CN115034483A - Method and system for monitoring running fault of hydroelectric generating set - Google Patents
Method and system for monitoring running fault of hydroelectric generating set Download PDFInfo
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Abstract
The invention discloses a method and a system for monitoring operation faults of a water-turbine generator set, and belongs to the technical field of detection of the water-turbine generator set. The method provided by the invention integrates multiple dimensions such as intelligent sensing, data processing, fault modeling, intelligent analysis, intelligent early warning and comprehensive diagnosis, establishes an equipment operation condition identification model based on a large amount of equipment real-time data, and can set a more accurate threshold range aiming at characteristic parameters of equipment under different working conditions, so that the machine training efficiency and the fault identification accuracy are improved, the operation fault monitoring of the water-turbine generator set is realized, and the reliability of the operation fault diagnosis of the water-turbine generator set and the accuracy of state trend prediction are improved.
Description
Technical Field
The invention belongs to the technical field of detection of a hydroelectric generating set, and particularly relates to a method and a system for monitoring an operation fault of the hydroelectric generating set.
Background
The hydroelectric generating set is used as a complex nonlinear dynamic system, the operation process of the hydroelectric generating set is influenced by water, mechanical and electrical complex coupling factors, a vibration signal of the hydroelectric generating set usually has strong, non-stable and nonlinear characteristics, and the state analysis and fault diagnosis of the traditional hydroelectric generating set have certain theoretical and engineering technical difficulties in accurately representing the complex mapping relation between faults and symptoms and realizing the accurate evaluation of the state of the hydroelectric generating set and the accurate diagnosis of the faults.
Therefore, for the technical field, it is urgently needed to explore a new state analysis and fault diagnosis mode of the hydroelectric generating set so as to improve the reliability of fault diagnosis and the accuracy of state trend prediction, and provide reasonable guidance suggestions for subsequent maintenance decision and advanced predictive maintenance.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a method and a system for monitoring the running fault of a water-turbine generator set, which can accurately evaluate the state of the set and improve the reliability of fault diagnosis and the accuracy of state trend prediction.
The invention is realized by the following technical scheme:
a method for monitoring the operation fault of a hydroelectric generating set comprises the following steps:
s1: collecting the operation data of the water-turbine generator set and carrying out data processing;
s2: constructing a comprehensive diagnosis mechanism model for identifying and diagnosing a specific fault mode and a performance degradation analysis model for predicting the equipment key characteristic parameters and the equipment health state degradation by using the processed operation data obtained in the step S1, and constructing a big data model for early warning of the fault model by combining the comprehensive diagnosis mechanism model and the performance degradation analysis model;
s3: and (4) performing comprehensive diagnosis analysis on the water-turbine generator set by using the comprehensive diagnosis mechanism model, the performance degradation analysis model and the big data model which are constructed in the S2 in combination with remote online diagnosis analysis and case library analysis, outputting a state evaluation report of the water-turbine generator set, and performing early warning and fault warning on the operation fault of the water-turbine generator set.
Preferably, in S1, the data processing includes performing digital filtering, envelope modulation, order tracking, edge calculation, time sequence storage, and condition marking on the collected operation data; and S3, carrying out comprehensive diagnosis analysis on the hydroelectric generating set, wherein the comprehensive diagnosis analysis comprises waveform analysis, spectrum analysis, trajectory analysis, cycloid attitude analysis, trend analysis, correlation analysis, waterfall diagram analysis, cascade diagram analysis, Bode diagram analysis and continuous waveform analysis.
Preferably, the comprehensive diagnosis mechanism model is constructed by real-time process data, vibration on-line monitoring data, inspection point detection data and equipment maintenance data of the equipment.
Preferably, the performance degradation analysis model is constructed by extracting real-time process data of the equipment and online monitoring data of the vibration.
Preferably, the big data model is constructed by the device key parameters.
The invention discloses a running fault monitoring system of a water-turbine generator set, which comprises:
the intelligent sensing module is used for acquiring the operating data of the water turbine generator set;
the data processing module is used for processing the acquired operation data;
the fault modeling module is used for constructing a comprehensive diagnosis mechanism model for specific fault mode identification and diagnosis, a performance degradation analysis model for equipment key characteristic parameter and equipment health state degradation prediction and a big data model for fault model early warning;
the intelligent analysis module is used for providing various data analysis and assisting a diagnosis engineer to realize remote online diagnosis and analysis;
the intelligent early warning module is used for realizing early warning and fault warning of abnormal phenomena of the unit;
and the comprehensive diagnosis module is used for providing an instant fault self-diagnosis report and providing a state evaluation report of the water-turbine generator set by combining a remote expert interactive diagnosis and case library matching mode.
