CN113806969A - Compressor unit health prediction method based on time domain data correlation modeling - Google Patents
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Abstract
The invention discloses a compressor set health prediction method based on time domain data correlation modeling, which relates to the technical field of pipe network operation component detection and aims to solve the problem that early warning is difficult to provide at the early stage of equipment failure in a middle and after-accident analysis mode, and the technical scheme is characterized by comprising the following steps of: establishing a model, namely establishing a state data perception model and establishing a fault mechanism association model; collecting normal parameters, obtaining a normal working condition state health degree reference value through a state data perception model, and recording the change rule of the fault under different fault conditions through a fault mechanism association model; comparing and analyzing data extraction, namely analyzing a state data perception model and a fault mechanism association model for multiple times to obtain an average value; and (5) data abnormity warning. The effects of prediction diagnosis, early warning and timely reminding of the equipment faults of the compressor unit are achieved.
Description
Technical Field
The invention relates to the technical field of pipe network operation component detection, in particular to a compressor set health prediction method based on time domain data correlation modeling.
Background
The safe and normal operation of the compressor unit equipment relates to the normal and stable operation of the pipe network transmission, the early stage of the compressor unit equipment failure is relatively unobvious in failure characteristics and has little influence on the normal operation of the pipe network, the change of the early stage of the equipment failure is difficult to monitor by the prior art means, so the change is often ignored, when the failure is developed to a more serious stage and is monitored, the damage of the equipment is often already generated, except for needing to maintain the equipment and consuming a large amount of manpower and material resources, the equipment can stop working at any time to influence the stable operation of the pipe network, therefore, before the equipment failure occurs, the potential failure of the equipment is found and early warning is carried out in advance, so that the timely and proper manual operation intervention is carried out in advance, the expansion of the failure and the occurrence of accidents are prevented, and the safe production of the equipment and the pipe network is ensured, has great practical significance.
At present, the research on the equipment fault diagnosis technology of the domestic compressor group mainly adopts a method for researching a fault mechanism, when the fault characteristics are obvious, the fault is often developed to a certain stage, and a data model is introduced into equipment state monitoring at home and abroad to quantitatively perceive the health state of equipment, identify early symptoms of the fault and provide early warning, wherein the mode driven by the data model obtains a lot of results in the fields of equipment health management and early warning.
The above prior art solutions have the following drawbacks: the mode of trouble thing and after analysis, hardly realize carrying out whole and quantitative health assessment to compressor unit equipment running state, also hardly provide early warning at the equipment initial stage that breaks down, the mode of data model drive also has the early warning not qualitative well, and the early warning wrong report is many, the shortcoming that the practicality is not strong.
Disclosure of Invention
The invention aims to provide a compressor unit health prediction method based on time domain data correlation modeling, which greatly improves the technical level of fault diagnosis of the existing compressor unit and early gives out early warning of faults.
In order to achieve the purpose, the invention provides the following technical scheme:
a compressor set health prediction method based on time domain data correlation modeling comprises the following steps:
s1: establishing a model, namely establishing a 'state data perception model' for judging the whole working state through normal working condition data in massive industrial time domain operation data of a compressor set, establishing a 'failure mechanism association model' for comparing and analyzing the change of the failure state of a compressor unit through a typical failure rule of the compressor set, and analyzing and observing the whole normal operation state;
s2: normal parameter collection, namely, calculating and analyzing normal state modeling data of compressor unit equipment through a state data perception model to obtain a normal working condition state health degree reference value, and a fault mechanism correlation model obtains a change mechanism of a typical fault through a parameter change rule of typical fault characteristics and records the change rule of the fault under different fault conditions, so that the error type can be conveniently analyzed integrally;
s3: the method comprises the steps of performing contrastive analysis of data extraction, establishing compressor unit equipment by using a compressor unit safety state analysis method of a state data perception model and a fault mechanism correlation model, comparing integrally obtained data, performing calculation analysis on compressor unit real-time sampling data to realize real-time state health quantitative evaluation of the compressor unit equipment, performing prediction diagnosis and early warning prompt on potential faults of the compressor unit by combining a fault mechanism correlation model, performing correlation sequencing on measuring points causing fault warning, and performing multiple analysis and averaging in the process of integral contrastive analysis to ensure the contrastive accuracy of integral data;
s4: and (3) data abnormity warning, namely comparing the safety state analysis condition of the compressor set of the state data perception model and the fault mechanism correlation model with the normal operation condition, if abnormity occurs, performing online real-time calculation on the data of fault parameters of the compressor set equipment at the moment by using the fault mechanism correlation model, and performing specific typical fault prediction diagnosis and early warning prompt on the compressor set equipment through concept calculation of the fault model, so that early warning on the fault risk of the compressor set is facilitated integrally and overhaul is performed before the integral state is serious.
