WO2019209137A1 - Procédé et système pour diagnostiquer un site industriel - Google Patents

Procédé et système pour diagnostiquer un site industriel Download PDF

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Publication number
WO2019209137A1
WO2019209137A1 PCT/RU2019/000121 RU2019000121W WO2019209137A1 WO 2019209137 A1 WO2019209137 A1 WO 2019209137A1 RU 2019000121 W RU2019000121 W RU 2019000121W WO 2019209137 A1 WO2019209137 A1 WO 2019209137A1
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Prior art keywords
industrial facility
data
model
industrial
facility
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PCT/RU2019/000121
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English (en)
Russian (ru)
Inventor
Денис Бахчанович КАСИМОВ
Денис Олегович ЛИСИН
Аслан Русланович ГУРФОВ
Виктор Александрович МЕЛЬНИКОВ
Дмитрий Павлович МОЛЧАНОВ
Максим Юрьевич ВДОВЕНКО
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Общество С Ограниченной Ответственностью "Кловер Групп"
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Publication of WO2019209137A1 publication Critical patent/WO2019209137A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring

Definitions

  • the invention relates to the field of monitoring and diagnosing the state of an industrial facility, in particular, tracking the residual life and time to breakdown of elements of an industrial facility.
  • a known method for predicting the need for maintenance of equipment (patent US 2009/0037206).
  • the method described in the patent involves determining the time of failure of industrial equipment based on the assessment of the mutual influence of the parameters using mathematical, analytical or empirical models.
  • the disadvantages of the described method is the inability to predict the influence of the processes described by the sensor signals implicitly or rarely encountered during the observation period.
  • the fundamental difference between the proposed solution and that described in US 2009/0037206 is the use of finite element models to take into account the influence of factors not directly measured on industrial equipment itself.
  • a solution is known that describes adaptive remote maintenance of rolling stock (US8849732 B2, 09/30/2014), in which maintenance of rolling stock is provided by machine learning the rules.
  • Existing rules or models are automatically updated.
  • Machine learning is used to create a more efficient set of rules. Rules can be replaced, generalized or otherwise adapted based on the processing by the dispatchers of the results of existing rules. The acceptance or rejection of an event by the dispatcher is used as reference knowledge for machine learning controlled by the dispatcher of the new rule.
  • Machine learning uses controller feedback to update a set of rules. However, this decision, although it speaks of predicting future malfunctions, does not say anything about how it is implemented.
  • the known method for improved monitoring of the state of the system includes the use of many auto-associative neural networks to determine the estimates of actual values measured by at least one sensor in at least one of the many modes of operation; determining a difference between the estimated measured values and the actual values measured by at least one sensor; and combining the differences using a fuzzy controlled mixer model; fault diagnostics on combined differences; and determining a change in system performance by analyzing combined differences.
  • a warning is provided if necessary.
  • the smart sensor system includes an on-board processor for implementing the method according to the invention.
  • a system for diagnosing an industrial facility comprising:
  • a data collection unit configured to collect data from a set of sensors of an industrial facility
  • - block model of an industrial facility configured to simulate an industrial facility
  • - an analysis unit configured to analyze the state of the industrial facility based on data received from the data collection unit and the model of the industrial facility
  • the analysis unit is configured to make a conclusion about the normal or abnormal functioning of an industrial facility based on the analysis
  • the analysis unit is configured to receive data on changes made to the industrial facility, and to command the model unit to change the model in accordance with the changes.
  • data on changes made to the industrial facility contain data on interference in the operation of the industrial facility in the elimination of failures and precautionary conditions;
  • the analysis unit is configured to determine an element requiring repair or replacement leading to an abnormal operation of an industrial facility;
  • an analysis unit is configured to modify the model based on data on repair or replacement of an element;
  • the industrial model is a system of equations describing the dependence of the values of the determined parameter of the industrial object on the input data obtained from the industrial object at least by sensors, and the equation contains four groups of correction factors:
  • the analysis unit is configured to zero out all temporary correction factors for a given element of an industrial facility when it receives a message about replacing a given element of an industrial facility, while the resettable time coefficients are stored in memory, while the analysis unit is configured to obtain new values temporary correction coefficients from the replaced element, comparing them with the saved coefficients and deciding on the lack of repair this element of an industrial facility.
