CN109470946A - A kind of generating equipment fault detection method and system - Google Patents
A kind of generating equipment fault detection method and system Download PDFInfo
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- CN109470946A CN109470946A CN201811088218.1A CN201811088218A CN109470946A CN 109470946 A CN109470946 A CN 109470946A CN 201811088218 A CN201811088218 A CN 201811088218A CN 109470946 A CN109470946 A CN 109470946A
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
A kind of generating equipment fault detection method and system, comprising: the current generated output of generating equipment is brought into the generating equipment fault detection model of fit pre-established, obtains current voltage match value；Determine whether the generating equipment currently breaks down based on the current voltage match value, voltage monitoring value；Predicted time is brought into the generating equipment operation situation prediction model pre-established, obtains generating equipment in the voltage prediction value of the predicted time；Determine whether the generating equipment predicted time breaks down based on voltage prediction value, by real-time detection generating equipment with the presence or absence of the operation situation after failure and certain time, the fitting data and prediction data of the relevant crucial characteristic feature of generating equipment are provided, make testing result that more there is convincingness, improves safety of the generating equipment in normal course of operation.
Description
Technical field:
The invention belongs to generating equipment field of fault detection, and in particular to a kind of generating equipment fault detection method and be
System.
Background technique:
Since the industrial revolution, the functional requirement of the detection of equipment fault just comes into being, it is however generally that, workpeople's judgement
Whether device fails, mainly pass through the experience of long-time accumulation or the knot obtained for the observation of equipment list elephant
By.Along with advancing by leaps and bounds for industrial development, industrial equipment is also increasingly developed, Xiang Zhineng, large size, high speed, distributed direction
Development.Device structure is more complicated compared to tradition, and function is also further powerful at the same time, and then increases the difficulty of maintenance.
Then, compared with greatly improving in the past, the information data of equipment fault is presented exponential type and increases the probability of device fails, is produced
Raw mass data, manually can not load, be unable to complete correct, effective accident analysis.
With the rapid development of information technology, especially the extensive of the various information systems such as electricity power enterprise's control system is built
If the data volume for promoting power plant to accumulate rapidly increases, user can be helped by the equipment state on-line monitoring system of a new generation
It realizes the intelligent management of equipment state, and then gives full play to the professional efficiency of apparatus manager, it is real-time that accident, which hinders post-processing,
The running overall dynamics variation of equipment is grasped, in the life cycle of every equipment, greatlys improve the operational safety of equipment
Horizontal and efficiency reduces the non-programmed halt due to caused by Equipment and accident, creates more benefits for electricity power enterprise.
Currently, the fault detection method for generating equipment is the detection method based on threshold value.Specific method is, for hair
Each key characterization monitoring node of electric equipment, by manually rule of thumb it being arranged alarm, early warning, failure bound threshold
Value, Real-time Monitoring Data is compared with threshold value to confirm whether the working condition of current device is normal；This method will be sent out
Each key feature of electric equipment separates, and can not comprehensively consider the operation conditions of equipment, meanwhile, when the key feature of equipment
When quantity is more, it is unfavorable for carrying out the analysis of equipment entirety.
Summary of the invention:
In order to overcome drawbacks described above, the present invention provides a kind of generating equipment fault detection methods, which comprises
The current generated output of generating equipment is brought into the generating equipment fault detection model of fit pre-established, is worked as
Preceding voltage match value；
Determine whether the generating equipment currently breaks down based on the current voltage match value, voltage monitoring value；
Predicted time is brought into the generating equipment operation situation prediction model pre-established, obtains generating equipment described pre-
Survey the voltage prediction value of time；
Determine whether the generating equipment predicted time breaks down based on voltage prediction value.
Preferably, the foundation of the generating equipment fault detection model of fit, comprising:
Historical current and generated output based on generating equipment obtain corresponding voltage；
Training set and test set are divided into based on the generating equipment historical temperature, voltage and generated output data；
Based on training set, the historical temperature, voltage and power generation are determined using support vector machine regression model of fit
The higher dimensional space distance relation of power；
It is tested based on the test set, adjusts the higher dimensional space distance of the historical temperature, voltage and generated output
Relation Parameters obtain generating equipment fault detection model of fit.
