2803 results about "Failure diagnosis" patented technology

A system for condition monitoring and fault diagnosis includes a data collection function that acquires time histories of selected variables for one or more of the components, a pre-processing function that calculates specified characteristics of the time histories, an analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the one or more components, and a reasoning function for determining the condition of the one or more components from the one or more hypotheses.

An acoustic channel is used for fault diagnosis, repair, and upgrades. Remote diagnosis uses self-test data encoded into sound waves. Repair data and upgrades are also encoded and transmitted as sound waves.

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.

The method comprises: using a sensor to collect the characteristic signals; the collected signals about each kind of running state are sent to the data processing module; after processed, the signals are sent to the computer system; said computer system comprises a device database, a symptom event database, a diagnosis knowledge database; a device database and signal analysis module, a symptom acquiring module, a diagnostic reasoning module, and a diagnosis explaining module; the software of the data processing module makes data exchange; the symptom event database exchanges data with the diagnostic module, data processing module; the diagnostic knowledge exchanges the data with the failure processing module, knowledge acquiring module and data processing module.

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.

In an embodiment of this invention, a diagnosis engine refers to a risk assessment DB and anti-risk measure DB, calculates along the time sequence a risk estimate amount in case of failure for each device and a maintenance cost in executing a measure method in advance, and adds the risk estimate amount and maintenance cost along the time sequence to calculate an optimum maintenance period of the device. When a failure has occurred in the device, the diagnosis engine refers to a failure diagnosis DB on the basis of the identification information of the device and the failure mode and analyzes, on the basis of an operation record, a failure which is different from a secular change.

The invention discloses a vibration monitoring-based wind generator set automatic fault diagnosis method. The method comprises the following steps: an acceleration sensor collects original vibration signals, a pre-communication subsystem stores and uploads data, and a central monitoring server receives data and stores the data in a database. The method also comprises the step of establishing a fault characteristic frequency library in the database which is used as the basis of fault diagnosis. The method of the invention can automatically realize the fault early warning and fault diagnosis of the key parts of the wind generator set; and the fault characteristic frequency pool can be continuously updated to realize the self-learning function of the method, thus improving the fault diagnosis function continuously.

Systems and methods for capturing and analyzing significant parameter data from vehicle systems whenever a diagnostic trouble code (DTC) is triggered. A multi-dimensional matrix is constructed, with vehicles, DTCs, and parameter data comprising three dimensions of the matrix. The data matrix is populated with DTC and parameter data from many different vehicles, either when vehicles are taken to a dealer for service, or via wireless data download. Time can be added as a fourth dimension of the matrix, providing an indication of whether a particular system or component is temporally degrading. When sufficient data is accumulated, the data matrix is pre-processed, features are extracted from the data, and the features are classified, using a variety of mathematical techniques. Trained classifiers are then used to diagnose the root cause of any particular fault signal, and also to provide a prognosis of system health and remaining useful life.

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.

The invention discloses a log management and fault diagnosis method and device under multiple hosts. The method comprises fault log collection, fault log analysis and fault log correlation analysis. Fault log collection comprises collecting fault logs of all hardware and software in the cluster and storing the logs in a log server uniformly. Fault log analysis comprises filtering the fault logs, extracting log template information and classifying the logs according to log types. Fault log correlation analysis comprises performing fault reason analysis by using log analysis results and combining time windows, clustering the related faults caused by the same fault into a category and trying to find the root of the type of the faults. The method and the device are capable of analyzing the system operation condition effectively, assisting administrators to process system faults and improving the fault type determination accuracy.