  • a method for diagnosing an industrial facility comprising the steps of collecting sensor data of an industrial facility using the hardware-software complex characterizing the physical parameters of the industrial facility; create a model of an industrial facility; analyze the state of the industrial facility on the basis of the collected data and the model of the industrial facility; moreover, when analyzing, they make a conclusion about the normal or abnormal functioning of an industrial facility; moreover, when analyzing, they accept data on changes made to the industrial facility, and change the model in accordance with the changes made.
  • data on changes made to the industrial facility contain data on interference in the operation of the industrial facility in the elimination of failures and precautionary conditions; determine the element requiring repair or replacement, leading to the abnormal functioning of the industrial facility; change the model based on data on repair or replacement of an element;
  • the industrial model is a system of equations describing the dependence of the values of the determined parameter of the industrial object on the input data obtained from the industrial object at least by sensors, and the equation contains four groups of correction factors:
  • the main tasks solved by the claimed invention are to determine the technical condition of an industrial facility in terms of failures, pre-failure states and operational modes violations according to the data of microprocessor-based systems for collecting data on the functioning of an industrial facility, determining the residual resource of elements of an industrial facility, automating the process of developing diagnostic algorithms for an industrial facility using models, as well as adaptation of the claimed method and system to the replacement of industrial elements
  • the object is.
  • the essence of the invention lies in the fact that using a set of sensors located in the elements of an industrial facility, the state of the industrial facility is monitored. Further, the data from the sensors are processed to diagnose the condition of the industrial facility. The data from the sensors are compared with the data generated by a pre-developed model of the industrial facility, the results of the comparison conclude serviceability of the industrial facility and its elements, the need for repair and / or replacement of elements of the industrial facility.
  • a feature of the claimed invention is the use of an engineering model of an industrial facility, the presence of feedback that changes the model of an industrial facility according to interference with the industrial facility when eliminating failures, precautionary states and when performing super-cycle work on scheduled types of services (latent failures).
  • the technical result achieved by the solution is to increase the accuracy of diagnostics of an industrial facility in terms of identifying pre-failure conditions, to more accurately diagnose an industrial facility by taking into account additional data on the facts of abnormal operation and replacement (repair) of elements of the industrial facility.
  • Figure 1 shows the functioning of the proposed method of diagnosis.
  • Figure 2 shows a method for obtaining the predicted parameter value of an industrial facility.
  • Fig. 3 shows a general diagram of the claimed diagnostic system.
  • a technical solution for servicing industrial facilities consists of a microprocessor-based data collection system on the operation of an industrial facility (hereinafter referred to as the ISU), a diagnostic system, and a resource management system (ERP system).
  • ISU microprocessor-based data collection system on the operation of an industrial facility
  • diagnostic system e.g., a diagnostic system
  • ERP system resource management system
  • ISU consists of a set of sensors installed on the most important elements of the analyzed industrial facility, input signal converters, output signal converters and signal processing facilities.
  • a key property of the signal processing means is the ability, in addition to generating control pulses, to accumulate in own memory information about the values of the input and output signals for a given period of time, as well as to transmit accumulated information through removable storage device or non-contact via GPRS or Wi-Fi channel to the diagnostic system.
  • the developed Clover PMM diagnostic system consists of a hardware-software complex that receives data from the local government and which, based on these data and engineering model data, diagnoses an industrial facility. Data can come both through wired transmission and wireless transmission, and data can also be loaded into the complex via a data storage device.
  • One of the features of the diagnostic system is the possibility of self-training on the basis of an expert assessment of the identified anomalies, that is, the system is trained by an expert who indicates whether the identified anomaly is a sign of malfunction of an industrial facility (incident) or not.
  • the diagnostic system is designed for automatic processing of MSU data and carries out data via GPRS, Wi-Fi, uploads it to the database, identifies anomalies using an engineering model, sends the identified anomalies for verification, as well as generates comments on identified incidents and sends them to the ERP system .
