2336 results about "Diagnosis methods" patented technology

A fault diagnosis method for a vehicle includes steps of: detecting, by a controller, whether a reference engineering code (REC) has been generated; if the reference engineering code (REC) has been generated as a result of performing the detecting step, measuring, by the controller, a time for which the reference engineering code (REC) has been generated; after the measuring step, calculating, by the controller, a percentage of the reference engineering code (REC), based on both the time for which the reference engineering code (REC) has been generated and a preset time; after the calculating step, storing, by the controller, the reference engineering code (REC) and the percentage of the REC; and after the storing step, when a request signal is received from a diagnostic device, providing, by the controller, an output signal that includes the reference engineering code (REC) and the percentage of the REC to the diagnostic device.

The invention discloses a district power network equipment abnormal alarm signal association analysis and diagnosis method in the field of automation of scheduling of electric power systems. According to the method disclosed by the invention, the weather situation of a plant station region at which abnormal alarm information is located can be automatically identified so as to determine the information processing mode. The method disclosed by the invention has the beneficial effects that by realizing intelligent association analysis and auxiliary decision making of the equipment abnormal alarm information under the real-time state, and performing playback on historical alarm information and performing real-time treatment on manual set information under the research state, the abnormal treatment time is greatly reduced, the incidence of accidents is reduced, a real-time, rich and intelligent auxiliary decision-making support is provided for regulation and control staff, and the safe, high-quality, economical and stable operation of an electric fence is further ensured.

The invention discloses a current-magnitude-based open-circuit failure online-diagnosis method for a power tube of an inverter, belongs to the field of motor control, and aims to solve the problem of poor robustness of a current-magnitude-based open-circuit failure diagnosis technology for the power tube of the inverter. The method comprises the following steps of establishing a current observer model of a permanent magnet synchronous motor driving system in a failure-free state, comparing an observed current value with detection current to obtain a three-phase current residual, converting the three-phase current residual to a two-phase coordinate system in a coordinate conversion way to obtain a current residual vector, standardizing the current residual vector, and diagnosing and positioning an open-circuit failure of the power tube of the inverter according to the amplitude and the phase of the standardized current residual vector. The current-magnitude-based open-circuit failure online-diagnosis method for the power tube of the inverter is free of influence of a system closed-loop control algorithm and insensitive to loads, and has higher robustness to parameter errors, measurement errors, system disturbance and the like.

The invention discloses a locomotive fault diagnosis method and system. The method comprises the following steps: obtaining monitored data acquired when a sampling device monitors locomotive equipment; comparing the monitored data with an equipment variable state or a data threshold in the fault information of the locomotive equipment in a database; if the variable state of the monitored data is abnormal or a sampling value exceeds a fault data threshold, calling fault prompting information of the fault information of the locomotive equipment from the database and displaying the fault prompting information; storing the fault information and monitored data of the locomotive equipment in storage equipment of a locomotive control unit; and carrying out fault handling on the locomotive equipment according to the fault prompting information of the locomotive equipment. Therefore, by utilizing the locomotive fault diagnosis method and system provided by the invention, all equipment of the whole locomotive can be monitored, the fault handling is carried out on the locomotive equipment with faults, and the fault information is stored, therefore, a set of complete locomotive fault diagnosis method and system is established, so that the function for locomotive fault diagnosis is more perfected.

The invention discloses a network diagnosis method based on a software defined network (SDN). The method includes the steps that a switchboard sets the rule of debug option of a flow table item according to a flow table debug setting command sent by a controller; the switchboard matches the key field of the message heading with the matching field of the flow table item, and judges whether the flow table item matched with the message is set up with a debug option field or not; if yes, the switchboard executes relevant debugging operations according to the content of the debug option field, and the operations include, but not limited to, sending the matching message and the contextual information to the controller; and the controller proceeds network diagnosis according to the message and the contextual information. The invention further discloses a network diagnosis system and equipment based on the SDN. With the technical scheme provided by the invention, the cycle for positioning a network problem is accelerated, the quality of the network is improved, and the operating maintenance cost of the network is reduced.

