277 results about "Fault tree analysis" patented technology

A controller for a building management system is configured to analyze faults in the building management system. The controller detects a fault in the building management system by evaluating data of building management system using a system of rules. The controller determines a conditional probability for each of a plurality of possible fault causes given the detected fault. The controller determines the most likely fault cause by comparing the determined probabilities and electronically reports the most likely fault cause.

The invention relates to an intelligent warning and fault analysis realizing method applied to an intelligent transformer substation monitoring system, which belongs to the technical field of transformer substation automation. An intelligent warning and fault analyzing module is used for analyzing various pieces of information reported by a measurement and control device, a protective device and a fault recorder, generating a fault analysis report by combining topology with every fault in an automatic identification fault reasoning model, and submitting the generated fault analysis report to a scheduling master station at a master station end. The workload of personnel on duty is greatly reduced, and the fault processing efficiency is improved. The requirement of an intelligent transformer substation on an intelligent warning and fault analysis system can be met, faults can be quickly judged and analyzed, and processing schemes can be provided so as to assist the personnel on duty in quickly processing the faults, and reduce the loss caused by the faults.

The invention discloses a method for performing fault diagnosis by using model conversion. According to the method, processed related information of a fault mode effect analysis model is converted into a corresponding Bayesian network model by using a self-defined data structure while ensuring complete data, an elementary event, a logic gate and an intermediate event of a fault tree in a fault tree analysis model are converted into nodes in a Bayesian network respectively, and a corresponding conditional probability table in the Bayesian network is set. The fault diagnosis is performed through the converted Bayesian network model. The method of the invention expands the use of the Bayesian network model in the fault diagnosis, improves the diagnosis accuracy of a fault diagnosis model in practical application, ensures the universality of model conversion, and can realize cross-tool conversion among different fault mode effect analysis, fault tree analysis result and the generated Bayesian network.

The invention relates to a rolling bearing fault diagnosis method. The method uses a learning algorithm of a CNN (Convolutional Neural Network) theory to complete a feature extraction task needed by fault diagnosis, and does not rely one manual selection, intrinsic features of input data are extracted automatically from simple to complex and from low-level to high-level, and abundant information hidden in known data can be dug out automatically; and a support vector regression method is used to identity a test sample in a classifying manner, support vector regression with a high generalization capability can be used to identity an unknown new sample in higher precision, and the disadvantage that a default classifier of deep learning tends to be low in the generalization capability can be overcome when support vector regression serves as a classifier to identify samples in the classified manner. The rolling bearing fault diagnosis accuracy and validity can be improved, a new effective approach is provided for solving problems in rolling bearing fault diagnosis, and the method of the invention can be widely applied to fault analysis of complex mechanical systems in the fields of chemical industry, metallurgy, electric power, aviation and the like.

Embodiments of the invention provide techniques for troubleshooting of computer systems using a fault tree analysis. In one embodiment, data parameters describing a status of a system may be monitored to determine the existence of a fault. In the event of a fault, fault tree analysis metadata may be evaluated to attempt to determine a root cause of the fault. If a root cause can be automatically determined, it may be presented to a user in a troubleshooting console, or may be used to trigger an automated corrective action. Alternatively, if a root cause cannot be automatically determined, the user may be presented with additional fault tree analysis metadata and any relevant data parameters in the troubleshooting console, so that the user may determine the root cause of the fault event.

The application discloses a method for fault analysis, comprising the following steps of: obtaining original alarm information of multiple fault alarm events; preprocessing the original alarm information to obtain standard alarm information; calculating the standard alarm information by using an Apriori algorithm, and obtaining a correlation degree between each fault alarm event; obtaining a corresponding correlation rule according to the correlation degree between the fault alarm events; and analyzing the target fault alarm events by using the correlation rule, and obtaining a fault analysisresult when the target fault alarm events are received. According to the method for the fault analysis, the fault alarm event can be effectively analyzed, so that the processing efficiency of the fault of the operation and maintenance is improved, and the reliability of the system is further enhanced. The application further discloses a device for fault analysis, equipment, and a computer readablestorage medium, which have the above beneficial effects.