Preferably, the intelligent sensing module comprises sensors arranged at each measuring point, and the measuring points comprise: key phase, X and Y direction swing degrees of an oil receiver, X and Y direction swing degrees of a combined bearing, X and Y direction swing degrees of a water guide bearing, X direction horizontal vibration of a bulb head, Y direction vertical vibration of a bulb head, X and Z direction horizontal vibration of the oil receiver, Y direction vertical vibration of the oil receiver, X and Z direction horizontal vibration of the combined bearing, Y direction vertical vibration of the combined bearing, X and Z direction horizontal vibration of the water guide bearing, Y direction vertical vibration of the water guide bearing, X and Z direction horizontal vibration of an inner water distribution ring, Y direction vertical vibration of the inner water distribution ring, X direction horizontal vibration of a runner chamber, Y direction vertical vibration of the runner chamber, inlet pressure pulsation, pressure pulsation between the runner and a movable guide vane, pressure pulsation of an upper cavity of the runner, pressure pulsation of a tail water elbow pipe and inlet pressure pulsation of a tail water cone pipe.
Preferably, the intelligent early warning module comprises:
the characteristic parameter early warning module finds the degradation trend of the equipment performance by analyzing the residual errors of the predicted value and the actual value of the equipment operation parameter, and realizes the characteristic parameter early warning;
the working condition event early warning module realizes early warning by analyzing the associated state characteristic value when the working condition of the equipment is alarmed;
and the online monitoring and early warning module is used for analyzing the vibration online monitoring data and the vibration online monitoring data to realize early warning.
Preferably, the case base comprises fault cases which are diagnosed manually or automatically in daily production.
Preferably, the system further comprises an equipment health state evaluation module, and the equipment health state evaluation module comprises:
the health state evaluation submodule is used for comprehensively analyzing and evaluating the health state of the equipment from five dimensions of a communication state, an operation state, an energy efficiency state, an operation and maintenance state and a health state;
the health state historical analysis submodule is used for displaying and analyzing the historical trend curve of the health of the equipment;
and the health degradation trend analysis submodule evaluates and predicts the degradation trend of the whole health state according to the historical operation time of the equipment, the change trend of the energy efficiency level and the fault record data.
Compared with the prior art, the invention has the following beneficial technical effects:
the method for monitoring the running fault of the water-turbine generator set disclosed by the invention integrates multiple dimensions of intelligent sensing, data processing, fault modeling, intelligent analysis, intelligent early warning, comprehensive diagnosis and the like, establishes the equipment running condition recognition model based on a large amount of equipment real-time data, and can set more accurate threshold value ranges aiming at the characteristic parameters of the equipment under different working conditions, thereby improving the machine training efficiency and the fault recognition accuracy, realizing the running fault monitoring of the water-turbine generator set, and improving the reliability of the running fault diagnosis of the water-turbine generator set and the accuracy of state trend prediction. Based on fault modeling, the invention designs multi-measuring-point deployment, adds professional sensors such as vibration, throw, pressure pulsation and key phase, and acquires data signals representing key characteristics of the water-turbine generator set on line in real time, thereby improving the comprehensiveness of acquired data points, ensuring the accuracy of monitoring equipment operation faults, and being more reliable than the traditional single vibration analysis. The invention adopts a comprehensive diagnosis mechanism model, a performance degradation analysis model and a big data model to construct the fault diagnosis of the hydroelectric generating set, and realizes the identification diagnosis of a specific fault mode, the degradation analysis of key characteristic parameters of equipment and the early warning of the fault model. The invention adopts a comprehensive diagnosis mode, combines self-diagnosis, expert diagnosis and case base matching, solves the contradictions of few fault samples, lack of professional technicians and the like faced by the fault diagnosis of the conventional hydroelectric generating set, improves the accuracy of equipment diagnosis, simultaneously improves the visual map analysis technologies such as waveform analysis, frequency spectrum analysis, trajectory analysis, cycloid posture analysis, trend analysis, correlation analysis, waterfall graph analysis, cascade graph and bode graph analysis, continuous waveform analysis and the like, and assists a diagnosis engineer to realize remote online diagnosis and analysis. The invention constructs equipment health state evaluation according to the equipment mechanism mode, analyzes the current grade of each mechanism mode of the equipment in real time, supports the trend change of key parameters of the equipment, analyzes and positions the mechanism mode, quickly discovers problems, quickly positions the reasons and phenomena of faults and improves measures.