The invention provides a fault diagnosis method for a rolling bearing based on a deep learning and SVM (Support Vector Machine). The method comprises using a manure learning algorithm in a deep belief network theory to complete a characteristic extraction task needed by fault diagnosis; automatically extracting the substantive characteristics of data input independent of manual selection from simple to complicate, from low to high, and automatically digging abundant information concealed in known data; in addition, classifying and identifying a test sample by adopting an SVM classification method, seeking and finding a global minimum of a target function through an effective method previously designed, so as to solve the problem that a deep belief network may be trapped into a locally optimal solution. According to the fault diagnosis method for the rolling bearing based on the deep learning and SVM provided by the invention, the accuracy and effectiveness of the fault diagnosis method for a rolling bearing can be improved, and a new effective way can be provided to solve the accuracy and effectiveness of the fault diagnosis method, therefore the fault diagnosis method can be extensively applied complex systems in chemistry, metallurgy, electric power, aviation fields and the like.

The invention relates to an intelligent integrated fault diagnosis method in an industrial production process. The intelligent integrated fault diagnosis method is characterized by comprising the following steps of: acquiring data in the industrial production process; analyzing and processing object characteristics according to an acquired signal; combining expert knowledge according to an intelligent integration method to carry out blast-furnace fault diagnosis analysis so as to identify a fault and find out a reason of the fault, carrying out fault exact location and diagnosis policy, and effectively regulating a production process so that the industrial production process can regularly carry out, wherein the intelligent integration method comprises the following steps of: establishing a Bayesian network model; comprehensively analyzing and processing FTA (full type approval) and FMEA (failure mode and effect analysis) models; and carrying out nerve net expert system fault diagnosis analysis and process. Simultaneously, the invention further relates to an intelligent integrated fault diagnosis device in the industrial production process, and the device is used for realizing the fault diagnosis method. According to the intelligent integrated fault diagnosis method in the industrial production process, disclosed by the invention, various information is fused, ratiocination is carried out under a complex situation, comprehensive diagnosis can be effectively carried out on the fault of the industrial production process, the integration, intelligence, accuracy and effectiveness of the fault diagnosis system are improved, and the production process is ensured to be performed smoothly.

The invention discloses an automatic diagnosis method for electrocardiographic abnormality and relates to the technical field of automatic diagnosis of electrocardiographic abnormality. The learning capacity of an RNN neural network to a time sequence and the learning capability of CNN to spatial features are combined to learn features of the electrocardiographic biological signal, and abnormal electrocardiographs of different types are automatically characterized. Then, a deep neural network based classifier is constructed, which is trained using an electrocardiograph with type annotations to improve the accuracy of classification, and automatic classification of different arrhythmia types is achieved. According to the invention, the process of manually extracting features is avoided; then, time sequence features and spatial features of the electrocardiographs are learned using RNN and CNN to form the classifier, and the classifier is trained through supervised learning so that the classifier can automatically diagnose abnormal electrocardiographs. As a result, the accuracy of automatic diagnosis and classification of electrocardiographic abnormality is improved.

The invention provides an intelligent arrhythmia diagnosis method based on multiple-lead and a convolutional neural network. The method includes the steps that 1, data samples are selected; 2, arrhythmia types are labeled; 3, led heartbeat signals are intercepted; 4, a normalized heartbeat set is obtained; 5, a concealed layer and an output layer are constructed; 6, a target function is set; 7, sample training is conducted; 8, arrhythmias classification is applied. According to the intelligent arrhythmia diagnosis method, network learning efficiency and precision of automatic arrhythmia diagnosis can be improved by training the convolutional neural network (CNN) through multiple-lead electrocardiogram data, a universal frame and a specific method for training the CNN through the multiple-lead electrocardiogram data with arrhythmia type labels are achieved, the arrhythmia types of electrocardiosignals to be diagnosed can be accurately judged, and the arrhythmia types can serve as diagnosis results or as reference of doctors.