The invention provides a fault predicting and diagnosing method suitable for a dynamic complex system. The method can be applied in the field of fault prediction and diagnosis of dynamic complex systems of spacecrafts and the like. The method comprises the following steps of: performing failure mode and effect analysis (FMEA) on the dynamic complex system to obtain a main fault mode and corresponding performance detection parameters, dividing the performance detection parameters into slowly variable data and fast variable data, pre-processing the performance detection parameters, establishingan autoregressive moving average model (ARMA) aiming at the slowly variable data to perform time sequence prediction, establishing a multi-resolution wavelet neural network aiming at the fast variable data to perform time sequence prediction, performing fault early warning on the time sequence prediction results by establishing a prediction interval model, and performing fault diagnosis by establishing a D-S (Dempster-Shafer) evidence theory-based multi-signal fusion model. The method can be used for predicting and diagnosing the faults of the dynamic complex system with high precision, and has strong universality.

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.

An instantaneous current and instantaneous voltage of a three-phase coil of a brushless DC motor are detected, and the internal condition of a compressor is predicted from these detection values. The prediction about the internal condition of the compressor is made in such a way that motor driving torque which is a parameter of a motor model is identified and poor lubrication, liquid compression or the like is predicted from this identified motor driving torque. This enables it to make, in real time, a failure forecast, failure diagnosis etc. on the compressor.

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 present invention discloses an implementation method of a long-distance state monitor and failure diagnosis system of a novel numerical control machine, and belongs to the numerical control machine, information processing, network technology, industrial control technology fields, and relates to the digital control technology, long-distance telemetry tracking and command, failure diagnosis, network communication, automatic control and signal processing, and mainly makes the use of the modern long-distance telemetry tracking and command technology and the failure diagnosis analysis technology, and combines the characteristics of the state monitor and failure diagnosis of the numerical control machine, and builds the method of the state monitor and failure diagnosis system of the novel numerical control machine based on the wired and the wireless internet.

A vehicle fault diagnosis and prognosis system includes a computing platform configured to receive a classifier from a remote server, the computing platform tangibly embodying computer-executable instructions for evaluating data sequences received from a vehicle control network and applying the classifier to the data sequences, wherein the classifier is configured to determine if the data sequences define a pattern that is associated with a particular fault.

The invention provides a fault on-line diagnosis and early warning method of a flameproof dry-type transformer for mine in order to raise the accuracy and rapidity of fault diagnosis. The method comprises the following steps: determining monitoring quantity; extracting characteristic values: extracting a three-dimensional spectra parameter and two-dimensional statistical parameter in a partial discharge signal as characteristic quantities, with regard to operation voltage, current and iron core leakage current, extracting effective values of the operation voltage, the current and the iron core leakage current as characteristic values, and taking real-time values of a temperature parameter as characteristic quantities; using a normalization method to calculate a corresponding value of each characteristic quantity and taking the corresponding value as an input parameter of an intelligent diagnosis system; collecting values of each monitoring quantity under different environment, and obtaining training and testing samples of a nerve network under corresponding environment; establishing a nerve network: selecting a generalized RBF nerve network intelligent diagnosis method to establish a nerve network; utilizing a sample data training nerve network and forming a fault diagnosis tool; establishing a database; storing collected real time data and the above diagnosis result into a ground server real-time database and a real-time early warning information table, and carrying out diagnosis and early warning through an expert system.

The invention discloses a gas pipeline leakage detecting and positioning device and a method based on transient model method, which belong to the technical field of failure diagnosis and flow safety security. The detecting device comprises a sensor group, a signal pre-processing device, an I/V plate, a collecting card, a voltage-stabilized power supply, and an operating condition machine; the detecting method includes of establishing a math model for the liquid flowing in the pipe, solving the flow parameters change in the pipe through a computer under a certain boundary condition, and comparing the model calculating values with the true measuring values at the pipe end; when the difference of the true measuring values and calculating values exceeds a certain range, then a leakage can be identified, and the leakage is positioned through a pressure gradient method. The invention provides a leakage detecting method based on the transient model method, and has the advantages of less investment, high sensitivity and good adaptability, and can be used for detecting and positioning gas leakage and leakage point for on-site gas pipeline.

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 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.