  • An ERP system can have a different structure depending on the specifics of an industrial facility, however, its key features from the point of view of the proposed technical solution are the ability to form work orders, record the fact that these orders are completed, and record information about the allocation of components (hereinafter - MPI) under execution of orders and transmit information about the work performed and decommissioned MPI to the diagnosis system.
  • - MPI allocation of components
  • ERP is a process management system.
  • An anomaly is a situation in which the values of the parameters of an industrial facility differ from normal for a given operating mode of an industrial facility.
  • Approximation - a description of the empirically observed pattern of parameter change by a mathematical function that describes the pattern with a given accuracy.
  • An incident is any case of abnormal operation of an industrial facility.
  • MPI - reusable materials - elements of an industrial facility that are dismantled during maintenance and repair and transferred to repair shops for further repairs.
  • Predictive analysis model - a model for predicting breakdowns of maintainable equipment, the operation of which can be adequately described by its sensors, as well as using calculation methods when constructing an engineering model.
  • MSU microprocessor control system - a set of hardware and software that manage an industrial facility and accumulate information about its work for a certain period of time.
  • NRE Violation of operating modes
  • the incident search rule is an automated incident search algorithm created by developers.
  • Pre-failure condition is a type of incident characterized by the transition of an industrial facility to a malfunctioning state while maintaining operability.
  • Hardware-software complex a device or system consisting of software and hardware, a particular example may be a microprocessor system, a server, a specialized integrated circuit, a general-purpose computer, etc.
  • Industrial facility an object related to the field of transport, engineering, production, private examples include a train, an airplane, a factory, a production line, a conveyor, a power plant, an industrial pump, etc.
  • Physical model - a digital model of an industrial facility, built on the basis of operational documentation and taking into account the nature of the physical processes that occur in the industrial facility during its operation.
  • An element of an industrial facility a component, block, part, assembly, unit, which is part of a facility that is more complex in design and functions, examples are a train or airplane engine, a separate line or conveyor machine, assembly unit, etc.
  • the task is solved, and the technical result is achieved by the fact that the diagnosis of an industrial facility (a flowchart of the method is shown in Fig. 1) is made by comparing (step b) the “predicted” (calculated) values of the industrial facility operation parameters obtained at the 4 during the formation of the engineering model (stage 2), with real values obtained in stage 5 from the data received by the LSG in stage 1, and identifying deviations that are beyond the statistically acceptable boundaries (step 8), and not going beyond these boundaries (step 7). Data sections in which parameter values went beyond the permissible limits are marked as “abnormal” (step 8) and sent to the expert for verification (confirmation) (step 9).
  • the expert’s assessment serves to train the model (step 10), which further recognizes similar cases as an anomaly or not an anomaly, according to the decision of the expert. If the expert confirms the existence of an anomaly, at step 12, a record about the malfunction of the industrial facility is created in the diagnostic card, then at step 13 it is sent to the ERP system, and the data ratio is entered as inadmissible in the statistical database of the predictive analysis model of the industrial facility during training models. If the expert does not recognize the presence of an anomaly, a piece of data is entered into the model as a normal mode of operation. From the ERP-system, information about the replaced equipment goes to the predictive analysis model to account for the replacement of elements of an industrial facility. Since neither the repaired nor the new element can have absolutely identical characteristics in comparison with the replaced element, the system may erroneously consider that anomalous changes have occurred in the operation of the industrial facility, therefore it is important to take into account the fact of changing the equipment of the industrial facility.
  • One of the beneficial effects of the implementation of the claimed technical solution is the creation of an information base for the transfer of devices having on-board microprocessor systems for servicing according to the current technical condition.
  • the predictive analysis model of an industrial facility (2) calculates the value of the desired output parameter based on input parameters (obtained from the LSG data (1)) as well as the dependencies of parameter values obtained analytically (when creating an engineering model of an industrial facility) and statistically (when data on work for a long period of time in a virtual neural network).
  • the data on the value of the parameters accumulated in the database contain a mark on whether the data section belongs to the normal or abnormal operating mode industrial object obtained in the verification process (9) by a technical expert. Additionally, the data contains marks:
  • the engineering model of the diagnosed elements of an industrial facility (Fig. 2) consists of models of three levels:
  • CAD model A three-dimensional model or a set of models built on the basis of design documentation and reproducing the geometric parameters of the diagnosed industrial facility.