By adopting the technical scheme, the normal operation state and abnormal operation state of the compressor are recorded by using the state data perception model and the fault mechanism correlation model, and the recorded data is compared with the actual data, so that the operation state of the compressor can be analyzed and compared integrally.
Further, the normal operating condition data in the massive industrial time domain operating data of the compressor unit device in S1 is data in which the real-time values of all parameters of the compressor unit device are within a normal range on the same time axis for a period of time.
By adopting the technical scheme, all parameters on the same time axis in a period of time can be used as normal comparison data, different data on the same time axis also represent different equipment running conditions, and the condition of equipment failure can be judged according to different data conditions during comparison.
Further, the normal working condition data in the massive industrial time domain operation data of the compressor unit equipment in S1 is screened to remove abnormal and interfering data, a state group under the normal working condition of the compressor unit equipment is generated, and a "state data perception model" reflecting the actual operation rule of the compressor unit equipment is automatically created according to the feature distribution of all parameters in the state group.
Through adopting above-mentioned technical scheme, the whole body detects the contrast result and can produce the influence by improper and interference data, so need calculate improper and interference data and substitute whole detection numerical value and get rid of, guarantee the accuracy of whole detection.
Further, the "fault mechanism association model" in S2 is that a typical fault rule of a compressor unit device is used, a characteristic parameter change rule of different typical faults is selected to establish a fault rule combination, and a characteristic parameter change rule of multiple typical faults of the compressor unit is synthesized to establish a unit fault mechanism association model together.
By adopting the technical scheme, the fault mechanism correlation model is used for recording fault combination and independent fault condition data under different conditions, and the condition of the running compressor is compared with the fault request, so that the overall fault type can be conveniently and integrally and rapidly analyzed, and the overall maintenance is convenient.
Further, the modeling data of the "fault mechanism correlation model" and the "state data perception model" in S2 are historical normal operating condition time domain data of the compressor unit equipment and historical time domain data of each typical fault parameter.
By adopting the technical scheme, the fault mechanism correlation model and the state data perception model need to record data under various conditions, and the overall record is historical normal working condition time domain data and historical time domain data of each typical fault parameter, so that common faults can be conveniently and integrally identified.
Further, the "state data perception model" in S2 is non-dimensionalized data having a health reference value of 0 to 1.
By adopting the technical scheme, the state data perception model is provided with the reference value, so that the whole body can be conveniently judged on the normal reference value, the whole body can be ensured to be well judged, and the whole body can be conveniently judged on the normal operation.
Further, the contrastive analyses in S3 are divided into two groups, one of the contrastive analyses in S3 takes the data change situation of multiple periods in the same state abnormal situation, and the contrastive analysis in S3 takes the data change situation of different parts relative to each other in the same period.
Through adopting above-mentioned technical scheme, contrast the detection data that wholly produce to guarantee wholly can detect the compressor running state, the detection data that wholly produces simultaneously carries out many times contrast, prevents that external influence from making data produce the change, guarantees the accuracy that wholly detects.
Further, the data abnormality alarm in S4 employs a corresponding speaker alarm device and a warning light alarm device.
Through adopting above-mentioned technical scheme, speaker alarm equipment and warning light alarm equipment can remind the staff in vision and sense of hearing, guarantee whole good warning effect, make things convenient for the staff in time to overhaul.
In conclusion, the beneficial technical effects of the invention are as follows:
1. the method comprises the steps of establishing a state data perception model and a fault mechanism association model by adopting a normal data and fault parameter data change rule of compressor unit equipment, then carrying out calculation analysis on real-time data through the state data perception model to obtain the similarity degree of the current equipment running state and the historical normal working condition, and finally carrying out calculation analysis on the association parameter data of state transaction by using the fault mechanism association model to realize the effects of prediction diagnosis and early warning of the compressor unit equipment fault;
2. the scheme that the early-stage fault of the compressor unit equipment is analyzed and prompted more reasonably by adopting a state data perception model and a fault mechanism correlation model solves the problem that the mode always depends on rules, expert experience judgment and post-processing, and achieves the effects of rapid detection and repeated detection;
3. compared analysis and data abnormity alarm of data extraction are adopted, so that monitoring and analysis of typical faults of compressor unit equipment on site are simpler and more reliable, early warning prompt is carried out on the typical faults of the compressor unit equipment, the faults can be timely solved in a sprouting state, normal and stable operation of the compressor unit equipment and a natural gas conveying pipe network is facilitated, and a timely reminding effect is generated.
