60results about How to "Improve fault diagnosis rate" patented technology

The invention relates to an online fault diagnostic apparatus for a circuit board. The online fault diagnostic apparatus comprises an upper computer and a lower computer, wherein the upper computer is used for performing online signal processing and fault diagnosis, the lower computer is used for providing needed signals for a tested circuit board and uploading test information, and the upper computer and the lower computer are connected and communicated by using a serial bus. The lower computer comprises a CPU (Central Processing Unit) board, a switching power board, a relay switch matrix board, an analog switch matrix board, a digital signal board, A / D (Analog to Digital) and D / A (Digital to Analog) boards, an interface device and a detector, wherein all functional circuit boards are mutually connected in the manner of a system bus, the interface device, the relay switch matrix board and the A / D and D / A boards are electrically connected, and the detector and the CPU board are electrically connected. The invention has strong universality, flexible assembly, simple operation and high fault diagnosis ratio and is suitable for the online fault diagnosis and location of the circuit boards in various industries.

The invention discloses an aviation piston engine all-purpose parameter test device and a method thereof, the test device includes: a control unit TCU, an IMS, a flight control computer, a servo-electric motor, a turbo-charger and an engine. The turbo-charger is driven by the engine discharge gas and supplies the turbo-charged air for the engine, the servo-electric motor is controlled by the control signal outputted from the turbo control unit TCU which senses and calculates correlation parameters, which causes the connected valve deflexion, in order to change the turbo-charged air pressure which is transferred from the turbo-charger to the aviation piston engine. The all-purpose parameter test system IMS, taken the singlechip as the kernel, obtains engines running status parameter and TCU controlling parameter, judges the two parts of parameters with overrun judgment and gives status code according to the fault mode, packs this two parts of parameters and status codes; transmits the packed parameters to the flight control computer; the flight control computer then carries the followed processing and transmits the data down to the ground.

The invention relates to a network topology structure and a node information gathering method, which are applied to a network topology structure in industrial Ethernet equipment and accurate search of network information. The method mainly comprises the following steps of: presetting interface information and an interface index of neighboring node state information in an exchange equipment host computer; receiving a neighbor finding report by using a network management terminal; determining the linking relation between ports according to the port linking state of the neighbor finding report and neighboring node information by using a network equipment node; and establishing a network topological diagram according to the port linking relation by using the network management terminal to perform corresponding network management.

The invention discloses a fault diagnosis method for a gyro measurement system. The method comprises the steps of: (1) determine the configuration type of gyros, and then calculating the angle relationship between the gyros; (2) determining the weight coefficient of a gyro balance equation; (3) determining judgment gyro sequencing; (4) designing a balance equation according to the determined judgment gyro sequencing result in step (3); and (5) designing a fault diagnosis method. The method provided in the invention can substantially improve the probability of gyro fault diagnosis, has strong project realizability, and can adapt to fault diagnoses of gyros with any complex configuration.

The invention provides a fault fusion diagnosis method for a rolling bearing based on an improved D-S evidence theory. The method comprises the steps of: carrying out classification identification ondata of different fault states by adopting a plurality of fault diagnosis methods, and reflecting different fault characteristics of the rolling bearing from multiple angles to obtain a plurality of primary diagnosis results; constructing a correlation matrix between each evidence by utilizing a conflict factor in the D-S evidence theory, and calculating reliability of each evidence; according tothe reliability of each evidence, classifying similar evidences and conflict evidences by adopting a nearest neighbor rule; retaining probability distribution of similar evidences while modifying basic probability distribution of conflicting evidences; and fusing similar evidences and modified conflict evidences by adopting a D-S combinational rule to obtain a final diagnosis result. According tothe fault fusion diagnosis method for the rolling bearing based on the improved D-S evidence theory, fault diagnosis is performed from multiple angles, then multiple decisions are fused, advantages ofeach diagnostic method can be fully preserved, at the same time one-sidedness caused by single diagnostic method is preserved substantially, so that fault diagnosis rate and diagnosis reliability areimproved.

