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2336 results about "Diagnosis methods" patented technology

The four methods of diagnosis consist of observation, auscultation and olfaction, interrogation, pulse taking and palpation. Observation indicates that doctors directly watch the outward appearance to know a patient's condition.

Current-magnitude-based open-circuit failure online-diagnosis method for power tube of inverter

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.
Owner:HARBIN INST OF TECH

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

Bearing fault classification diagnosis method based on sparse representation and LDM (large margin distribution machine)

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.
Owner:CHONGQING UNIV

Dispatching end grid fault diagnosis method based on wide-area fault recording information

ActiveCN103837795AResolution frequencySolve key problems such as difficult data synchronizationFault locationInformation technology support systemDiagnosis methodsCondition monitoring
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.
Owner:STATE GRID SHANDONG ELECTRIC POWER +2

PCA (Principle Component Analysis) model based furnace temperature and tension monitoring and fault tracing method of continuous annealing unit

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.
Owner:SHANGHAI BAOSTEEL IND TECHNOLOGICAL SERVICE +1
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