Preferably, the foundation of the generating equipment operation situation prediction model, comprising:
Based on training set, the historical temperature, voltage and power generation are determined using support vector machine regression prediction model
The higher dimensional space distance relation of power；
It is tested based on the test set, adjusts the higher dimensional space distance of the historical temperature, voltage and generated output
Relation Parameters obtain generating equipment operation situation prediction model.
Preferably, described to be divided into training set and test based on generating equipment historical temperature, voltage and generated output data
Collection includes:
Obtain historical temperature, electric current and the generated output monitoring data sample of each monitoring point of generating equipment；
DUAL PROBLEMS OF VECTOR MAPPING is carried out based on the monitoring data sample, obtains time series vector set corresponding with each monitoring point；
The time series vector set is divided according to time series；
The ready-portioned time series vector set is proportionally divided into training set and test set.
Preferably, it is described based on the current voltage match value and voltage monitoring value determine the generating equipment currently whether
It breaks down, comprising:
Current voltage match value and current voltage monitor value are compared, if the current voltage match value is worked as with described
The difference of preceding voltage monitoring value exceeds preset range, then the current operation troubles of generating equipment.
It is preferably, described to determine whether the generating equipment predicted time breaks down based on voltage prediction value, comprising:
The voltage prediction value is compared with preset normal voltage range, if the voltage prediction value is not normal
In voltage range, then the generating equipment operation troubles in predicted time.
Preferably, described to be based on the current voltage match value, current voltage monitor value, voltage prediction value and normal operation
Voltage under state determines that the generating equipment is current and the operation troubles of predicted time, later further include:
After determining generating equipment operation troubles, generating equipment is carried out based on current voltage match value and voltage prediction value
Warning.
A kind of generating equipment fault detection system, the system comprises:
First brings module into: for the current generated output of generating equipment to be brought into the generating equipment failure pre-established inspection
Model of fit is surveyed, current voltage match value is obtained；
First determining module: for determining that the generating equipment is worked as based on the current voltage match value, voltage monitoring value
It is preceding whether to break down；
Second brings module into: for predicted time to be brought into the generating equipment operation situation prediction model pre-established, obtaining
To generating equipment the predicted time voltage prediction value；
Second determining module: for determining whether the generating equipment predicted time breaks down based on voltage prediction value.
Preferably, described first module is brought into further include: generating equipment fault detection model of fit module；
Historical current and generated output based on generating equipment obtain corresponding voltage；
Training set and test set are divided into based on the generating equipment historical temperature, voltage and generated output data；
Based on training set, the historical temperature, voltage and power generation are determined using support vector machine regression model of fit
The higher dimensional space distance relation of power；
It is tested based on the test set, adjusts the higher dimensional space distance of the historical temperature, voltage and generated output
Relation Parameters obtain generating equipment fault detection model of fit.
Preferably, described second module is brought into, further includes: establish generating equipment operation situation prediction model module；
Based on training set, the historical temperature, voltage and power generation are determined using support vector machine regression prediction model
The higher dimensional space distance relation of power；
It is tested based on the test set, adjusts the higher dimensional space distance of the historical temperature, voltage and generated output
Relation Parameters obtain generating equipment operation situation prediction model.
Compared with prior art, the invention has the following beneficial effects:
1, a kind of generating equipment fault detection method provided by the invention and system, by the current generated output of generating equipment
It brings the generating equipment fault detection model of fit pre-established into, obtains current voltage match value；It is quasi- based on the current voltage
Conjunction value, voltage monitoring value determine whether the generating equipment currently breaks down；Predicted time is brought into the power generation pre-established
Equipment operation situation prediction model obtains generating equipment in the voltage prediction value of the predicted time；It is true based on voltage prediction value
Whether the fixed generating equipment predicted time breaks down, by each key feature comprehensive analysis of generating equipment, more fully,
Preferably solves the fault detection problem of equipment.
2, a kind of generating equipment fault detection method provided by the invention and system, by real-time detection generating equipment whether
There are the operation situation after failure and certain time, the fitting data of the relevant crucial characteristic feature of generating equipment and pre- is provided
Measured data makes testing result more have convincingness, improves safety of the generating equipment in normal course of operation.
Detailed description of the invention:
Fig. 1 is the generating equipment fault detection flow chart of the invention based on support vector machines；
Fig. 2 is the flow chart that generating equipment fault detection of the invention/prediction model is established.
Specific embodiment:
For a better understanding of the present invention, following will be combined with the drawings in the embodiments of the present invention, in the embodiment of the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.
