1357 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 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 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.

A computer implemented process is provided for assessing and characterizing the degree of success or failure of an operational event of a machine system such as a fluid compressor machine or turbine machine or the like on a continuous numerical scale. The computer implemented process develops and tracks machine unit signatures, machine site signatures and machine fleet signatures to evaluate various operational events and provide fault detection. At least some sensor data acquired from the machine system during an operational event is transformed to correct or at least reduce variabilities in the data caused by ambient conditions and fuel quality. The transformed data is then analyzed using statistical methods to determine how closely the operational event conforms to an expected normal behavior and the information is used to develop a single comprehensive quality assessment of the event. By saving, tracking and updating operational event assessments over time, machine/component degradation may be recognized at any early stage and corrective action may be initiated in advance of a catastrophic failure.

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.

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.

A wiring fault detection, diagnostic and reporting technique enables linking devices within a process control system to measure the electrical characteristics of a segment protocol bus and the electrical characteristics of the signals transmitted via the protocol bus. The technique connects a signal line of a segment protocol bus to one of a plurality of measurement blocks within a wiring fault detection unit. The one of the plurality of measurement blocks measures an electrical characteristic associated with the segment protocol bus and sends the measured electrical characteristic to a wiring fault diagnostic manager. The wiring fault diagnostic manager analyzes the measured electrical characteristic to determine a type of the wiring fault and reports the type of the wiring fault via a user interface.

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.

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.

A system and method is provided for fault detection in a turbine engine. The fault detection system and method uses discrete event system modeling to provide improved fault diagnosis and prognosis. The fault detection system and method receives sensor taken at multiple dynamic events occurring in different time windows. An event determination mechanism evaluates the received sensor data to determine if specified events have occurred. Indications of the occurrence of specified events are then passed to a discrete event system model. The discrete event system model analyzes the timing and sequencing of the event occurrences to determine if a fault has occurred. This method does not require a detailed modeling of the system, and thus can be applied to complex systems such as turbine engines.

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 method of detecting abnormality of an information processing apparatus to be started by biometric authentication without being started when the apparatus is inoperable. The information processing apparatus performs the biometric authentication using obtained biometric data of a user and pre-registered reference biometric data of the user in order to allow the apparatus to be used. When the apparatus cannot be started, the information processing apparatus outputs the reference biometric data, a detecting apparatus capable of obtaining the reference biometric data obtains the reference biometric data, the detecting apparatus obtains biometric data of the user and detects abnormality of the information processing apparatus by comparing the biometric data of the user obtained by the information processing apparatus and the obtained reference biometric data for authentication determination.

The invention provides a bearing fault diagnosis system which comprises an intelligent bearing provided with six output ports with a first output port and a second output port outputting vibration acceleration signals, a third output port and a fourth output port speed signals and a fifth output port and a sixth output temperature signals. The system is characterized in that: the six groups of signals output by the intelligent bearing are sent to the input port of an A/D conversion module, six groups of digital signals output by the A/D conversion module are sent to a processor internally provided with a status monitor and a fault recognizer, wherein, the status monitor monitors the status of the bearing according to the vibration acceleration digital signals, the speed digital signals and the temperature digital signals, and the fault recognizer utilizes a signal processing tool to get a fault judgment result. The system of the invention has the advantages of being capable of monitoring the status of the bearing in an on-line manner according to the three types of signals of the intelligent bearing on the basis of the current intelligent bearing provided with a compound sensor so as to judge which part a fault takes place in.

A system and method for fault diagnosis includes receiving information defining a relationship between failure modes and diagnostic trouble codes and extracting diagnostic trouble code data, including set times, frequency data and diagnostic trouble code sequence information for a plurality of diagnostic trouble codes relating to a plurality of failure modes. The system and method further include constructing a Markov chain using the diagnostic trouble code data for each of the plurality of failure modes, training the Markov chain to learn a set of state parameters using the diagnostic trouble code data, and computing a likelihood of a diagnostic trouble code sequence for each of the plurality of failure modes using the trained Markov chains.