The running fault monitoring system of the water turbine generator set disclosed by the invention is simple in construction, high in automation degree, compatible with the existing system hardware and wide in application range.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the following figures and specific examples, which are intended to be illustrative, but not limiting, of the invention.
Like fig. 1, this embodiment provides a hydroelectric set operation fault monitoring system, and this monitoring system relies on industry internet platform, mainly includes two aspects content: (1) establishing an equipment mechanism model and a big data AI model around the hydroelectric generating set, and establishing a comprehensive diagnosis mechanism model by utilizing real-time process data, vibration on-line monitoring data, inspection data, equipment maintenance data and the like of equipment through a model construction and application system to realize the identification and diagnosis of a specific fault mode; extracting real-time process data and online oscillation monitoring data of the equipment, establishing a performance degradation analysis model based on AI big data, and realizing degradation prediction of key characteristic parameters and health state of the equipment; thirdly, establishing a big data model for key parameters of the equipment, fusing the big data model with a mechanism diagnosis model on the basis of parameter change trend prediction, and realizing the early warning function of a fault model; (2) and collecting data such as vibration, throw, temperature, current, voltage, power and the like of the water-turbine generator set, establishing an equipment mechanism model and a big data model, and realizing equipment fault self-diagnosis. And a professional signal analysis technology is adopted, and the expert remote diagnosis and the intelligent diagnosis are combined, so that the contradictions that the conventional fault diagnosis of the water turbine generator set is few in fault samples, lack of professional technicians and the like are solved.
In this embodiment, the function modules of the monitoring system for the running fault of the water turbine generator set are specifically as follows:
intelligent sensing module
The intelligent sensing module is used for acquiring the operation data of the water turbine generator set, for example: the intelligent sensing module adopts sensors to be arranged at measuring points of the water turbine generator set so as to collect data related to the measuring points, the embodiment carries out targeted design and optimization on the measuring points aiming at the operation characteristics of the water turbine generator set and the engineering problem existing in the prior art, and the arrangement of the measuring points and the matching of the sensors are shown in the following table:
the data acquisition of the motion/running state of each component of the water turbine generator set is realized through the arrangement of the measuring points, so that the accuracy of monitoring the running fault of the equipment is ensured, and the method is more reliable than the traditional single vibration analysis; aiming at data acquisition, technical measures of combining a plurality of anti-interference software and hardware such as shielding cables, differential input, photoelectric isolation, insulation treatment, power supply noise reduction, digital filtering and the like are preferably selected, and the interference of field static electricity, high frequency and strong magnetic fields of the water turbine generator set on data acquisition signals is reduced as far as possible.
Second, data processing module
The data processing module is mainly used for processing the collected running/motion data of the water turbine generator set, and the data processing mode comprises digital filtering, envelope adjustment, order ratio tracking, edge calculation, time sequence storage, working condition marking and the like. In a further preferred scheme, the monitoring system also provides a plurality of data quality evaluation algorithms such as a data dead point, a data effective range, a data fluctuation speed, a data fluctuation range, data correlation and the like, and the problem of false alarm of a fault diagnosis model caused by self faults of the sensor and network communication faults is reduced. In a further preferred scheme, the data processing module adopts a vibration analysis model based on a Digital Signal Processing (DSP) technology, adopts a latest digital signal processing technology-real-time calculation order ratio tracking technology, is used for collecting and analyzing vibration signals of the rotary machine and extracting vibration fault diagnosis signs, solves the problem of leakage in frequency spectrum analysis under the condition of variable speed and variable working conditions, and has clear frequency spectrum characteristics.
Third, fault modeling module
The fault modeling module is mainly used for establishing an equipment mechanism model and a big data model, and specifically comprises the following steps: (1) establishing a comprehensive diagnosis mechanism model by utilizing real-time process data, vibration on-line monitoring data, inspection data, equipment maintenance data and the like of the equipment to realize the identification and diagnosis of a specific fault mode; (2) extracting real-time technical process data of the equipment and online vibration monitoring data to establish a performance degradation analysis model based on AI big data, and realizing degradation prediction of key characteristic parameters and the health state of the equipment; (3) and establishing a big data model for the key parameters of the equipment, and fusing the big data model with a mechanism diagnosis model on the basis of parameter change trend prediction to realize the early warning function of the fault model.