The invention discloses an SVM classification model-based equipment fault diagnosing method. The method comprises the following steps: performing a preprocessing operation on equipment data; constructing a fault diagnosis case knowledge base; performing fault diagnosis on a support vector machine on the basis of an SVM classification model; acquiring fault information and performing maintenance guide. According to the support vector machine-based intelligent equipment fault diagnosis method, the fault features of equipment are highlighted to the maximum degree; the situations that the equipment data are incomplete and inaccurate are reduced; the possibility is provided for constructing an accurate and reliable fault diagnosis model; the problem that the diagnosis model ages along with the operating time of the equipment is solved; the misdiagnosis rate of the fault diagnosis is reduced; the accuracy and the speed of the equipment fault diagnosis are greatly improved.

The invention provides a universal circuit breaker mechanical fault diagnosis method based on feature fusion of vibration and sound signals. The method includes steps of 1, collecting machine vibration signals and machine sound signals during an engaging and disengaging process of a universal circuit breaker; 2, adopting an improved wavelet packet threshold value denoising algorithm for denoising; 3, adopting a complementary total average empirical mode decomposition algorithm for extracting a plurality of solid mode function components reflecting state information of engagement and disengagement actions of the circuit breaker from the denoising signals; 4, determining the number Z of the solid mode function components; 5, calculating the energy ratio, the sample ratio and the power spectrum entropy as three types of features; 6, adopting a combination core principal component analysis method for performing dimension reduction on a feature sample with unified three types of features of the vibration and the sound signals and obtaining M principle components; 7, establishing a related vector machine based sequence binary tree multiple classifier model.

The automated fault diagnostic system operates on engine-compressor sets with one vibration sensor per sub-group of engine cylinders and one sensor per compressor cylinder. Vibration signals linked to crankshaft phase angle windows (“VT”) mark various engine events and compressor events. In data-acquisition-learning mode, VT is stored for each engine and compressor event per operating load condition, statistical process control (SPC) theory identifies alarm threshold bands. Operator input-overrides are permitted. If no baseline data is stored, the system automatically enters the learn mode. To monitor, current VT are obtained and current load condition is matched to the earlier load set and alarms issue linking predetermined engine or compressor event to the over-under VT. Baseline data, SPC analysis, alarms and monitoring are set for crankcase flow, engine cylinder exhaust temperatures, ignition system diagnostic messages. Compressor performance alarms use suction and discharge temperatures and pressures.

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

An abnormality diagnosis device used for a machine apparatus having a part rotating and sliding relative to a stationary member is provided with a detection section (31) and a signal processing section (81). The detection section (31) is fixed to the part rotating or sliding or to the stationary member and has a vibration sensor (32) and a temperature sensor (33). The signal processing section (81) determines the condition of the part based on a signal detected and outputted by the detection section (31). The signal processing section (81) determines whether there is an abnormality in the part, or whether there is an abnormality in the part and the degree of damage of the part.

The invention relates to a drive circuit for an injector arrangement comprising a fuel injector, and method of detecting faults in the drive circuit. The drive circuit comprises diagnostic means (R_H, R_L) that is operable to sense a measured voltage (V_BIAS) between the injector and a known voltage level (V_BAT, V_GND). The measured voltage (V_BIAS) is biased with respect to the known voltage (V_BAT, V_GND) to a predicted voltage (V_PinjN, V_Bcalc) unless the drive circuit has a fault. A fault signal is provided on sensing of a measured voltage (V_BIAS) that differs from the predicted voltage (V_PinjN, V_Bcalc). The drive circuit may additionally, or alternatively, comprise diagnostic means (R_F). The diagnostic means (R_F) is operable to sense a detected current (I_dect) and to provide a fault signal on detection of a fault, when the detected current (I_dect) is at variance from a threshold current (I_trip).