  • CAE model An analytical model of an industrial facility, built on the basis of technological documentation and reproducing the physical properties of the materials of which the model is made.
  • the physical model of an industrial facility built on the basis of operational documentation and taking into account the nature of the physical processes that take place in an industrial facility during its operation.
  • the work parameter of an industrial facility is selected that most accurately describes the change in its objective function when the input parameters change.
  • the parameters obtained from the ISU are sent to the input of a CAE or physical model, changing the corresponding values of the parameters laid down earlier.
  • the value of the output parameter is calculated based on the simulation of processes occurring inside an industrial facility using the finite element method.
  • the final model of operation of an industrial facility is a system of equations describing the dependence of the values of the output parameter on the input and having 4 groups of correction factors:
  • Temporary adjustment factors Values that change for a given element of an industrial facility over time and depend on the fact repair element of an industrial facility. The influence of repair on the operation of this element of an industrial facility is determined.
  • the coefficient belongs to group (1) at the stage of model creation, and to groups (2 ... 4) - automatically in the process of statistical analysis of the model park work histories.
  • the determination of the values of the coefficients (2 - 4) is carried out with the accuracy required for this type of industrial plant element based on a statistical analysis of the available information on the operation of a known-good model (sections of the model’s working time not marked by the rules or operator as “anomalies” or “incidents”) with finding the most plausible solution. All found coefficients are initially determined as temporary correction factors, after which their belonging to groups (2) or (3) can be determined on the basis of the analysis of repeatability of values for different elements of an industrial facility before and after repair:
  • Adaptation of the model to the change of an element of an industrial facility during repair is carried out by zeroing all temporary correction factors for a given element of an industrial facility when a message is received about the replacement of an element of an industrial facility with the preservation of their previous values in the model history. New values of temporary correction factors are determined based on observation of the model. After obtaining statistically reliable values (both by the number of measurements and by the normality criterion), the new matrix of values of temporary correction factors is compared with the old one. If the new matrix coincides with the old with a given accuracy, a message is generated about the fictitious repair of an industrial facility.
  • an expert is trained to model the model, which occurs in two ways:
  • the search algorithm compares alternately the absolute, relative values of the input parameters (in the case of a supercharger, this is the motor current, rotor speed, inlet temperature and air pressure) as well as their rate of change, their dispersion and cross-correlation coefficients with values obtained in areas not marked as abnormal.
  • parameters that have an atypical value are marked as abnormal.
  • the influence of each parameter on the output parameter is estimated using the correlation coefficient (since the rate of change of parameters is estimated, the correlation coefficient will be calculated for the rate of change of pressure of the injected air).
  • the parameter that has the maximum deviation from typical values is recognized as the root cause of the anomaly (in this case, the stopping speed of the supercharger rotor (the rate of change of its rotational speed when the motor current is zero). Since the anomaly indicates the anomaly, the compressor’s bearing , the model automatically associates the exceeded rate of change of the rotational speed of the supercharger shaft when the motor current is zero with the precautionary state of the bearing.
  • the deviation of the actual value of the output parameter from the forecast for this section is marked as unacceptable, and if it is exceeded by the data in the future, such sections will be marked as an incident.
  • the assessment of the residual resource and the time before the breakdown of an element of an industrial facility is carried out by solving the problem of finding the most likely value of a parameter that best describes historical data for the period of observation using machine learning methods. If necessary, use to estimate the remaining resource and time to breakdown of the calculated (not measured at the industrial facility) parameter, the model is supplemented by a separate unit that solves the problem of determining the empirical regularity of changes in the calculated parameter at given points.
  • the estimation is made on the basis of the empirical distribution function of the failure frequency depending on the frequency of detected anomalies.
  • the assessment is made on the basis of an analysis of the discrepancy between the predicted and real values of the parameters characterizing the operation of the node by the trend analysis method. If there are several trends, the search for a working trend is carried out by the method of logistic regression.