The invention discloses a draught fan state estimation method and system. The method comprises the steps that online operation data of a draught fan is obtained; the online operation data is preprocessed, and time series data is obtained; whether or not the time series data shows that the draught fan is in a failure state through an online state monitoring model is judged, and a judging result is obtained; when the judging result shows that the draught fan is not in a failure state, the output detection result is health; when the judging result shows that the draught fan is in the failure state, the time series data showing that the draught fan is in the failure state is marked, and a failure detection sample is obtained; the failure type corresponding to the failure detection sample is determined through the online failure detection module; the failure type is output. Accordingly, the time series data is adopted as an input detection sample, compared with single state information, the time sequence is achieved, the time series data is more real, time-varying information of a draught fan system can be reflected, and the failure diagnosis rate of the draught fan is increased.

The invention discloses a switched circuit fault diagnosis method based on wavelet transformation and ICA (independent component analysis) feature extraction. The switched circuit fault diagnosis method includes the steps of firstly, performing classifier training and fault dictionary construction, namely, based on circuit simulation, acquiring feature parameters by a method based on wavelet transformation and ICA feature extraction, and constructing a fault dictionary and a training classifier based on the feature parameters; secondly, performing fault diagnosis, namely, acquiring the feature parameters aiming at a switched current circuit to be diagnosed by referring to the fault dictionary and by the method based on wavelet transformation and ICA feature extraction, inputting the feature parameters into a trained classifier, and subjecting the switched current circuit to be diagnosed to fault diagnosis to obtain output signals of the classifier, namely, fault diagnosis results. The switched circuit fault diagnosis method based on wavelet transformation and ICA feature extraction has the advantages of ingenious concept, easiness in implementation, simulation proving and capability of distinguishing various fault types more accurately as compared with an existing method.

The system comprises a highly-reliable beneficiation equipment fault diagnosis system and a highly-reliable beneficiation equipment fault diagnosis method based on industrial cloud, belonging to the technical field of fault diagnosis. The beneficiation equipment fault diagnosis system mainly comprises a local intelligent module and a cloud intelligent module in a local server, a model establishment and management module and a fault diagnosis module in a cloud server, and a local monitoring center. The beneficiation equipment fault diagnosis method comprises the steps the: the model establishment and management module optimizes fault diagnosis models of or establishes fault diagnosis models for the industrial cloud server and the local intelligent module; the local intelligent module calculates a local diagnosis result by means of the local fault diagnosis model, and the fault diagnosis module calculates a cloud diagnosis result by means of an optimal local fault diagnosis model; and the local monitoring center displays the diagnosis results. The cloud server in the highly-reliable beneficiation equipment fault diagnosis system establishes and evaluates the fault diagnosis models according to data of different beneficiation plants, and improves the fault diagnosis rate; the fault diagnosis efficiency is improved by using the cloud server; the models are reduced, and the local diagnosis efficiency is ensured; and the local diagnosis service can be offered when the cloud diagnosis service is interrupted, and the diagnosis reliability is ensured.

The invention, which relates to the technical field of the analog circuit fault diagnosis, provides an improved particle swarm optimization diagnosis method and system of an analog non-linear direct current circuit. With the method and system, a problem that the existing pattern-classification-based fault diagnosis method does not has the enough diagnosis capability for the unknown fault state and a multi-fault state of the analog circuit can be solved. The method comprises: establishing a fault diagnosis equation; calculating fitness values of all particles; calculating a unified inertia weight of all particles; calculating changes of the fitness values; determining a final value of the inertia weight of all particles at current iteration; updating speeds and positions of all particles in the particle swarm; and repeating all above-mentioned steps until method convergence is realized. Compared with the standard PSO algorithm, the method and system based on the technical scheme have the high fault diagnosis coverage rate and the fast fault diagnosis speed.