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FIG. 1 is a schematic view of the working process of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Referring to fig. 1, a method for predicting health of a compressor unit based on time domain data association modeling includes the following steps:
s1: establishing a model, establishing a 'state data perception model' for judging the whole working state through normal working condition data in the massive industrial time domain operation data of the compressor set, establishing a 'failure mechanism correlation model' for comparing and analyzing the change of the failure state of the compressor set through the typical failure rule of the compressor set, analyzing and observing the whole common operation state, wherein the normal working condition data in the massive industrial time domain operation data of the compressor set is data in which the real-time values of all parameters of the compressor set are in a normal range on the same time axis in a period of time, the normal working condition data in the massive industrial time domain operation data of the compressor set are screened to remove abnormal and interference data to generate a state set under the normal working condition of the compressor set, and according to the characteristic distribution of all parameters in the state set, automatically creating a 'state data perception model' reflecting the actual operation rule of compressor unit equipment;
s2: normal parameter collection, normal state modeling data of compressor unit equipment is calculated and analyzed through a 'state data perception model' to obtain a normal working condition state health degree reference value, a 'fault mechanism correlation model' obtains a typical fault change mechanism through a parameter change rule of typical fault characteristics and records the change rule of faults under different fault conditions, so that the error type can be conveniently and integrally analyzed, the 'fault mechanism correlation model' is a model for establishing a fault rule combination by utilizing the typical fault rule of the compressor unit equipment and selecting the characteristic parameter change rule of different typical faults, the characteristic parameter change rules of various typical faults of the compressor unit are synthesized to jointly establish a unit fault mechanism correlation model, the 'fault mechanism correlation model' and the 'state data perception model' modeling data are historical normal working condition time domain data of the compressor unit equipment and historical time domain data of various typical fault parameters, the state data perception model is dimensionless data with a health degree reference value of 0-1;
s3: the comparative analysis of data extraction, the compressor unit equipment is established by the compressor unit safety state analysis method of a state data perception model and a fault mechanism correlation model, the data obtained integrally are compared, the real-time state health quantitative evaluation of the compressor unit equipment is realized by calculating and analyzing the real-time sampling data of the compressor unit, the prediction diagnosis and early warning prompt of the potential faults of the compressor unit are carried out by combining a fault mechanism correlation model, the measuring points causing the fault warning are subjected to correlation sequencing, meanwhile, in the process of integral comparison and analysis, multiple analyses are needed to take an average value, so that the comparison accuracy of integral data is ensured, the contrastive analysis is divided into two groups, one group adopts the multi-time-period data change condition under the condition of same state abnormity, and the other group adopts the relative data change condition of different parts in the same time period;
s4: and (3) data abnormity warning, namely comparing the safety state analysis condition of the compressor set of the state data perception model and the fault mechanism association model with the normal operation condition, if abnormity occurs, performing online real-time calculation on the data of fault parameters of the compressor set equipment at the moment by using the fault mechanism association model, performing specific typical fault prediction diagnosis and warning prompt on the compressor set equipment through concept calculation of the fault model, and being beneficial to performing warning on the fault risk of the compressor as a whole and performing maintenance before the integral situation is serious, wherein the data abnormity warning adopts corresponding loudspeaker warning equipment and warning lamp warning equipment.
The working principle is as follows: establishing a state data perception model by using time domain data of historical normal working conditions of compressor unit equipment, establishing a fault mechanism correlation model by using a typical fault mode of the compressor unit equipment, wherein the state data perception model covers a section of sampling values which can reflect the running time of the compressor unit equipment under each working condition, each group of data of the state data perception model can express a normal running state of the compressor unit equipment, the state data perception model meets the simultaneity of each variable parameter in each group of sampling values, namely the sampling values of each parameter at the same historical moment are needed, performing calculation analysis on historical data for modeling by using the established state data perception model to obtain a health degree reference value of the normal working condition, and performing calculation analysis on a typical fault rule for modeling by using the established fault mechanism correlation model to obtain a fault characteristic parameter change trend, after a state data perception model and a fault mechanism correlation model are built, the state data perception model obtains a state data correlation model judgment reference value by carrying out inverse calculation on modeling data, the fault mechanism model obtains a change mechanism of a typical fault by carrying out parameter change rules on characteristics of the typical fault, then real-time industrial time domain data is calculated and analyzed by using the state data perception model to obtain the similarity degree between the current operation state and the historical normal state of the compressor unit equipment, once state transaction is generated, the fault mechanism model is immediately used for carrying out calculation and analysis on the fault parameter data of the compressor unit equipment in the transaction time period, the real-time data of the compressor unit equipment is brought into the model for carrying out real-time calculation and analysis to obtain the similarity degree between the current operation state and the historical normal state of the compressor unit equipment, and once the state transaction is lower than the health degree reference value, state transaction prompt is carried out, and then, calculating and analyzing the characteristic data of the compressor unit equipment typical faults mainly represented by the abnormal operation time period by using a fault mechanism correlation model, and carrying out health prediction and early warning prompt on the compressor unit equipment so as to carry out health prediction diagnosis and early warning on the compressor unit equipment.