  • the missing pieces of data are restored by modeling the operation of the diagnosed industrial facility with an engineering model. Based on the data obtained, analytical algorithms are optimized, after which the trends indicating degradation are identified in the reconstructed data set. Depending on the nature of the possible failure, the identified trends are approximated and superimposed on the empirical distribution function (with a gradual onset of failure) or the time it takes to reach the threshold level (with a discrete function of the onset of failure) is calculated.
  • the system consists of a software and hardware complex, in particular, a server 309 of the diagnostic system, equipment 301, consisting of a microprocessor system management (hereinafter - ISU) (2) and the transmitter 303, the diagnostics workstation 306 and the ERP system 314 of the owner of the industrial facility or service company.
  • a server 309 of the diagnostic system, equipment 301 consisting of a microprocessor system management (hereinafter - ISU) (2) and the transmitter 303, the diagnostics workstation 306 and the ERP system 314 of the owner of the industrial facility or service company.
  • - ISU microprocessor system management
  • ISU 302 of the diagnosed industrial facility consists of a specialized computer of industrial design, a system of sensors and signal converters and control devices (if any) and, in addition to managing the industrial facility, accumulates information about its operation.
  • the accumulated information is transmitted to the server 309 of the diagnostic system using the transmitter 303 through the channel 305 GPRS / Wi-Fi. If it is impossible to transfer data via GPRS / Wi-Fi, information is read using a portable data carrier 304 during the scheduled maintenance of the industrial facility and using the data download unit 307 it is uploaded to the diagnostics system server 309 through the diagnostics workstation 306, which is a standard computer with specialized software that has access to the Interner / Ethernet network through which data is transferred to the server 309 of the diagnostic system.
  • the server 309 of the diagnostic system by means of the data analysis unit 310, data are processed by the ISU 302 using the predictive analysis model 312, as a result of which the anomaly unit 311 detects anomalies that are sent for verification to the data verification unit 308 contained in the diagnostics workstation 306 and / or an expert. Both the anomalies confirmed and refuted during verification by the diagnostician and expert are transmitted to the server 309 of the diagnostic system for training the predictive analysis model 312. Anomalies confirmed during verification on the server 309 of the diagnostic system are marked in the block 313 of incidents as incidents and are sent to the ERP system 314 to generate comments through block 315 comments.
  • block 316 orders are formed orders for which from the warehouse are allocated reuse materials (hereinafter - MPI) 317.
  • - MPI allocated reuse materials
  • Modern locomotives are equipped with on-board microprocessor systems (MCU) designed to control the operation of power electric transmission and auxiliary equipment of the locomotive.
  • MCU microprocessor systems
  • an ISU of a locomotive consists of a control rack, a set of sensors, controls (relays and servos) and a display module (DM) for interacting with the driver.
  • DM display module
  • the design of most ISUs allows the accumulation and storage of information on the operation of locomotive equipment in the memory of the display module with the possibility of its further reading via portable flash drives or remote reading via wireless GPRS or Wi-Fi networks (depending on the version of the ISU) .
  • incidents are detected (events in the operation of the locomotive that indicate a malfunction). They are divided into violations of operating modes (NRE) and pre-failure conditions and are entered into the ERP-depot system for taking measures when locomotives call for the nearest service or repair.
  • NRE operating modes
  • AVS MSU automated diagnostician workstations
  • the diagnostic algorithms built into the automated workstation of the ISU used to have one significant drawback: for introducing new diagnostic algorithms into the automated workstation It was necessary to modify the automated workplace of the local self-government. As a result, with the accumulation of diagnostic experience and the emergence of new types of NRE and pre-failure conditions, the weight of incidents found using algorithms decreases and the problem of manually searching for incidents reappears. In the latest version of AWS MSU, this problem was partially solved by introducing a built-in language for writing diagnostic algorithms, but user algorithms existed only in the local version of the AWP and could not be extended to all locomotives of the series.
  • Incidents identified during data analysis are included in the locomotive comment list.
  • information exchange is organized between the systems, in which incidents are sent to the diesel locomotive monitoring system in an automated mode, and after the repair is completed, data on repaired and replaced equipment is imported from the diesel locomotive monitoring system to the hardware-software complex for adjustment equipment models (in fact, the model of repaired equipment is separated in order to exclude the influence of data before repair ont to her work).
