-level inverter are collected. A bridge arm voltage fault feature extraction module extracts fault features of the voltage signals and then sends the fault features to a neural network module to be analyzed. Voltage of each middle bridge arm is measured through a main neural network module so that eleven fault modes, including a fault-free mode, of an open circuit of a single-bridge-arm component of the three-level inverter can be diagnosed. Voltage of each upper bridge arm and voltage of each lower bridge arm are measured through two auxiliary neural network modules so that the other four fault modes of the open circuit of the single-bridge-arm component of the three-level inverter can be diagnosed, and by combination of the three neural network modules, thirteen multi-fault modes of the open circuit of each single-bridge-arm component of each phase of the bridge arms of the three-level inverter and simultaneous open circuits of multiple components of each phase of the bridge arms of the three-level inverter are diagnosed together. According to the three-level inverter multi-mode fault diagnosis circuit and the diagnosis method thereof, fault components can be exactly positioned, the algorithm structure is simplified, calculation of a Hessian matrix can be avoided, and accordingly the calculation amount and internal storage demands in a training are reduced, the operation speed is high, the diagnosis accuracy is high and the anti-interference capacity is high.

A simulator simulates routing system protocols to build routing tables corresponding to a modeled network, and a comparator compares the routing tables in the actual network to these simulator-created routing tables. Because the modeled system represents a fault-free version of the actual system, and assuming that the modeled routing system protocols are representative of the algorithms used in the actual routers, these simulator-produced routing tables will represent the ‘ideal’ routing tables that should be present in the routers of the actual network. By querying each router in the actual network for its routing table and comparing each routing table to the corresponding simulator-produced routing table, any differences from the ‘ideal’ can be identified.

The invention provides a multiband infrared radiation automatic measuring system which can timely and automatically measure the infrared radiation characteristics of a measured object within different bands under complicated backgrounds and completely meets the requirements of long-term fault-free automatic observation radiation calibration measurement. The multiband infrared radiation automatic measuring system comprises a scanning device, a light splitting device, an infrared detection device and a control system/circuit, wherein the scanning device, the light splitting device and the infrared detection device are sequentially arranged on a light path in a radiation incident direction; and the scanning device comprises a protection window capable of rotating, a rotary reflecting mirror and a fixed double-blackbody correction assembly. In the invention, light spectrum light splitting and band modulating and canning technologies are used, and two medium and long wave detectors are selected to match with the technologies, thereby increasing measurable optical channels to effectively form the radiation calibration measurement to more long-wave and medium wave bands. Moreover, the radiation size required by a blackbody radiation cavity is compressed, the design and manufacture difficulties of the blackbodies are reduced, and the controllable precision is improved.

A system and method of protection switching in a communications network that makes more efficient use of resources in the network and reduces the loss of data traffic carried by the network. The communications network includes an established working path and an established protect path interconnecting a source node and a sink node. The source node and the sink node are configured to perform label switching on the network. The source node receives primary data traffic and secondary data traffic over one or more communications paths. In a fault-free condition, the source node sends the primary data traffic over the working path to the sink node, and sends the secondary data traffic over the protect path to the sink node. In the event of a fault or switchover condition in the working path, the source node performs traffic trunk and label merging on the primary and secondary traffic, and sends the merged traffic over the protect path to the sink node.

Methods, systems, apparatus and devices to provide autonomous self-repair for programmable logic. Using Competitive Runtime Reconfiguration, an initial population of functionally identical, yet physically distinct individual programmable logic configurations are produced at design time. During operation, individuals compete for selection based on a fitness function favoring fault-free behavior and any physical resource exhibiting an operationally-significant fault decreases the fitness of those configurations which use it. Through runtime competition, the presence of the fault becomes occluded from the visibility of subsequent operations. Offspring formed through crossover and mutation of faulty and viable configurations are reintroduced into the population to enable evolution of a customized fault-specific repair, realized as new configurations using normal throughput processing operations. In an embodiment, the error detection circuit is also checked for errors.

The invention discloses a fault detection and identification method for civil aircraft system based on LSTM-AE depth learning framework and relates to the technical field of condition monitoring and fault diagnosis of complex civil aircraft system, and can be used to realize the monitoring and identification of flight faults. The invention comprises the following steps: selecting time series dataof multi-state parameters of an aircraft in flight under a certain stable condition, and according to the characteristics of the monitored object, the time series data of state parameters under suitable conditions are selected for the training of the system reconstruction model, then the fault-free state of civil aircraft system is modeled and reconstructed by making full use of the long-time series-dependent memory ability of LSTM model. The fault monitoring and identification are realized by further analyzing the reconstruction error of its state parameters. The invention solves the problemof insufficient fault monitoring means of civil aircraft system, utilizes the advantage of deep learning in big data analysis to mine massive operation and maintenance data of civil aircraft, and provides important support for fault monitoring of civil aircraft system and route fault isolation.

The invention discloses an AGV intelligent dynamic scheduling method in a workshop environment, comprising the following steps: an agent-based workshop AGV scheduling mode is built; an initial scheduling sequence of machined work pieces is acquired based on a genetic algorithm and input to a database; a device agent quires the database to get a transport task, gets a fault-free AGV list from a management agent, initiates a tendering process, and releases the transport task; an AGV agent participates in tendering based on a beat mechanism, and AGVs' inferior competitive bidding under the condition of multi-device concurrent tendering is avoided based on the beat mechanism; the device agent evaluates the AGV tendering result; the AGV agent receives the competitive bidding result, and makes a plan for next step; and if there is AGV fault disturbance, the management agent handles the disturbance. Real-time intelligent dynamic scheduling of multiple work pieces and multiple tasks in a workshop production environment is realized effectively.