The invention relates to a transmission system fault diagnosis method based on multi-information fusion. The method comprises the following steps: acquiring multiple groups of original signals of a transmission system in normal and fault states; preprocessing the acquired signals; respectively extracting first feature vectors, and performing normalization processing and feature fusion to serve astraining samples; constructing a support vector machine model, and training by using the training sample; collecting all signals of the transmission system in real time to be preprocessed, extractingand normalizing a second feature vector, conducting diagnosis through the trained support vector machine diagnosis model after feature fusion, and outputting a fault detection result. Multiple signalsare collected for feature extraction and feature fusion, the problem of low accuracy caused by diagnosis of single features can be effectively solved, the obtained diagnosis result better conforms tothe real running state of a motor, and the fault diagnosis rate of the transmission system is effectively increased.

The invention discloses a vibration and acoustic transmission signal regulation instrument for detecting mechanical faults, belongs to the technical field of mechanical equipment fault detection instruments and is used for detecting mechanical equipment faults. The vibration and acoustic transmission signal regulation instrument consists of a resonance modulation part and a normal signal processing part, wherein the resonance modulation part comprises a program controlled amplifying circuit, a high Q-factor band-pass filter, a secondary resonance circuit, an envelop detection circuit and anti-aliasing circuits; the normal signal processing part comprises a channel selection circuit and an anti-aliasing circuit; a sensor for detecting faults is connected with the program controlled amplifying circuit; the program controlled amplifying circuit is connected with the high Q-factor band-pass filter and the channel selection circuit respectively; the high Q-factor band-pass filter is connected with the secondary resonance circuit, the envelop detection circuit and the anti-aliasing circuit; the channel selection circuit is connected with the anti-aliasing circuits; and the anti-aliasing circuits are both connected with an A / D (analogue-to-digital) acquisition card. By using a vibration and acoustic transmission signal regulation technique, the vibration and acoustic transmission signal regulation instrument is good in effect of extracting fault signals of mechanical equipment and is precise in fault diagnosing.

The invention relates to a method for detecting the abnormality of an underground heat pipe network by infrared thermography coupling soil temperature / humidity. In the direction of an underground pipe network, the surface temperature of a direct burial heat pipe is detected; four infrared thermography images are shot; and according to the area and temperature change of a temperature abnormal region, a surface temperature abnormal region is positioned. For a region with an insulation layer damage or leakage fault, a soil temperature and humidity sensor is used for measuring and recording the soil temperature and the volume water content. Higher temperature gradient shows in the vertical direction; when the change tendency of the volume water content of the soil at the lower part of the surface temperature normal / abnormal region along with the depth is consistent, but the soil water content of the abnormal region is slightly higher than that of the normal region, it is judged that an insulation layer of the underground pipe network is damaged; and if the temperature and volume water content changes of underground soil in the two regions are prominently different along with the depth, the surface temperature abnormal phenomenon indicates the leakage of the underground pipe network. The method can accurately position the leakage point of the heat pipe and the damage point position of the insulation layer to instruct maintainers for repair, reduces blind excavation of pavements, and improves the repair efficiency.

The invention discloses a missile performance testing unit for a missile testing system, and relates to the technical field of missile testing devices. The missile performance testing unit comprises amissile performance testing unit box body, a second alternating current input interface is embedded in the missile performance testing unit box body, and the second alternating current input interface is connected with one power supply input end of a first DC-DC conversion circuit through a second power supply switch and a second switch power supply, the first path of the output end of the firstDC-DC conversion circuit is connected with the input end of a second power supply processing circuit, the second path of the output end of the first DC-DC conversion circuit is connected with the power input end of a cranking module, and a second direct current power socket is embedded in a turntable control unit box body. The missile performance test is completed by matching a computer measurement and control unit, a turntable control unit, the missile performance testing unit and an air source component, and the missile fault diagnosis rate and maintenance efficiency can be improved.

The invention discloses a hierarchical adaptive threshold function-based wavelet threshold denoising method. In a process of collecting a bearing signal of a rotary machine, due to interference of field equipment and environment, the collected signal contains noises; and for ensuring real and effective measurement data, it is necessary to perform denoising processing on the collected original bearing signal. The threshold function which is continuous at a threshold, is derivable in a wavelet domain and has a trend parameter is built; by calculating entropy values of noise signal energy and total signal energy of wavelet decomposition layers, the trend parameters of the layers are obtained; and threshold function mathematic models corresponding to the wavelet decomposition layers are obtained. According to the method, the trend parameters are adaptively selected in the decomposition layers through a wavelet threshold function; and noise components in bearing outer ring fault, inner ringfault and ball fault signals can be removed more effectively to achieve a better denoising effect.