Embodiment 1
As shown in Figure 1, the specific steps are as follows:
Step 1: the current generated output of generating equipment is brought to the generating equipment fault detection fitting mould pre-established into
Type obtains current voltage match value；
Step 2: determine whether the generating equipment currently occurs based on the current voltage match value, voltage monitoring value
Failure；
Step 3: predicted time is brought into the generating equipment operation situation prediction model pre-established, obtains generating equipment
In the voltage prediction value of the predicted time；
Step 4: determine whether the generating equipment predicted time breaks down based on voltage prediction value.
The present invention provides a kind of fault detection method of generating equipment, realize to generating equipment in the process of running whether
The detection function to break down, detection generating equipment whether normal operation, and provide the operation shape of generating equipment after a certain period of time
State improves the safety of generating equipment working environment.
In order to realize accurately detection generating equipment whether break down or after a certain time whether normal operation this
Problem, the present invention provides a kind of generating equipment fault detection method based on support vector machines.Below to of the invention specific
Embodiment is described in further detail.
Step 1: the current generated output of generating equipment is brought to the generating equipment fault detection fitting mould pre-established into
Type obtains current voltage match value；
S1. generating equipment Real-time Monitoring Data extracts: batch obtains the real-time prison of generating equipment under normal operating conditions
Measured data；
S2. the pretreatment of equipment feature and vectorization: mapping to vector space after the Real-time Monitoring Data of equipment is pre-processed,
Obtain time series vector set corresponding with each key measuring point of equipment；
S3. generating equipment Fault Model is established: training simultaneously establishes setting based on support vector machine regression method
Standby Fault Model；
S1. the specific steps of Real-time Monitoring Data acquisition include:
S11. the analog quantity and quantity of state of the relevant key feature node of generating equipment are obtained from Real-time Monitoring Data library
Real-time Monitoring Data, and form characteristic set；
S12. the Real-time Monitoring Data of the relevant crucial characterization analog quantity of generating equipment is obtained from Real-time Monitoring Data library,
And form tag characterization data acquisition system.
S2. the pretreatment of equipment feature and the specific steps of vectorization include:
S21. after Real-time Monitoring Data is acquired, pretreatment operation, including cleaning, interpolation, noise reduction, pass are carried out to it
The related pretreatment operation such as connection and standardization, it is ensured that have just in any each data acquisition monitoring point of data obtaining time point
Regular data；
S22. equipment Real-time Monitoring Data is mapped into vector space, according to the standardization result of Real-time Monitoring Data value,
The numerical value that vector corresponds to dimension is set, for the measuring point of same equipment, obtains time series vector sequence.
S3. as shown in Fig. 2, the specific steps that generating equipment Fault Model is established include:
S31. Real-time Monitoring Data is divided into multiple groups training data, the time span phase of every group of data according to time series
Together, training characteristics collection is characterized data, and characterize data integrates the characterize data being aligned as time shaft with characteristic；
S32. it is directed to each group of Real-time Monitoring Data, preferably, selecting support vector machine regression model as machine
Learning method establishes regression analysis model of fit.
Step s32 further comprises:
S321. preferably, the regression analysis model of selection support vector machines is trained；
S322. generating equipment Fault Model parameter initialization；
S323. using training set training generating equipment Fault Model, the model of fit of regression analysis is obtained；
S324. the effect of detection model, and record cast result are assessed using test set；
S325. model parameter is updated according to assessment result, step s323-s324 is repeated, until meeting preset condition.
Step 2: determine whether the generating equipment currently occurs based on the current voltage match value, voltage monitoring value
Failure；
The process of involved model training is as shown in Figure 2 in s3.
For specific generating equipment, equipment Real-time Monitoring Data is obtained, and maps data into vector space；
It is carried out using feature vector of the fault detection and Tendency Prediction model that training obtains in s3 step to current time
It calculates, the characterization match value for obtaining current time equipment and the predicted value after certain time；
Confirm whether equipment breaks down according to situations such as characterize data match value and the difference of actual monitoring value.
