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1357 results about "Failure diagnosis" patented technology

Failure to diagnose: Failure to diagnose refers to the failure of a medical professional to correctly diagnose a medical condition. Failure to diagnose, delayed diagnosis and other types of misdiagnosis are common causes of medical malpractice lawsuits; see misdiagnosis and medical malpractice .

Multifunctional remote fault diagnosis system for electric control automobile

ActiveCN102183945AReduce the cost of after-sales serviceImprove the efficiency of after-sales serviceElectric testing/monitoringInteraction interfaceNetwork management
The invention relates to a multifunctional remote fault diagnosis system for an electric control automobile. The multifunctional remote fault diagnosis system comprises a remote fault diagnosis service center, PC (Personal Computer) diagnosis client sides and a diagnosis communication device. The remote fault diagnosis service center serves as a key of the system and is mainly used for realizing an automobile fault diagnosis network management function and an automobile remote fault diagnosis assistance function; the PC diagnosis client sides are mainly used for providing specific automobile diagnosis application functions and remote diagnosis interfaces for users with different rights through human-computer interaction interfaces; and the diagnosis communication device is mainly used for realizing the data communication between the PC diagnosis client sides and a vehicle-mounted network and providing diagnosis data service for upper applications. By means of the multifunctional remote fault diagnosis system, with the remote fault diagnosis service center as a core and all PC diagnosis client sides as nodes, an automobile fault diagnosis network is established; automobile diagnosis data sharing is realized by means of the diagnosis communication device; multifunctional automobile remote fault assistance and fault elimination help can be provided; automobile fault information is subjected to statistic analysis; and a reliable automobile quality report is provided for an automobile manufacturer.
Owner:WUHAN UNIV OF TECH +1

Centralized alarm monitoring system and method of power system terminal communication access network

The invention discloses a centralized alarm monitoring system and method of a power system terminal communication access network. The method comprises the following steps: alarm collection: receiving real-time alarm messages reported by device network management systems, synchronizing the real-time alarm messages of the device network management systems to an alarm processor module to provide alarm and processing data sources for alarm; alarm preprocessing: performing normalized processing and classified compression on the collected alarm messages; alarm monitoring: displaying all kinds of preprocessed alarm messages on an alarm operating floor and a network topology diagram in real time, and forwarding important alarm to operation and maintenance personnel through short messages, emails and in station message means for important focus; fault diagnosis: performing analysis, diagnosis and qualitative diagnosis on the alarm according to the experience in an alarm processing experience library, and identifying all kinds of possible fault reasons inducing the alarm; and fault processing scheduling: locating the geographic position of a fault point according to the fault diagnosis result and the operation and maintenance group in charge, processing a work order, and scheduling the corresponding operation and maintenance group to remove the fault.
Owner:INFORMATION COMM COMPANY STATE GRID SHANDONG ELECTRIC POWER +1

Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform

The invention relates to an extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform includes the following steps: (1), collected original vibration signals of mechanical and electrical equipment are decomposed according to the EEMD, white noise is added, and intrinsic mode function (IMF) components are obtained through decomposition; (2), the sensitive IMF components closely related to failure are chosen, and other irrelative IMF components are ignored; (3), the sensitive IMF components chosen through step (2) are decomposed in an orthogonal wavelet packet mode, and a wavelet coefficient of each node is obtained; and (4), envelopes are extracted from the obtained wavelet packet coefficients by adoption of the Hilbert transform and the Fourier transform, power spectrums are calculated, the power spectrum corresponding to each wavelet packet coefficient is obtained and serves as the early failure sensitive characteristic , and the sensitive characteristics are automatically obtained. Self-adapting signals can be decomposed, the sensitive characteristics can be convenient to obtain automatically, diagnosis precision and speed are improved, and a mechanical and electrical system can be diagnosed quickly, accurately and stably. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform can be applied to the field of mechanical and electrical equipment failure diagnosis.
Owner:BEIJING INFORMATION SCI & TECH UNIV