Wherein, the general diagnosis mechanism model at least comprises the following contents corresponding to common fault types:
based on the model construction, the method has the advantages that: on one hand, the big data model can effectively utilize real-time process data, vibration on-line monitoring data, inspection data, equipment maintenance data and the like of the equipment to carry out comprehensive analysis, and analysis and diagnosis based on the AI data model are realized; on the other hand, a performance degradation analysis model based on AI big data can be established, and degradation prediction of key characteristic parameters and the health state of the equipment is realized. In the embodiment, the model construction is from data set construction, data set splitting, model training, model evaluation to model output and model release, all operations are based on a visual and configurable management platform, and the problems of AI modeling, training and optimization of the water-turbine generator set are solved.
Fourth, intelligent analysis module
The intelligent analysis module is used for providing various data analyses including waveform analysis, frequency spectrum analysis, trajectory analysis, cycloid attitude analysis, trend analysis, correlation analysis, waterfall graph analysis, cascade graph and bode graph analysis, continuous waveform analysis and the like, and a visual spectrum analysis technology is adopted to assist a diagnosis engineer in realizing remote online diagnosis and analysis.
Fifth, intelligent early warning module
The intelligent early warning module is used for realizing early warning prompt and fault warning of abnormal phenomena of the unit and pushing key event messages such as the state, the fault warning and the parameter warning of the unit in real time; in this embodiment, the intelligent early warning module includes a characteristic parameter early warning module, and finds the degradation trend of the equipment performance by analyzing the residual errors of the predicted value and the actual value of the equipment operation parameter, so as to realize characteristic parameter early warning; the working condition event early warning module realizes early warning by analyzing the associated state characteristic value when the working condition of the equipment is alarmed; and the online monitoring and early warning module is used for analyzing the vibration online monitoring data and the vibration online monitoring data to realize early warning.
On the basis, the intelligent early warning module further comprises an alarm threshold value for configuring the vibration amplitude according to the power of the equipment and the characteristics of the base, the vibration amplitude and monitoring parameter out-of-limit events in the operation of the equipment are defined and set, the system dynamically monitors the change trend of the parameters in real time, and once the early warning rule is triggered, the user is reminded in time through short messages, telephones, mails, mobile applications, sound alarms and other modes.
Sixth, comprehensive diagnosis module
The comprehensive diagnosis module is used for providing an instant fault self-diagnosis report and providing a state evaluation report of the water-turbine generator set by combining a remote expert interactive diagnosis and case base matching mode. Specifically, an equipment fault diagnosis result is utilized, an equipment diagnosis report is generated in real time by combining with expert advice, and information such as an equipment structure diagram, equipment key parameter values and equipment state evaluation is displayed. The comprehensive diagnosis module can realize the complete daily and monthly equipment diagnosis report, including the abnormal data intelligent self-diagnosis report identified by the mechanism model and the big data model and the remote diagnosis confirmation report of experts. The user can collect and share the diagnosis report and build an equipment knowledge case library of an enterprise. The comprehensive diagnosis module can realize the introduction of a picture, a parameter trend analysis chart and a vibration map which are verified on site into a diagnosis report, and also supports the derivation of the diagnosis report and the derivation of the map waveform. According to the diagnosis report, the equipment is conveniently and periodically checked, the service life of the equipment is prolonged, the production efficiency of the equipment is increased, and the equipment is prevented from generating serious uncontrollable faults. The diagnosis report is automatically generated, the previous complicated manual diagnosis, data arrangement and report making processes are avoided, and the diagnosis report is interconnected with the running state of the equipment in real time.
The case base is a fault case which is manually diagnosed or automatically diagnosed in daily production, is confirmed by field technicians and remote diagnosis experts, automatically generates an equipment fault case or manually adds the equipment fault case to the case base, and can provide functions of fault case retrieval, sharing, learning, training, fault diagnosis experience accumulation, fault analysis and verification and the like for equipment management experts, diagnosis experts and operation and maintenance personnel. Meanwhile, the equipment monitoring data corresponding to the fault case can be used for a manual intelligent machine learning system to carry out fault characteristic self-learning, so that the updating of the AI diagnosis model is realized, and the diagnosis accuracy of the AI model is improved.