The invention discloses a device for detecting an open circuit and a short circuit of a line. The device comprises a plurality of light-emitting components connected to a line interface and an electro-acoustic transducer connected with the line interface through a driving circuit; a driving power supply is connected between the driving circuit and the line interface; when the line is in a closed-circuit condition, all the light-emitting components are turned on, the driving circuit is conducted, and the electro-acoustic transducer sounds; when one or multiple lines are in a short-circuit condition, the light-emitting components connected to the short-circuit line are turned off, the driving circuit is cut off, and the electro-acoustic transducer does not sound; and when any line is in an open-circuit condition, all the light-emitting components are turned off, the driving circuit is cut off, and the electro-acoustic transducer does not sound. The device disclosed by the invention has the advantages of simple circuit structure, low manufacturing cost, rapid detection speed, stability and reliability.

The invention discloses an analog circuit fault diagnosis method based on a circular model and a neural network. Firstly, fuzzy group analysis is carried out on an analog circuit, and each piece of fuzzy group information is obtained; then circular model parameters of different measuring points of all the elements in the analog circuit under different test frequencies are obtained through circularmodel simulation, and a feature vector is obtained through construction to be used as a training sample for training a constructed neural network; and a circular model parameter of each measuring point at each test frequency is obtained according to degradation data during fault diagnosis, so as to form a test feature vector to be input into a trained neural network for fault diagnosis. Accordingto the method, the fault diagnosis of the analog circuit is realized by combining the circular model parameters with the neural network, so that the fault diagnosis rate of the analog circuit can beeffectively improved.

The invention provides an electric vehicle fault diagnosis method based on a dynamic threshold model, and the method is used for the fault diagnosis of a battery system in an electric vehicle, and improves the threshold model building and parameter identification algorithms. A circuit basic characteristic test experiment is conducted at different temperatures, and the parameters of an equivalent circuit model are obtained; An OCV-SOC-Q three-dimensional response surface model is established; and model parameter identification is conducted by adopting a recursive least square method with forgetting factors, and a dynamic threshold model about R0 and tau is established. In the actual fault diagnosis process, parameters and states are identified by using a double extended Kalman filtering algorithm to obtain battery R0, tau, capacity and SOC; a parameter reference value is determined by adopting a temperature interpolation method; a parameter threshold is determined; a residual error is generated; and whether the battery has a fault or not is judged by comparing the residual error with a threshold value. According to the method, the fault diagnosis rate is high, and the problems of untimely detection, false alarm and missed alarm can be avoided.

The present invention discloses an analog circuit fault feature extraction method based on a cloud correlation coefficient matrix. The method comprises the steps of: solving cloud numerical features of a fault mode, a membership matrix of a feature vector, an extreme value of the integrated membership matrix and a cloud correlation coefficient and a cloud association degree according to a cloud model, extracting a high-frequency sequence with a large degree of association corresponding to the fault extraction vector and a low-frequency sequence with a small degree of association, and randomlyselecting cloud correlation coefficients with large difference of the degrees of association as the fault feature vectors of the circuit so as to complete fault feature extraction of the tested analogcircuit. The analog circuit fault feature extraction method employs the cloud model capable of processing the circuit tolerance and the fuzziness at the boundary of the elements to improve the resolution ratio of the fault modes when fault diagnosis is performed, is good in diagnosis performance, can accurately locate the fault elements and improves the diagnosis performance and the efficiency ofthe circuit.