Locomotive fault diagnosis method and system

InactiveCN102042909APerfect fault diagnosis functionRailway vehicle testingDiagnosis methodsComputer science
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.
Owner:DATONG ELECTRIC LOCOMOTIVE OF NCR

Fault diagnosis method for rolling bearing based on deep learning and SVM (Support Vector Machine)

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.
Owner:CHONGQING UNIV

Intelligent integrated fault diagnosis method and device in industrial production process

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.
Owner:WUHAN UNIV OF SCI & TECH

Network fault diagnosis method based on deep learning in virtual network environment

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.
Owner:NANJING UNIV OF POSTS & TELECOMM

Sensor-fault diagnosing method based on online prediction of least-squares support-vector machine

The invention discloses a sensor-fault diagnosing method based on the online prediction of a least-squares support-vector machine. In the method, a least-squares support-vector machine online-predicting model is established, and then the measured data of a sensor is acquired on line and used as an input sample of the least-squares support-vector machine online-predicting model to realize that the output value of the sensor at the next moment is predicted in real time as the predicting model is trained on line. Whether sensor faults occur or not is detected by comparing residual errors generated by the predicting value and the actual output value of the sensor. When the faults occur, the unary linear regression for a residual-error sequence is carried out by a least-squares method to realize the identification of the deviation and drift faults of the sensor, and furthermore, measures can be more effectively taken to carry out real-time compensation for the output of the sensor. Through the sensor-fault diagnosing method, the online fault diagnosis of the sensor can be rapidly and accurately realized, and the sensor-fault diagnosing method is particularly applicable to diagnosing the deviation faults and the drift faults of the sensor.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY

Vibration monitoring and failure diagnosis method for wind generating set

The invention discloses a method for vibration monitoring and fault diagnosis of a wind generating set, which comprises the following steps: A. presetting a monitoring point, arranging a sensor is arranged, outputting an original vibration signal through the sensor, and finishing the storage of the signal through data acquisition equipment; B. sending feature value data to a receiving module through a component bus module and storing the feature value data in a data center, or obtaining the original vibration signal and store the original vibration signal in the data center through the receiving module which avoids the component bus module; C. presetting a threshold curve in the data center, the feature value data grows continuously, and the receiving module performings fault early warning when the feature value data exceeding exceed a threshold and sends sending status early warning when the feature value data continuously exceed the threshold; and D. establishing a fault diagnosis module is established in an analysis module, presetting a fault diagnosis threshold is preset, the analysis module obtaining envelope signal data from the data center by the analysis module, calculating a comprehensive fault evaluation value through the fault diagnosis module, and comparing the comprehensive fault evaluation value with the fault diagnosis threshold, thereby performing fault alarming.
Owner:BEIJING TIANYUAN SCI & TECH CREATION WINDPOWER TECH +1

Multi-parameter identification based secondary system fault diagnosing method for intelligent substation

The invention discloses a multi-parameter identification based secondary system fault diagnosing method for an intelligent substation. The multi-parameter identification based secondary system fault diagnosing method is mainly applicable to digital substation accordant with IEC61850 communication protocol. The method includes: comprehensively analyzing multi-dimensional parameters including various alarm information, telemeasuring data, telecommand states and the like of a process layer, a spacer layer, a station control layer through a network message recording and analyzing device, monitoring states of secondary devices in real time by utilizing original messages as analysis data and utilizing recording of transient state of device exception and long-term monitoring of operation states as means, and realizing fault diagnosis and state monitoring of the secondary system of the intelligent substation. The multi-parameter identification based secondary system fault diagnosing method for the intelligent substation has the advantages in two aspects including that normal states of the secondary device of the intelligent substation can be monitored, background data and maintenance reference are provided for state maintenance; exception states are identified, quick fault diagnosis is realized, effective instruction can be provided for transport maintenance, and convenience in maintenance of the substation is improved.
Owner:STATE GRID CORP OF CHINA +3
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