The invention discloses a performance degradation evaluation method based on an FCM-HMM rolling bearing. The method comprises the steps that an autoregressive model (AR) is used to extract the characteristic of the vibration signal of an early fault-free rolling bearing and the failure characteristic of a failure rolling bearing of the same type and position (bearing of the same kind in short for the sake of description); a fuzzy C-mean value (FCM) performance degradation evaluation model is established through an early fault-free sample and a failure sample; an HMM model is established through the fault-free sample; in view of the advantages of FCM and HMM, degradation indexes acquired through FCM and HMM is input into the FCM performance degradation model as input characteristics, so as to acquire normal and failure clustering centers; and an early fault threshold is set at the same time. Experimental analysis shows that the evaluation indexes acquired through the performance degradation method provided by the invention can monitor the performance degradation trend of the rolling bearing in real time, and early faults can be detected in time.

The invention relates to a design method for stacking Ethernet devices. In an annular-structure stack system, on the basis of the thought of link integration, all the stack ports in two directions of a stack unit are added to the same stack aggregation group; the destination of messages which are sent to the other stack unit is set to be the stack aggregation group; and then, a stack port is found out from a port mapping table of the stack aggregation group according to the hash values of the messages so as to forward the messages from this port. When a stack link at some part fails, the stack system only needs to modify the configuration of two stack units which are next to the fault and remove the failed port from the stack aggregation group so as to prevent the messages from being forwarded from the failed port and forward the messages from other fault-free ports. Because other stack units of the stack system do not need to sense the fault, the method restricts the influence of the failed stack port on the entire stack system, and enables the fault of the stack system to be repaired quickly.

The invention relates to global satellite navigation system receiver autonomous integrity monitoring technology and discloses a parity vector method-based double-satellite failure recognition method, aiming at the problems of false positives and false negatives caused by fault deviation offsetting when the parity vector method is used for recognizing two fault satellites. According to the technical scheme, the parity vector method is used for recognizing one fault satellite; with the fault satellite as the basis, four fault-free satellites are found out, and the information of the fault-free satellites is used for roughly locating, so that the fault satellites can be recognized; the recognized fault satellites are removed, and then the position resolution is carried out again, so that the locating accuracy is improved; therefore, the problems of false positives or false negatives caused by fault deviation offsetting can be avoided. The method solves the problem of the fault deviation offsetting caused by parity vector residual error and realizes the detection and the recognition for a plurality of fault satellites. After the method is used for detecting and recognizing satellite failure, the locating accuracy is improved. The method is mainly used for monitoring the autonomous integrity of a global satellite navigation system receiver.

Signature circuits are used during testing of an integrated circuit. Test vectors are applied as inputs to a circuit under test. A signature circuit stores a “signature” for the circuit under test based on a combination of signals from the circuit under test in response to test vectors and a previous stored state of the signature register. The value contained in the signature register at the end of the test is the signature. A fault-free circuit generates a particular signature for the applied test vectors. Faults can be determined by detecting variances from the expected signature. In one embodiment, the signature circuit uses a combination of two error detection codes.

The invention discloses a receiver autonomous integrity monitoring method used for a satellite navigation system, which is an RAIM method based on multi-satellite fault detection and removal and achieve the fault removal goal by a mode of acquiring constellations comprising the most fault-free satellites. In the method, fault-free individuals are formed by adopting coding, initialization and individual detection; then an individual comprising the most fault-free satellites is obtained by optimal search, and the individual is used as an initial result; when terminal condition is satisfied, the individual is the final result of RAIM. The method has simple realization, high fault removal efficiency and good robustness, not only can carry out single-fault detection and removal but also can carry out multi-fault detection and removal, is not only suitable for single system but also is suitable for multisystem and effectively achieve the goal of carrying out multi-satellite fault detection and removal in the RAIM method.

The invention relates to a method for online abnormity detection of a low-speed diesel engine of a ship based on baseline deviation. The method for online abnormity detection of the low-speed diesel engine of the ship based on baseline deviation comprises the following steps that fault-free data are obtained from historical parameter records of a monitoring system for the low-speed diesel engine of the ship, data under the steady working condition are selected for constructing a health baseline in a memory matrix form, and the health baseline in the memory matrix form is used as a health state baseline of the monitoring system for the low-speed diesel engine; and real-time monitoring data are obtained from the monitoring system for the low-speed diesel engine of the ship, baseline deviation calculation is conducted on the real-time monitoring data under the steady working condition, and an online abnormity detection result based on baseline deviation is output.

The invention discloses a neural network assisted integrated navigation method for an underwater vehicle. The neural network assisted integrated navigation method is implemented by use of strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), a magnetic compass pilot (MCP) and a terrain aided navigation system (TAN), wherein the integrated navigation is completed by use of a decentralized filter structure of Kalman filtering and a fault-tolerant method, assisted by a radial basis function neutral network (RBFNN). In a fault-free time period, RBFNN is in an online learning model, the observed quantity difference between the SINS and each auxiliary system is taken as the expected output of the RBFNN, and the output fb of an accelerometer after error compensation and the output shown in the specification of a gyroscope are taken as the inputs of the RBFNN; when a sub-system composed of the SINS serving as a reference system and each auxiliary system is out of order, an RBFNN prediction mode is immediately activated, and the predicted output is taken as the measurement input of a corresponding sub-filter. Compared with the SINS mode out of order, the RBFNN mode has the advantages that the navigation accuracy is improved; especially when the fault recovery time is relatively long, the improvement of the navigation accuracy of the RBFNN mode is particularly obvious.