The invention provides an intelligent fault diagnosis method of a numerical control machine tool. The intelligent fault diagnosis method uses fault tree diagnosis as a base, faults are diagnosed by combining a Bayes probability and rule reasoning method, fault cause is found, and maintenance advices are proposed. The intelligent fault diagnosis method includes: firstly extracting fault information from historical maintenance records and machine tool operation instructions, and establishing a numerical control machine tool fault information knowledge base; searching fault trees belonging to a fault position in the fault information knowledge base according to fault characteristic information, calculating probability of occurrence of each fault tree under the current condition if a plurality of fault trees fit conditions, and determining sequence for diagnosing the fault trees according to size of the probability; and finally judging whether all sub-events of top events of the fault trees occur, returning to a fault treatment method corresponding to the events and serving as a solution if the sub-evens are bottom events of the fault trees, otherwise searching sub-events of the events continuously until finding all bottom events fit the fault characteristic information. The intelligent fault diagnosis method of the numerical control machine tool is rapid in diagnosis speed and accurate and reasonable in diagnosis result.

A set of fault records representing faults previously detected in an enterprise computer system is received and analyzed. The analysis comprises a variety of analytical operations and results in a report provided to a user, the report particularly including a set of fault sources identified as highly important to address, with respect both to the system as a whole and to particular categories of faults.

The invention relates to the fault tree analysis system for a nuclear power plant with advanced boiling water reactor. The full digital instrument control system uses six different modes to simulate the transmission of the digital signals and the analog signals from the detection units. It is to develop the fault tree for various signal transmission modes to support the nuclear power plant in probabilistic risk assessment (PRA) and meet requirements for simulated signal detection, transmission, logic operation and equipment actuation. Thus, the digital instrument control flow process can fit into PRA model and properly reflect its importance in risk assessment.

The invention provides a single phase grounding fault line selection method for medium voltage distribution networks based on fault process analysis, which comprises the steps of calculating neutral point voltage, determining whether the neutral point voltage exceeds a preset threshold value, diagnosing fault type, determining whether the fault is a single phase grounding fault, describing fault process, positioning line selection area, selecting line for the fault, outputting result and finishing. The method can reflect the process of fault development and evolution intuitively and constitutionally, effectively selects the line for stable grounding with single phase grounding fault time more than 500 milliseconds, and can totally capture and describe transient fault during the fault process.

The invention discloses a preventative device maintenance method based on dynamic reliability and belongs to the technical field of petrochemical equipment management. The preventative device maintenance method based on the dynamic reliability comprises the following steps of 1 establishing a device basic database including equipment accounts, historical maintenance and repair data, a fault strength analysis data, operation monitoring data and the like; 2 utilizing basic data to conduct operation time analysis, fault strength analysis and alarm state and operation trend analysis on devices; 3 utilizing results to establish a device reliability level and fault strength incidence matrix, formulate DRBPM data analysis logic, automatically screen preventative maintenance devices and generate preventative maintenance planning reports of the devices; 4 examining, verifying and executing the preventative maintenance planning reports of the devices. The preventative device maintenance method based on the dynamic reliability can be used for dynamic analysis and management of states of petrochemical equipment, facilitate formulation of scientific and effective device maintenance measures and provide guarantee for timely device fault elimination and production continuity reliability and can be widely applied to the technical field of petrochemical equipment management.

The invention belongs to an electromagnetic compatibility fault diagnosis method and particularly relates to an interference source identification method in a system-grade electromagnetic compatibility fault diagnosis. The interference source identification method is characterized by comprising the following steps of: step 101, utilizing positioning sensitive equipment; step 102, carrying out fault tree analysis on the positioning sensitive equipment; step 103, performing the fault tree analysis so as to obtain a priority level of a coupling path; step 104, testing to obtain interference testing data; step 105, storing the testing data into a database; step 106, obtaining a potential interference source through the fault tree analysis of the step 102; step 107, searching and determining the priority level of the interference source; step 108, combining the step 105 and the step 107 to identify the interference source; step 109, judging whether the interference source is a determined interference signal or not; if not, carrying out step 110; otherwise, carrying out step 111; step 110, carrying out environment monitoring; and step 111, obtaining the determined interference signal and finally, finishing the compatibility fault diagnosis. The interference source identification method in an electromagnetic compatibility fault diagnosis system has the advantages of convenience for use, short period, low cost and controllable faults.