The invention discloses an electrocardiogram diagnosis method and system based on a deep convolution neural network. The diagnosis method comprises the steps that a collected original electrocardiogram of the human body is preprocessed to obtain a plurality of heartbeat fragments with a preset length; the heartbeat fragments are input into a trained deep convolution neural network model, and a prediction value of each category is obtained; the category of a single heartbeat corresponding to the maximum prediction value and the time interval of the heartbeat and a previous heartbeat are recorded, and a time series is formed; on the basis of a preset mapping relation between a clinical diagnosis criterion and the time series, the time series is matched with the diagnosis criterion, and a corresponding diagnosis result is obtained. Any feature in the electrocardiogram is not required to be extracted, so that valid information loss, noise data introduction and other problems caused by manual screening and processing are avoided to the largest extent, and the influence of manual characteristic extraction on the diagnosis accuracy is reduced.

The invention provides a bearing fault classification diagnosis method based on sparse representation and an LDM (large margin distribution machine), overcomes the defects that signal decomposition is incomplete, a reconstructed signal cannot better keep features of an observed signal and the like in the conventional single-channel mechanical compound fault diagnosis method. According to the method, signal conversion from one dimension to high dimension is realized with a CEEMD (complete ensemble empirical mode decomposition) method, the decomposition completeness is guaranteed, and a mode mixing phenomenon is inhibited; meanwhile, a dimensionality reduction method based on sparse representation is introduced into a feature extracting and processing process of a blind source signal, data are subjected to sparse reconstruction through sparse representation, and data feature information is extracted from global data, so that the reconstructed signal can better keep the data features of the observed signal; further, the LDM classification method is introduced into a model fault type classification processing process of a to-be-detected bearing, and the accuracy and effectiveness of bearing fault diagnosis can be improved by aid of the generalization ability of the LDM classification method.

The invention discloses an analog circuit dynamic online failure diagnosing method based on GSD-SVDD, belonging to the technical field of analog circuit failure diagnosis. In an offline test process, a KFCM algorithm is adopted to calculate a failure resolution value of each testable node and an optimal test node set is selected according to the failure resolution value. In an online diagnosis process, a failure diagnosis model is established by adopting an SVDD single classification approach based on a map spatial distance positive and negative sample weighting, test samples are diagnosed by a layered diagnosis method, and a failure class library and the diagnosis model are renewed dynamically. The method effectively reduces the drill and online diagnosis time of the diagnosis model, guarantees the real-time property of the online diagnosis and improves the precision of the failure diagnosis and can dynamically renew parameters of the diagnosis model so as to enable the a diagnosis system to have the self-adaption capability.

A self-adaptive stealing-leakage diagnosis method comprises following steps of step 1: acquiring historical sample data of electric quantity, load, alarm and line loss from a metering main station; step 2: pre-processing the historical sample data; step 3: building an expert sample bank; step 4: building a stealing-leakage diagnosis model; step 5: conducting the stealing-leakage diagnosis; and step 6: optimizing model parameters, and rebuilding a model. The self-adaptive stealing-leakage prevention diagnosis method has a self-learning property, by building a model by learning power consumption data of electric power users, and reasonability and accuracy of new data are diagnosed through the formed power consumption principle, so that the monitoring and diagnosis accuracy of the stealing and leakage of electric power can be effectively improved.

A network system in which an electrical apparatus having a battery which performs charges and discharges to supply electric power to the electrical apparatus and a managing server are connected by the network and perform a diagnosis of the battery provided for the electrical apparatus. The electrical apparatus accesses a Web page provided by the managing server through the network and then transmits an information file about the battery to the managing server. The managing server reads the information file to analyze the information about the battery and then transmits the analytical results to the electrical apparatus.