Seventh, equipment health state evaluation module
The equipment health state evaluation module comprises: the health state evaluation submodule is used for comprehensively analyzing and evaluating the health state of the equipment from five dimensions of a communication state, an operation state, an energy efficiency state, an operation and maintenance state and a health state; the health state historical analysis submodule is used for displaying and analyzing the historical trend curve of the health of the equipment; and the health degradation trend analysis submodule evaluates and predicts the degradation trend of the whole health state according to the historical operation time of the equipment, the change trend of the energy efficiency level and the fault record data. The equipment state evaluation module evaluates five dimensions of the communication state, the energy efficiency state, the running state, the health state and the operation and maintenance state of each equipment in real time to represent the equipment state (including state grade and duration), combines the equipment structure diagram, displays the key parameter data of the equipment, combines the equipment attribute design parameters, and compares and analyzes the equipment running condition in real time. And establishing equipment state evaluation according to the equipment mechanism mode, analyzing the current grade of each type of mechanism mode of the equipment in real time, and analyzing and positioning the mechanism mode by combining the trend change of key parameters of the equipment.
The system combines a mechanism model and a big data model, calculates and evaluates the communication state, the energy efficiency state, the running state, the health state and the operation and maintenance state of the equipment in real time for 24 hours, and reminds a user in time through short messages, telephones, mails, mobile applications, sound alarms and other modes once a fault alarm is triggered. The user can analyze the trend change of the key parameters and analyze and position the mechanism mode when the failure of the equipment occurs at the PC end and the mobile end in real time. And by combining expert opinions provided by the system, the problems are quickly found, the causes and phenomena of faults are quickly positioned, and measures are improved.
In this embodiment, the server that monitoring system of hydroelectric set operation trouble adopted includes database server, application server, big data server. Data processing technologies such as a local cache strategy, variable working condition acquisition strategy scheduling, an event data marking storage strategy and a time dilution algorithm strategy are adopted to optimize network communication and data storage performance, on one hand, excessive computer resources occupied by massive vibration data in a stable state of the water-turbine generator set are reduced, on the other hand, high-density time sequence storage of the water-turbine generator set is ensured when the state changes, and the high-precision data requirement of fault diagnosis and analysis is met.
The monitoring system for the operation fault of the water-turbine generator set can provide a complete storage system with high availability and expansibility for different types of data, and supports the storage requirements of various structured and unstructured data through a column type storage database, a distributed file system, a memory database and the like. Meanwhile, at a data access layer, a platform needs to provide a uniform data access interface for different types of data.
The database adopts or integrates a high-performance domestic time sequence database with a large number of user performance, realizes the second-level permanent storage of process quantity and extracted vibration characteristic data, and meets the performance requirements of mass historical data of big data AI model training. The time sequence database is a big data storage component specially designed for storing data storage of the industrial Internet of things, and the technical indexes of the time sequence database are shown in the following table:
the monitoring system for the running fault of the hydroelectric generating set supports the functions of collecting, transmitting, storing, managing and the like of multi-source heterogeneous data, can fuse periodic off-line detection data of a patrol point inspection system, real-time data of monitoring system equipment and vibration on-line monitoring data, can perform fine modeling aiming at the running working condition of the equipment, and is more reliable compared with the traditional single vibration analysis technology.
It should be noted that the above description is only a part of the embodiments of the present invention, and equivalent changes made to the system described in the present invention are included in the protection scope of the present invention. Persons skilled in the art to which this invention pertains may substitute similar alternatives for the specific embodiments described, all without departing from the scope of the invention as defined by the claims.
Claims (10)
1. A method for monitoring the operation fault of a hydroelectric generating set is characterized by comprising the following steps:
s1: collecting the operating data of the water-turbine generator set and processing the data;
s2: constructing a comprehensive diagnosis mechanism model for specific fault mode identification and diagnosis and a performance degradation analysis model for equipment key characteristic parameter and equipment health state degradation prediction by using the processed operation data obtained in the step S1, and constructing a big data model for fault model early warning by combining the comprehensive diagnosis mechanism model and the performance degradation analysis model;
s3: and (4) performing comprehensive diagnosis analysis on the water-turbine generator set by using the comprehensive diagnosis mechanism model, the performance degradation analysis model and the big data model which are constructed in the S2 in combination with remote online diagnosis analysis and case library analysis, outputting a state evaluation report of the water-turbine generator set, and performing early warning and fault warning on the operation fault of the water-turbine generator set.