The disclosed fault analysis rule extraction device includes: an event preprocessing section and a fault analysis rule extraction section. The event preprocessing section merges, with respect to event groups each of which is grouped every certain time period, event groups that can be presumed to occur from the same fault by a cluster analysis. The fault analysis rule extraction section extracts a fault analysis rule useful for a fault analysis by relating event logs to clusters. By the fault analysis rule extraction device of the above configuration, highly accurate fault analysis rule are automatically provided from the event logs of a system.

A box-type substation intelligent online failure diagnosis system comprises an electric parameter collection module, a non-electric-parameter collection module, a switch value collection module, a data processing module, a micro-processor module, a failure diagnosis module, a high-voltage switch cabinet failure tree, a transformer room failure tree, a low-voltage switch box failure tree, a GPRS module, a monitoring center, a data output module and a warning module. The substation intelligent online failure diagnosis system has the advantages that operation state data of a box-type substation in real time can be collected and warnings are given out if abnormal-condition data occur so that workers can predict probable failures; failure symptom data can be timely and precisely collected so that online failure diagnosis can be carried out, the failures can be timely eliminated, and failure diagnosis efficiency is improved; a failure diagnosis method based on a failure tree analysis method is adopted, searching efficiency of failure diagnosis knowledge is improved, meanwhile readability of the failure diagnosis knowledge is also improved, and maintenance of failure diagnosis procedures is facilitated.

The invention discloses a preparation method of polypeptide synthesis carbetocin. The technical scheme of the method comprises the steps of taking Rink Amide MBHA (methylbenzene hydrogen amine) resin as an initial raw material; taking Fmoc-protected amino acid as a monomer; sequentially connecting amino acid, wherein Cys adopts Fmoc-Cys (CH2CH2CH2COOR)-OH, and the last amino acid uses Fmoc-Tyr (Me)-OH, and obtaining protected nonapeptide resin; removing the Fmoc azyl protected group of Tyr; cutting straight chain peptide from the resin by peptide cutting reagents such as FTA (fault tree analysis) and the like; precipitating a straight chain peptide rough product by adding diethyl ether; performing ring formation reaction by adding BOP (butyl octyl phthalate) /HOBt (oxhydryl benzotriazole)/DMF (dimethyl formamide); and purifying the preparation type HPLC (high performance liquid chromatography) in a separating way to obtain a target product. After the method is used, the each-step peptide connecting yield is more than 99%, the peptide cutting yield is 96.2%, and the gross yield is as high as more than 35%. The preparation method is convenient to industrial implementation, and has a higher industrial prospect.

The invention discloses a method for diagnosing the transmission chain fault of a wind generating set. The method comprises the steps that a wind generating set transmission chain fault information knowledge base of a fault tree structure is established; the knowledge base is searched according to fault feature information detected in real time, and a corresponding fault tree is located; reasoning is carried out according to preset data in the knowledge base and the bayesian network, the posterior probability P(Fn|S) of the fault trees Fn is obtained, and the probability distribution sequence of the fault trees is obtained; and retrieval is sequentially carried out according to the probability distribution sequence of the fault trees, all triggered bottom events under all the fault trees are obtained through judgment, and a fault reason diagnosis set is formed. The invention further provides a transmission chain fault diagnosing method applying the method for diagnosing the fault. The bayesian network technology is introduced on the basis of a fault tree analysis method, a powerful basis is provided for fast and effective diagnosis of the fan transmission chain fault reason, the shutdown time of the wind generating set is shortened, the manual maintenance cost is reduced, and the power generating rate and economic benefits are increased.