The invention discloses a wind-power gear box fault diagnosis method based on wavelet medium-soft threshold and electronic-magnetic diaphragm (EMD), which includes: A. measuring a wind-power gear box by utilizing an acceleration sensor to obtain vibration accelerator signals; B. performing wavelet analysis on the signals, performing de-noising preprocessing through a wavelet medium-soft threshold method to eliminate interfering noise and obtain de-noising signals; C. performing EMD analysis on the de-noising signals to obtain each intrinsic mode function (IMF) component; D. judging true and false of the IMF components according to the related coefficients between the IMF components and original signals, rejecting false IMF components, and analyzing true IMF components to obtain components containing fault characteristics; and E. obtaining envelope spectrums containing fault characteristic components to draw fault characteristics. The wind-power gear box fault diagnosis method combines wavelet medium-soft threshold de-noising and the EMD and is effective in fault characteristic information drawing.

An intelligent defect diagnosis method for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layouts and fabrication data; analyzing the defect data, design layouts, and the fabrication data by a defect analysis system, wherein the analyzing step further contains the sub-steps: segmenting and grouping the design layouts into pluralities of multi-pattern group cells to construct LPG cell based pattern groups; introducing the defect data; segmenting defect image into pluralities of defect and pattern contours; mapping the defect data to each multi-pattern group cell to form the LPG based defect composite pattern group; performing coordinate conversion and pattern match between image contour and design layout for coordinate correction; fulfilling CAA with defect contour, pattern contour and design layout, and obtaining corresponding defect yield; classifying the defect type of defect data through defect image classification analysis.

A plasma processing apparatus includes at least three gas supply lines connected to a process chamber in parallel to allow a gas to flow therethrough, and at least three flow-rate controllers provided on the gas supply lines to detect the flow rate of the gas flowing through each of the flow-rate controllers to control the flow rate to a set value and a diagnosis method. The apparatus has a function of splitting and supplying a gas controlled to a predetermined flow rate by the third flow-rate controller, to a first flow-rate controller for the smallest detectable range of the three flow-rate controllers and to a second flow-rate controller, in order to test the operation of the control of the flow rate of the first flow-rate controller, based on the value obtained from the flow rate of the gas flowing through the second flow-rate controller and the predetermined flow rate.

The invention discloses a wavelet transformation and deep learning-based rolling bearing weak fault diagnosis method. The method comprises the following steps: a rolling bearing vibration signal is obtained, and the acquired vibration signal is subjected to continuous wavelet transformation to obtain a time-frequency diagram; autocorrelation operation is performed on wavelet coefficients corresponding to each frequency on the time-frequency diagram to filter out noise interference and extract periodic fault components; the Hilbert transformation is used to perform envelope demodulation to obtain a fault characteristic frequency; a processed time-frequency diagram is input as a feature diagram, and categories of early faults are determined by training a deep learning classification model. Theoretical and experimental results show that fault categories can be determined accurately at an early stage when weak fault of a rolling bearing occur based on an improved wavelet time-frequency diagram used as the classification model for input training, right determination results can be given when the method is applied to different bearings, high training speed can be realized, and high actual application value can be achieved.

The invention discloses a dispatching end grid fault diagnosis method based on wide-area fault recording information. The method comprises the following steps that when a grid breaks down, fault data of a primary system of the grid are recorded according to a certain sampling frequency mode and are sent to a dispatching main station end through a fault recording networking system; the mapping relation between a basic data platform of grid equipment and a fault recording system is established through CT identification; different sampling frequencies of a fault recorder are unified to be the same sampling frequency according to an interpolation method; a differential-current out-of-limit value is set and independent grid equipment serves as a calculation unit to carry out differential-current calculation; whether the differential-current value is out of limit is confirmed to position fault places and confirm protection actions. The dispatching end grid fault diagnosis method has the advantages of solving the key problems that sampling frequency of the fault recorder is different and data are hard to synchronize, and achieves fault diagnosis application functions such as system operation state monitoring, protection behavior analyzing and accurate fault positioning through the fault recording information only.