2. The hydroelectric generating set operation fault monitoring method according to claim 1, wherein in S1, the data processing comprises digital filtering, envelope adjustment, order tracking, edge calculation, time sequence storage and condition marking of the collected operation data; and S3, performing comprehensive diagnosis and analysis on the hydroelectric generating set, wherein the comprehensive diagnosis and analysis comprises waveform analysis, frequency spectrum analysis, trajectory analysis, cycloid attitude analysis, trend analysis, correlation analysis, waterfall diagram analysis, cascade diagram analysis, Bode diagram analysis and continuous waveform analysis.
3. The method for monitoring the operational faults of the hydroelectric generating set according to claim 1, wherein the comprehensive diagnosis mechanism model is constructed by real-time process data, vibration on-line monitoring data, inspection point detection data and equipment maintenance data of equipment.
4. The method for monitoring the operational failure of the hydroelectric generating set according to claim 1, wherein the performance degradation analysis model is constructed by extracting real-time process data of the equipment and online monitoring data of the oscillation.
5. The method for monitoring the operational fault of the hydroelectric generating set according to claim 1, wherein the big data model is constructed by the key parameters of the equipment.
6. The utility model provides a hydroelectric set operational failure monitoring system which characterized in that includes:
the intelligent sensing module is used for acquiring the operating data of the water-turbine generator set;
the data processing module is used for processing the acquired operation data;
the fault modeling module is used for constructing a comprehensive diagnosis mechanism model for specific fault mode identification and diagnosis, a performance degradation analysis model for equipment key characteristic parameter and equipment health state degradation prediction and a big data model for fault model early warning;
the intelligent analysis module is used for providing various data analysis and assisting a diagnosis engineer to realize remote online diagnosis and analysis;
the intelligent early warning module is used for realizing early warning and fault warning of abnormal phenomena of the unit;
and the comprehensive diagnosis module is used for providing an instant fault self-diagnosis report and providing a state evaluation report of the water-turbine generator set by combining a remote expert interactive diagnosis and case library matching mode.
7. The hydroelectric generating set operation fault monitoring system of claim 6, wherein the intelligent sensing module comprises sensors disposed at each measuring point, and the measuring points comprise: key phase, X and Y direction swing degrees of an oil receiver, X and Y direction swing degrees of a combined bearing, X and Y direction swing degrees of a water guide bearing, X direction horizontal vibration of a bulb head, Y direction vertical vibration of a bulb head, X and Z direction horizontal vibration of the oil receiver, Y direction vertical vibration of the oil receiver, X and Z direction horizontal vibration of the combined bearing, Y direction vertical vibration of the combined bearing, X and Z direction horizontal vibration of the water guide bearing, Y direction vertical vibration of the water guide bearing, X and Z direction horizontal vibration of an inner water distribution ring, Y direction vertical vibration of the inner water distribution ring, X direction horizontal vibration of a runner chamber, Y direction vertical vibration of the runner chamber, inlet pressure pulsation, pressure pulsation between the runner and a movable guide vane, pressure pulsation of an upper cavity of the runner, pressure pulsation of a tail water elbow pipe and inlet pressure pulsation of a tail water cone pipe.
8. The hydroelectric generating set operational failure monitoring system of claim 6, wherein the intelligent early warning module comprises:
the characteristic parameter early warning module finds the degradation trend of the equipment performance by analyzing the residual errors of the predicted value and the actual value of the equipment operation parameter, and realizes the characteristic parameter early warning;
the working condition event early warning module realizes early warning by analyzing the associated state characteristic value when the working condition of the equipment is alarmed;
and the online monitoring and early warning module is used for analyzing the vibration online monitoring data and the vibration online monitoring data to realize early warning.
9. The hydroelectric generating set operation fault monitoring system of claim 6, wherein the case base comprises fault cases which are manually diagnosed or automatically diagnosed in daily production.
10. The hydroelectric generating set operation fault monitoring system of claim 6, further comprising an equipment health state evaluation module, wherein the equipment health state evaluation module comprises:
the health state evaluation submodule comprehensively analyzes and evaluates the health state of the equipment from five dimensions of communication state, running state, energy efficiency state, operation and maintenance state and health state;
the health state historical analysis submodule is used for displaying and analyzing the historical trend curve of the health of the equipment;
and the health degradation trend analysis submodule evaluates and predicts the degradation trend of the whole health state according to the historical operation duration of the equipment, the change trend of the energy efficiency level and the fault recording data.
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