Step 3: predicted time is brought into the generating equipment operation situation prediction model pre-established, obtains generating equipment
In the voltage prediction value of the predicted time；
S4. generating equipment operation situation prediction model training: training is simultaneously established based on support vector machine regression method
Equipment operation situation prediction model；
S4. the specific steps of generating equipment operation situation prediction model foundation include:
S41. Real-time Monitoring Data is divided into multiple groups training data, the time span phase of every group of data according to time series
Together, training characteristics collection is characterized data, and characterize data integrates the characterization number for delaying certain time compared with characteristic as time shaft
According to；
S42. it is directed to each group of Real-time Monitoring Data, preferably, selecting support vector machine regression model as machine
Learning method establishes Regression Model.
Step s42 further comprises:
S421. preferably, the regression analysis model of selection support vector machines is trained；
S422. generating equipment operation situation prediction model parameters initialize；
S423. using training set training generating equipment operation situation prediction model, the prediction model of regression analysis is obtained；
S424. the prediction effect of detection model, and record cast result are assessed using test set；
S425. model parameter is updated according to assessment result, step s323-s324 is repeated, until meeting preset condition.
Step 4: determine whether the generating equipment predicted time breaks down based on voltage prediction value.
The process of involved model training is as shown in Figure 2 in s4.
For specific generating equipment, equipment Real-time Monitoring Data is obtained, and maps data into vector space；
The fault detection that training obtains in s4 step is calculated with feature vector of the Tendency Prediction model to current time,
The characterization match value for obtaining current time equipment and the predicted value after certain time；
Confirm whether equipment breaks down according to situations such as characterize data match value and the difference of actual monitoring value.
Embodiment 2
It is invented based on same design, the present invention also provides a kind of generating equipment fault detection system, the system comprises:
First brings module into: for the current generated output of generating equipment to be brought into the generating equipment failure pre-established inspection
Model of fit is surveyed, current voltage match value is obtained；
First determining module: for determining that the generating equipment is worked as based on the current voltage match value, voltage monitoring value
It is preceding whether to break down；
Second brings module into: for predicted time to be brought into the generating equipment operation situation prediction model pre-established, obtaining
To generating equipment the predicted time voltage prediction value；
Second determining module: for determining whether the generating equipment predicted time breaks down based on voltage prediction value.
Described first brings module into further include: generating equipment fault detection model of fit module；
Historical current and generated output based on generating equipment obtain corresponding voltage；
Training set and test set are divided into based on the generating equipment historical temperature, voltage and generated output data；
Based on training set, the historical temperature, voltage and power generation are determined using support vector machine regression model of fit
The higher dimensional space distance relation of power；
It is tested based on the test set, adjusts the higher dimensional space distance of the historical temperature, voltage and generated output
Relation Parameters obtain generating equipment fault detection model of fit.
Described second obtains module, further includes: establishes generating equipment operation situation prediction model module；
Based on training set, the historical temperature, voltage and power generation are determined using support vector machine regression prediction model
The higher dimensional space distance relation of power；
It is tested based on the test set, adjusts the higher dimensional space distance of the historical temperature, voltage and generated output
Relation Parameters obtain generating equipment operation situation prediction model.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is flow chart and side of the reference according to the method for the embodiment of the present application, system and computer program product
Block diagram describes.It should be understood that each process and box that can be realized by computer program instructions in flow chart and block diagram, with
And the combination of the process and box in flow chart and block diagram.Can provide these computer program instructions to general purpose computer, specially
With the processor of computer, Embedded Processor or other programmable data processing devices to generate a machine, so that passing through
The instruction that computer or the processor of other programmable data processing devices execute generates for realizing in one process of flow chart
Or the device for the function of being specified in multiple processes and one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
The manufacture of device is enabled, which realizes in one box of one or more flows of the flowchart and block diagram or multiple
The function of being specified in box.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and one, block diagram
The step of function of being specified in box or multiple boxes.
The above is only the embodiment of the present invention, are not intended to restrict the invention, all in the spirit and principles in the present invention
Within, any modification, equivalent substitution, improvement and etc. done, be all contained in apply pending scope of the presently claimed invention it
It is interior.
Claims (10)
1. a kind of generating equipment fault detection method, which is characterized in that the described method includes:
The current generated output of generating equipment is brought into the generating equipment fault detection model of fit pre-established, obtains current electricity
Press match value；Determine whether the generating equipment currently breaks down based on the current voltage match value, voltage monitoring value；
Predicted time is brought into the generating equipment operation situation prediction model pre-established, obtains generating equipment in the prediction
Between voltage prediction value；Determine whether the generating equipment predicted time breaks down based on voltage prediction value.