The embodiment of the invention discloses a power transmission line fault detection apparatus which comprises a light window, a spectroscope, an infrared image acquisition device, an ultraviolet objective lens, a wide spectrum ultraviolet optical filter, a day-blind ultraviolet optical filter, an ultraviolet detector, an image fusion processing system and an output device. The light transmitted by a transmission line is transmitted through the light window; the light of a long-wave infrared spectral band is transmitted through the spectroscope and get imaged by an infrared camera; after the light of the ultraviolet spectral band is reflected by the spectroscope, the light enters the ultraviolet objective lens and passes through the wide spectrum ultraviolet optical filter or day-blind ultraviolet optical filter and then enters the ultraviolet detector to be imaged. An ultraviolet image and an infrared image are subjected to processing by the image fusion processing system and finally output by the output device. The system realizes the all-weather detection of faults of transmission lines and solves the problem in the prior art that the fault diagnosis rate is not high and the simultaneous detection of voltage-type faults and the current-type faults can not be carried out. The system effectively improves the fault diagnosis rate. The invention also provides a transmission line fault detection method.

The invention discloses an analogue circuit fault diagnosis method based on an improved type clone selection algorithm. The method includes the first step of canceling the decision rules that an original decision algorithm determines which diagnosis radiuses faults belong to based on experience, and adopting the minimum Euclidean distance as a diagnosis decision condition, the second step of modifying an affinity calculation formula and utilizing a formula f=1 / (1+d) to replace an original formula f=1 / d so as to prevent overflowing in calculation and standardize the affinity within a fixed range of (0,1], and the third step of modifying an overall affinity calculation mode and utilizing an average value expression method of the affinity of all individuals in a species group to replace a sum expression method of the affinity of all the individuals in the species group. Through the first step, failure switch-off can be eliminated, false switch-off and excessive switch-off can be reduced and the fault diagnosis rate can be improved. Through the second step, calculation is convenient and the comparability of the affinity is higher. The improved type clone selection algorithm is applied to analogue circuit fault diagnosis and has superior performance.

A 3G (the third generation telecommunication) network based remote dental air circuit diagnosing and monitoring system belongs to the field of dental air systems. The 3G network based network based remote dental air circuit diagnosing and monitoring system comprises a control system arranged on dental air circuit equipment, and is characterized in that the control system comprises a PLC (programmable logic controller), a monitoring disc, a control switch and a sensor; running of the dental air circuit equipment is monitored in a service room; the dental air circuit equipment are monitored within a distance ranging from 3m to 500m; via a server, various working parameters and alarm parameters of a plurality of units of dental air circuit equipment are centralized and displayed and alarm information is promoted, and a user / an equipment manufacturer / an after-sale maintenance personnel can access to the server to monitor the dental air circuit equipment. The dental air circuit equipment can be monitored according to above three schemes, and by analyzing monitoring data, fault equipment can be removed according to any one of the above schemes when equipment exception is found. The 3G network based network based remote dental air circuit diagnosing and monitoring system is simple to control, multiply in fault acquiring modes and high in diagnosing rate, and can avoid fault omission and realize real-time monitoring.

The invention discloses a rolling bearing fault diagnosis method based on feature fusion and WOA-ELM. The rolling bearing fault diagnosis method comprises the following steps of: step 1, taking official bearing data of the Case Western Reserve University as original vibration data; 2, performing time domain analysis, spectral analysis and wavelet packet decomposition on the original data, and extracting time domain, frequency domain and time-frequency domain features; 3, performing normalization processing on a mixed domain feature set obtained in the step 2; 4, performing dimension reduction on the normalized high-dimensional feature set by using a manifold learning LPP algorithm to obtain a low-dimensional feature sample set; and step 5, optimizing ELM network parameters by using a WOA algorithm, and inputting the low-dimensional feature data into a WOA-ELM model for fault diagnosis. According to the method, the problems of insufficient feature extraction, redundant feature information existing in a multi-feature sample, insufficient stability caused by network parameters randomly generated by an extreme learning machine and the like are effectively solved. The method has the obvious advantage of improving the fault diagnosis rate of bearings.

The invention discloses an in-loop dual-backup system. Two peripheral device controllers and two peripheral device structures are adopted; the two peripheral device controllers work in a dual-core lock step mode; the peripheral devices are respectively controlled by different peripheral device controllers; and one of the peripheral devices negates the written data according to bits. According to the in-loop dual-backup system, the dual-backup peripheral device is seamlessly connected to the loop of the dual-core lockstep peripheral device controller, so that the fault diagnosis rate of peripheral device access is greatly improved, and meanwhile, due to the isomerism of data stored in the peripheral device, the risk of common cause failure is greatly reduced.