The invention relates to a dynamic fault tree analysis method for a system with a correlated failure mode, comprising the following steps of: 1, defining the correlated failure mode; 2, defining a correlated failure gate corresponding to the correlated failure mode; 3, solving the correlated failure gate through a Markov chain; 4, establishing a dynamic fault tree model of the system through the correlated failure gate; and 5, solving the dynamic fault tree model of the system to obtain the reliability of the system. The dynamic fault tree analysis method has the beneficial effects that the reliability of the correlated failure mode can be quantitatively calculated through analyzing and calculating the reliability of the correlated failure mode, so that the reliability of the system can be accurately obtained, and the maintenance and replacement costs of parts can be effectively reduced on the premise of ensuring the reliability of the system.

The invention discloses an extension device and a method from a logic tree to a physical tree in a malfunction analysis, which can realize the exact location to malfunction. The extension device comprises a logic tree management module used for arranging a malfunction detection point and establishing the logic tree which presents an equipment malfunction mode according to the malfunction detection point, a configuration management module used for establishing a configuration database according to the current configuration, a correlation analysis knowledge base which is connected with the logic tree management module and the configuration management module and used for achieving malfunction detection according to an analysis rule, and a physical tree extension module which is connected with the correlation analysis knowledge base and used for determining an edge method according to a node pair in the logic tree. Compared with the prior art, the device and the method of the invention can be smartly and self-adaptively extended from the logic tree to the physical tree, have high efficiency and exact positioning, and can be widely applied to mobile communication equipment malfunction analysis.

The invention relates to a numerical control machine tool system component reliability evaluation method based on cascading fault analysis. The method comprises the following steps that the entire numerical control machine tool system components are divided into multiple subsystems, and a fault transfer directed graph model is established according to the fault transfer relation between all the subsystems; the fault transfer directed graph model is described by using an adjacency matrix; a fault-correlation-based influence degree CK value of each subsystem is calculated; an inherent fault probability function of each subsystem is obtained according to the fault-correlation-based influence degree CK value of each subsystem and a comprehensive fault probability function through calculation; and reliability evaluation is performed on the numerical control machine tool system components by utilizing the inherent fault probability function. The cumulative fault process of the elements of the subsystems is considered by a reliability model, and fault correlation influence degree factors of other subsystem are also integrated so that the reliability model meets the reality further in comparison with the reliability model based on mutually independent assumption between systems.

The embodiment of the invention provides a risk assessment method and a risk assessment device, so as to solve technical problems that during a tunnel blasting project in the prior art, qualitative assessment is mainly carried out through an experienced person and an expert, the qualitative assessment is mainly subjective judgment and qualitative analysis, quantitative analysis is lacked, and thus, the stability and the accuracy of an assessment result are poor. According to the risk assessment method and the risk assessment device provided by the invention, after the identity of a user passes verification, basic events for project assessment inputted by the user are received, the risk values of the basic events are calculated quantitatively, and according to the risk values and the weight values of multiple basic events and a grahame assessment method and a fault tree analysis method, the risk value of the assessment project is acquired, and according to the risk value of the assessment project, the assessment result of the assessment project is acquired. Thus, the technical effects of quantitatively calculating the risk value of the assessment project and acquiring the risk assessment result according to the multiple basic events of the assessment project are achieved.

The invention discloses an avionics system fault diagnosis method based on combination of three methods which are FTA (fault tree analysis), a BAM (bidirectional associative memory) neural network and a BP (back-propagation) neural network. According to the invention, a fault diagnosis model integrating FTA and the BAM neural network and a fault diagnosis model combining the BP neural network are established respectively. When a fault phenomenon is obvious, the fault diagnosis model integrating FTA and the BAM neural network is adopted. Firstly, a fault mode of the system is acquired by using FTA, a training sample of BAM is analyzed and summarized, and finally, BAM carries out parallel association through an associative memory matrix so as to acquire a diagnosis result. When a fault phenomenon is not obvious or a fault example base does not have such a fault, the fault diagnosis model combining the BP neural network is adopted. Learning is carried out on a training sample, that is, data such as voltage, temperature and the like, acquired by the BP neural network, and fault diagnosis is carried out by using the trained network in the end. The two fault diagnosis models have complementary advantages, and make up deficiencies of each other, thereby effectively solving problems existing in avionics system fault diagnosis, and improving the accuracy and the efficiency of fault diagnosis.