2. generating equipment fault detection method as described in claim 1, which is characterized in that the generating equipment fault detection is quasi-
The foundation of molding type, comprising:
Historical current and generated output based on generating equipment obtain corresponding voltage；
Training set and test set are divided into based on the generating equipment historical temperature, voltage and generated output data；
Based on training set, the historical temperature, voltage and generated output are determined using support vector machine regression model of fit
Higher dimensional space distance relation；
It is tested based on the test set, adjusts the higher dimensional space distance relation of the historical temperature, voltage and generated output
Parameter obtains generating equipment fault detection model of fit.
3. generating equipment fault detection method as claimed in claim 2, which is characterized in that the generating equipment operation situation is pre-
Survey the foundation of model, comprising:
Based on training set, the historical temperature, voltage and generated output are determined using support vector machine regression prediction model
Higher dimensional space distance relation；
It is tested based on the test set, adjusts the higher dimensional space distance relation of the historical temperature, voltage and generated output
Parameter obtains generating equipment operation situation prediction model.
4. generating equipment fault detection method as claimed in claim 2, which is characterized in that described to be based on generating equipment history temperature
Degree, voltage and generated output data are divided into training set and test set includes:
Obtain historical temperature, electric current and the generated output monitoring data sample of each monitoring point of generating equipment；
DUAL PROBLEMS OF VECTOR MAPPING is carried out based on the monitoring data sample, obtains time series vector set corresponding with each monitoring point；
The time series vector set is divided according to time series；
The ready-portioned time series vector set is proportionally divided into training set and test set.
5. generating equipment fault detection method as described in claim 1, which is characterized in that described quasi- based on the current voltage
Conjunction value and voltage monitoring value determine whether the generating equipment currently breaks down, comprising:
Current voltage match value and current voltage monitor value are compared, if the current voltage match value and the current electricity
The difference of monitor value is pressed to exceed preset range, then the current operation troubles of generating equipment.
6. generating equipment fault detection method as described in claim 1, which is characterized in that described to be determined based on voltage prediction value
Whether the generating equipment predicted time breaks down, comprising:
The voltage prediction value is compared with preset normal voltage range, if the voltage prediction value is not in normal voltage
In range, then the generating equipment operation troubles in predicted time.
7. generating equipment fault detection method as claimed in claim 6, which is characterized in that described quasi- based on the current voltage
Voltage under conjunction value, current voltage monitor value, voltage prediction value and normal operating condition determines that the generating equipment is current and pre-
The operation troubles of time is surveyed, later further include:
After determining generating equipment operation troubles, generating equipment is warned based on current voltage match value and voltage prediction value
It accuses.
8. a kind of generating equipment fault detection system, which is characterized in that the system comprises:
First brings module into: intending for bringing the current generated output of generating equipment into pre-establish generating equipment fault detection
Molding type obtains current voltage match value；
First determining module: it is for determining the generating equipment currently based on the current voltage match value, voltage monitoring value
It is no to break down；
Second brings module into: for predicted time to be brought into the generating equipment operation situation prediction model pre-established, being sent out
Voltage prediction value of the electric equipment in the predicted time；
Second determining module: for determining whether the generating equipment predicted time breaks down based on voltage prediction value.
9. generating equipment fault detection system as claimed in claim 8, which is characterized in that described first, which brings module into, also wraps
It includes: generating equipment fault detection model of fit module；
Historical current and generated output based on generating equipment obtain corresponding voltage；
Training set and test set are divided into based on the generating equipment historical temperature, voltage and generated output data；
Based on training set, the historical temperature, voltage and generated output are determined using support vector machine regression model of fit
Higher dimensional space distance relation；
It is tested based on the test set, adjusts the higher dimensional space distance relation of the historical temperature, voltage and generated output
Parameter obtains generating equipment fault detection model of fit.
10. generating equipment fault detection system as claimed in claim 9, which is characterized in that described second brings module into, also wraps
It includes: establishing generating equipment operation situation prediction model module；
Based on training set, the historical temperature, voltage and generated output are determined using support vector machine regression prediction model
Higher dimensional space distance relation；
It is tested based on the test set, adjusts the higher dimensional space distance relation of the historical temperature, voltage and generated output
Parameter obtains generating equipment operation situation prediction model.
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