The embodiments of the present invention are preferred embodiments of the present invention, and the scope of the present invention is not limited by these embodiments, so: all equivalent changes made according to the structure, shape and principle of the invention are covered by the protection scope of the invention.
Claims (8)
1. A compressor set health prediction method based on time domain data correlation modeling is characterized by comprising the following steps: the method comprises the following steps:
s1: establishing a model, namely establishing a 'state data perception model' for judging the whole working state through normal working condition data in massive industrial time domain operation data of a compressor set, establishing a 'failure mechanism association model' for comparing and analyzing the change of the failure state of a compressor unit through a typical failure rule of the compressor set, and analyzing and observing the whole normal operation state;
s2: normal parameter collection, namely, calculating and analyzing normal state modeling data of compressor unit equipment through a state data perception model to obtain a normal working condition state health degree reference value, and a fault mechanism correlation model obtains a change mechanism of a typical fault through a parameter change rule of typical fault characteristics and records the change rule of the fault under different fault conditions, so that the error type can be conveniently analyzed integrally;
s3: the method comprises the steps of performing contrastive analysis of data extraction, establishing compressor unit equipment by using a compressor unit safety state analysis method of a state data perception model and a fault mechanism correlation model, comparing integrally obtained data, performing calculation analysis on compressor unit real-time sampling data to realize real-time state health quantitative evaluation of the compressor unit equipment, performing prediction diagnosis and early warning prompt on potential faults of the compressor unit by combining a fault mechanism correlation model, performing correlation sequencing on measuring points causing fault warning, and performing multiple analysis and averaging in the process of integral contrastive analysis to ensure the contrastive accuracy of integral data;
s4: and (3) data abnormity warning, namely comparing the safety state analysis condition of the compressor set of the state data perception model and the fault mechanism correlation model with the normal operation condition, if abnormity occurs, performing online real-time calculation on the data of fault parameters of the compressor set equipment at the moment by using the fault mechanism correlation model, and performing specific typical fault prediction diagnosis and early warning prompt on the compressor set equipment through concept calculation of the fault model, so that early warning on the fault risk of the compressor set is facilitated integrally and overhaul is performed before the integral state is serious.
2. The method for predicting the health of a compressor unit based on time domain data correlation modeling according to claim 1, wherein: and the normal working condition data in the massive industrial time domain operation data of the compressor unit equipment in the S1 is data in which the real-time values of all parameters of the compressor unit equipment are within a normal range on the same time axis in a period of time.
3. The method for predicting the health of a compressor unit based on time domain data correlation modeling according to claim 2, wherein: and (3) screening normal working condition data in the mass industrial time domain operation data of the compressor unit equipment in the S1 to remove abnormal and interference data, generating a state group of the compressor unit equipment under the normal working condition, and automatically creating a 'state data perception model' reflecting the actual operation rule of the compressor unit equipment according to the characteristic distribution of all parameters in the state group.
4. The method for predicting the health of a compressor unit based on time domain data correlation modeling according to claim 1, wherein: the "fault mechanism association model" in S2 is a unit fault mechanism association model that is created by using typical fault rules of compressor unit equipment, selecting characteristic parameter variation rules of different typical faults to create a fault rule combination, and integrating the characteristic parameter variation rules of various typical faults of the compressor unit.
5. The method for predicting the health of a compressor unit based on time domain data correlation modeling according to claim 1, wherein: and modeling data of a fault mechanism association model and a state data perception model in the S2 are historical normal working condition time domain data of the compressor unit equipment and historical time domain data of each typical fault parameter.
6. The method for predicting the health of a compressor unit based on time domain data correlation modeling according to claim 5, wherein: and in the S2, the health degree reference value of the state data perception model is non-dimensionalized data of 0-1.
7. The method for predicting the health of a compressor unit based on time domain data correlation modeling according to claim 1, wherein: the comparative analysis in the S3 is divided into two groups, one of the comparative analysis in the S3 adopts the multi-period data change condition under the same state abnormal condition, and the other of the comparative analysis in the S3 adopts the relative data change condition of different components in the same period.
8. The method for predicting the health of a compressor unit based on time domain data correlation modeling according to claim 1, wherein: and in the S4, corresponding speaker alarm equipment and warning light alarm equipment are adopted for data abnormity alarm.
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