The invention provides a PCIE equipment fault detection method, a device and a server.The server comprises a CPU and a BMC, and the BMC receives equipment information of in-place PCIE equipment collected by the CPU; equipment information of the PCIE equipment supporting the MCTP on the PCIE bus is obtained; determining a PCIE device to be detected according to the obtained device information of the in-place PCIE device and the device information of the PCIE device supporting the MCTP; and performing fault detection on the to-be-detected PCIE device, and determining a fault PCIE device in a fault state. According to the PCIE equipment fault detection method and device, the BMC does not only depend on a BIOS interrupt service program to detect the PCIE equipment fault, the BMC can actively determine the to-be-detected PCIE equipment and perform fault detection on the to-be-detected PCIE equipment, so that the fault detection means of the PCIE equipment is expanded. The system fault diagnosis rate can be effectively improved.

A 3G (the third generation telecommunication) network based remote dental air circuit diagnosing and monitoring system belongs to the field of dental air systems. The 3G network based network based remote dental air circuit diagnosing and monitoring system comprises a control system arranged on dental air circuit equipment, and is characterized in that the control system comprises a PLC (programmable logic controller), a monitoring disc, a control switch and a sensor; running of the dental air circuit equipment is monitored in a service room; the dental air circuit equipment are monitored within a distance ranging from 3m to 500m; via a server, various working parameters and alarm parameters of a plurality of units of dental air circuit equipment are centralized and displayed and alarm information is promoted, and a user / an equipment manufacturer / an after-sale maintenance personnel can access to the server to monitor the dental air circuit equipment. The dental air circuit equipment can be monitored according to above three schemes, and by analyzing monitoring data, fault equipment can be removed according to any one of the above schemes when equipment exception is found. The 3G network based network based remote dental air circuit diagnosing and monitoring system is simple to control, multiply in fault acquiring modes and high in diagnosing rate, and can avoid fault omission and realize real-time monitoring.

The invention discloses a switching circuit fault diagnosis method based on wavelet transform and ICA feature extraction, step 1: classifier training and constructing a fault dictionary, based on circuit simulation, adopting a method based on wavelet transform and ICA feature extraction to obtain characteristic parameters, based on The characteristic parameters construct the fault dictionary and train the classifier; Step 2: Fault diagnosis: refer to the fault dictionary, use the method based on wavelet transform and ICA feature extraction to obtain the characteristic parameters for the switching current circuit to be diagnosed, and input the characteristic parameters into the training Fault diagnosis is carried out in the classifier of the switch current circuit to be tested, and the output signal of the classifier is the fault diagnosis result. The invention has an ingenious concept and is easy to implement. The simulation proves that compared with the existing method, various fault types can be more accurately distinguished .

The invention discloses an online fault diagnosis system for an on-chip memory. The system comprises a storage access controller, a data backup controller, an address parser, an interleaver, a staticmemory tester and an on-chip memory, and is characterized in that the storage access controller converts a received system request into a required address signal, a required write data signal, a required read-write control signal and a required read data signal; the data backup controller is used for backing up the content of the memory packet needing to be backed up to the redundant packet; the address parser distributes an access request of the memory access controller to a memory logic packet. The interleaver maps an access request for a memory logic packet to a memory physical packet; thestatic memory tester is used for periodically testing the on-chip memory; and the on-chip memory is used for storing and reading data. The fault diagnosis coverage rate of the on-chip memory is improved, and the availability and performance of the system are not sacrificed.

According to the comprehensive service station virtual network fault diagnosis method and device provided by one or more embodiments of the invention, the fault propagation model is constructed, false fault nodes are firstly removed, and the virtual services are sorted based on the fault risk degree, so that important virtual services are diagnosed preferentially, and for each virtual service, and the interpretation capability of the corresponding suspected fault node to the virtual service is calculated, thereby constructing a fault set, and effectively improving the network fault diagnosis rate.