A digital cable TV receiver includes a POD module and a host device. The host device includes a controller and a HDMI port linked to a first peripheral device. When the controller receives a diagnostic request for HDMI status from the POD module, it generates a HDMI status report and transmits the DVI status report to the POD module. The HDMI status report includes HDMI status information associated with the first peripheral device. If the host device further includes a DVI port linked to a second peripheral device and receives a diagnostic request for HDMI/DVI status from the PDO module, it generates a HDMI/DVI status report and transmits the HDMI/DVI status report to the POD module. The HDMI/DVI status report includes HDMI status information associated with the first peripheral device and DVI status information associated with the second peripheral device.

The invention discloses a rolling bearing fault diagnosis method based on composite multi-scale permutation entropies, and belongs to the technical field of fault diagnosis. The method comprises the following steps: measuring a vibration signal of a faulty object; extracting composite multi-scale permutation entropies from the vibration signal; reducing the dimension of the composite multi-scale permutation entropies with use of a Laplacian score; taking the first multiple composite multi-scale permutation entropies with low scores after dimension reduction as fault feature vectors and dividing the fault feature vectors into multiple training samples and multiple test samples; inputting the multiple training samples into a multi-fault classifier established based on a support vector machine to perform learning so as to classify the test samples; and identifying the working mode and the fault type of the faulty object according to the classifying result. According to the fault diagnosis method disclosed by the invention, feature extraction is highly innovative, and the degree of identification is high in the process of fault mode identification.

The invention discloses an automatic expert comprehensive fault diagnosis system and a diagnosis method of a wind power unit, including: a data acquisition module, which collects relevant data of a main control unit and a vibration signal acquisition unit and transmits it to the fault diagnosis module; a fault diagnosis module, which is comprehensively used Neural network knowledge and expert system module knowledge, use forward reasoning and real-time parameter fluctuation values ​​to compare with the data in the fault sample knowledge base, and generate fault information in real time; the fault diagnosis module will diagnose the fault information through the man-machine Interface module for output. The present invention uses the main control unit to read parameters such as wind turbine instantaneous power, rotational speed, propeller angle, cabin temperature, wind speed, oil temperature, yaw state, etc. to improve the accuracy of fault diagnosis, and utilizes the neural network expert system based on wavelet packet analysis Carry out automatic analysis, and give operation and maintenance suggestions through the expert diagnosis system.

The invention relates to a fault monitor and diagnosis method of a continuous annealing unit, in particular to a PCA (Principle Component Analysis) model based furnace temperature and tension monitoring of a continuous annealing unit, mainly comprising the following steps of firstly, according to process variable data obtained in the field, and establishing a temperature and tension monitor modelby utilizing a principle component analysis PCA method; secondly, establishing an off-line model and calculating the T2 statistics quantity and the SPE statistics quantity as well as contributed control limits thereof by utilizing the data, obtained in step one, when process variable is in a normal work condition; thirdly, applying an on-line model, calculating the T2 statistics quantity and the SPE statistics quantity of current data, monitoring whether a current state is normal or not according to information supplied by the off-line model, and giving alarm signals if abnormal; fourthly, determining a leading variable which causes a fault by utilizing contribution of the T2 statistics quantity and contribution of the SPE statistics quantity. The invention monitors the furnace temperature and tension in real time in the production process and traces back a fault reason for leading to system abnormality when the abnormality occurs.

The embodiment of the invention provides a network fault diagnosis method and device. Automatic locating of network faults can be realized without manual intervention; furthermore, fault root causes can be determined; and thus, automatic fault diagnosis is realized. The method comprises the steps of: acquiring log, alarm, configuration and KPI data of network elements, network management equipment and monitoring equipment, and detecting an abnormal network element and abnormal information according to the acquired data; according to the abnormal information and the pre-stored corresponding relationship, determining a first fault event; according to the first fault event and a pre-stored fault rule library, determining a first fault rule; and, acquiring the real-time data of the abnormal network element, confirming the suspected fault event in the first fault event and a second fault event, performing logic calculation according to the suspected fault event and second fault event confirmation result and the fault rule, and determining root causes causing faults of a network system. The network fault diagnosis method and device provided by the invention are applied to the technical field of computers.