  • the data should not contain time sections in which information on the operation of the equipment was not received.
  • TED Traction electric motors
  • Electromechanical model The dependence of the current TED on the speed of the wheelset.
  • the developers implemented a project to analyze the work and assess the technical condition of the TZFP-220-UZ turbogenerator.
  • a turbogenerator is the main equipment of power units of thermal power plants (TPPs) along with a power boiler, steam turbine and power transformer. For this reason, an emergency shutdown of a turbogenerator entails an emergency shutdown of the entire power unit, and the costs of restoring repair work.
  • TPPs thermal power plants
  • the main model for assessing the technical condition was determined by the model of the temperature of the stator winding of the turbogenerator from full power and the stator cooling system. To ensure the required accuracy of model training from the telemetry data set, sections with non-stationary modes of equipment operation (starts, shutdowns, sections of power change and transient actions) were removed and a training sample of data on normal operation of a turbogenerator for 1 month was determined.
  • the temperature sensors in the presented turbogenerator model are equipped only with grooves N ° 2, 4, 22, 24, 42, 44; for the control and localization of overheating zones in the intermediate stator grooves not equipped with temperature sensors, a heat map of active steel and stator windings with using CAD and CAE finite element methods. To clarify the areas of overheating, optimization algorithms were used.
  • a key feature of this application is the presence of feedback.
  • the model allows you to control the change in parameter values, thereby checking the fact of the work and evaluate their quality. After this, the model is retrained using the new parameter values to take into account the changed properties of the repaired (replaced) rods and to eliminate the erroneous acceptance of differences in the data for the anomaly.
  • Embodiments are not limited to the embodiments described herein, and other embodiments of the invention will be apparent to a person skilled in the art based on the information set forth in the description and knowledge of the prior art without departing from the spirit and scope of the present invention.
  • the functional connection of elements should be understood as a connection that ensures the correct interaction of these elements with each other and the implementation of one or another functionality of the elements.
  • Particular examples of functional communication can be communication with the possibility of exchanging information, communication with the possibility of transmitting electric current, communication with the possibility of transmitting mechanical motion, communication with the possibility of transmitting light, sound, electro-magnetic or mechanical vibrations, etc.
  • the specific type of functional connection is determined by the nature of the interaction of the mentioned elements, and, unless otherwise indicated, is provided by well-known means using principles well known in the art.
  • the methods disclosed herein comprise one or more steps or actions to achieve the described method. The steps and / or actions of the method can replace each other without going beyond the scope of the claims. In other words, unless a specific order of steps or actions is defined, the order and / or use of specific steps and / or actions can be changed without departing from the scope of the claims.
  • a computer-readable storage medium examples include read-only memory, random-access memory, register, cache semiconductor storage devices, magnetic media such as internal hard drives and removable drives, magneto-optical media and optical media such as CD-ROMs and digital versatile disks (DVDs), as well as any other Gia known in the prior art storage media.

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Abstract

L'invention concerne le domaine de monitorage et de diagnostic de l'état d'un objet industriel. Le résultat technique de la présente solution technique consiste à améliorer la précision de diagnostic d'un site industriel notamment en ce qui concerne la détection d'états précédant une panne, un diagnostic plus précis d'un site industriel grâce à la prise en ligne de compte de données supplémentaires sur les faites de fonctionnement anormal et de remplacent (réparations) d'éléments d'un site industriel. Ce résultat technique est obtenu grâce à la mise en oeuvre d'un système de diagnostic de site industriel comprenant un bloc de collecte de données; un bloc de modèle de site industriel; un bloc d'analyse, le bloc d'analyse étant réalisé de sorte à pouvoir accepter des données sur les modifications apportées au site industriel et commander au bloc modèle pour modifier un modèle selon les modifications apportées.
PCT/RU2019/000121 2018-04-28 2019-02-25 Procédé et système pour diagnostiquer un site industriel WO2019209137A1 (fr)

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RU2018116275A RU2707423C2 (ru) 2018-04-28 2018-04-28 Способ и система для диагностирования промышленного объекта
RU2018116275 2018-04-28

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