CN201637483U - Rotary equipment failure prediction module - Google Patents
Rotary equipment failure prediction module Download PDFInfo
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- CN201637483U CN201637483U CN2010201018226U CN201020101822U CN201637483U CN 201637483 U CN201637483 U CN 201637483U CN 2010201018226 U CN2010201018226 U CN 2010201018226U CN 201020101822 U CN201020101822 U CN 201020101822U CN 201637483 U CN201637483 U CN 201637483U
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Abstract
The utility model relates to a rotary equipment failure prediction module, comprising a power supply module; the output end of the power supply module is respectively connected with a sensor and a data acquisition module; the sensor conveys detected vibrating signals into the data acquisition module; the data acquisition module conveys the vibrating signals into a data classification module for classification and processing, then conveys the classified and processed vibrating signals into a time sequence predication module, a gray prediction module, a combined predication module and a frequency component amplitude predication module for predication and analysis; the predicted and analyzed signals are conveyed into a self-adaptive optimization selection module for optimized selection, and the optimized data is displayed and stored by a display module; and the data acquisition module and the data classification module are controlled to work by a system control module. The rotary equipment failure prediction module adopting a modular structure carries out predication on vibrating trend of rotary equipment, can adapt to the requirements of different rotary equipments, and also can realize real-time online predication on the state of the rotary equipments. The rotary equipment failure prediction module can be widely applied in failure predication, detection and analysis of various rotary equipments.
Description
Technical field
The utility model relates to a kind of mechanical failure prediction device, particularly about a kind of slewing fault prediction device that is used for slewing presence detection range.
Background technology
To the status monitoring and the fault detect of slewing operation, people very are concerned about all the time and pay attention to.The safe and reliable operation of support equipment can not only be enhanced productivity and can also be reduced cost of equipment maintenance.For big-and-middle-sized plant equipment, traditional maintenance mode is that " based on the preventive maintenance of time " also claims periodic maintenance, no matter being equipment, the principal feature of this maintenance mode whether fault is arranged all by the time prophylactic repair of artificial plan, for avoiding important plant equipment unexpected shutdown to cause enormous economic loss, periodically the time cycle of compulsory maintenance is often left bigger safety coefficient, thereby this maintenance mode is uneconomic.But, under the situation of not grasping plant equipment current state and unpredictable future development, must not adopt then.
Anticipatory maintenance (also claiming state-maintenance) is an international emerging equipment Dynamic Maintenance mode, can fundamentally change original maintenance of equipment system, it is by the running status of plant equipment being done monitoring and being predicted the prophylactic repair mode that replaces based on the time, and being referred to as " in the anticipatory maintenance mode of state ", its principle is: have only when monitoring, analyze and predict the outcome and show when being necessary to keep in repair and just carry out or prepare keeping in repair.This modernized maintenance mode can be monitored and the fault and the maintenance dates of HERALD equipment, even character and the position that can differentiate and predict fault, accomplishes on purpose to overhaul.This modern maintenance mode of precognition maintenance can improve the utilization factor of machine, prolongs the accumulative total maintenance cycle, reduces the accumulative total maintenance frequency, saves the expense of safeguarding, corresponding increasing productivity.Thereby, with advanced person's the traditional preventative maintenance of anticipatory maintenance replacement, become the development trend of the advanced maintenance mode of key equipment and big-and-middle-sized equipment based on the time based on state.But in a large amount of plant equipment that industry spot is used, wherein many key equipment power are big, load is heavy and unstable, operating mode is more abominable, need take measures to guarantee the normal operation of plant equipment and it is implemented the maintenance of science.It is the most effective and most economical adopting any failure prediction method at different plant equipment, and at present existing test forecasting techniques can't be determined often and weigh.Adopt a kind of forecast model often can only effectively realize the state trend prediction at a kind of equipment under specific operating mode, practical face is narrow, can not be applied in the equipment of different operating modes.In addition, current industrial production is more and more paid attention to reducing cost, and particularly requires to prolong the equipment operation cycle when avoiding plant equipment to have an accident as far as possible.For this reason, press for the online trend indication technology that can provide predict device state promptly and accurately to develop and maintain information.
Summary of the invention
At the problems referred to above, the purpose of this utility model provides a kind ofly can realize real time on-line monitoring, precision of prediction is higher and widely used slewing fault prediction device.
For achieving the above object, the utility model is taked following technical scheme: a kind of slewing fault prediction device is characterized in that: it comprises a supply module, a sensor, a data acquisition module, a data classifying module, a time sequence prediction module, a gray prediction module, a combined prediction module, a frequency component amplitude prediction module, an adaptive optimization selection module, a display module and a system control module; The output terminal of described supply module is connected with a data acquisition module with a sensor respectively, described sensor is sent into detected vibration signal in the described data acquisition module, and by described data acquisition module with vibration signal send into classify in the data classifying module handle after, be sent to respectively in a time sequence prediction module, a gray prediction module, a combined prediction module and the frequency component amplitude prediction module and carry out forecast analysis; And the signal after the forecast analysis is sent into an adaptive optimization select to do optimization selection in the module, optimal data is shown and storage by a display module; Described data acquisition module and data classifying module are by a system control module Control work.
Described adaptive optimization selects module to comprise four comparers, the history data store module of the history data store module of the history data store module of a time series forecasting, the history data store module of a gray prediction, a combined prediction, a frequency component amplitude prediction, an optimum solution objective function module and a comparison counter; The input end of each described comparer connects the output terminal of each prediction module and the described history data store module corresponding with each prediction module respectively, the output terminal one tunnel of each described comparer is connected with the input end of each self-corresponding described history data store module respectively, the described optimum solution objective function of another Lu Junyu module connects, the output terminal of described optimum solution objective function module is connected with an input end that compares counter with described display module respectively, described relatively another input end of counter is connected with the output terminal of each described prediction module, and the described relatively output terminal of counter is connected with described optimum solution objective function module.
It is the 16 channel data capture cards of PXIe-6251 that described data acquisition module adopts based on model.
The utility model is owing to take above technical scheme, it has the following advantages: 1, the utility model is owing to adopted data acquisition module, the data classifying module, the time series forecasting module, the gray prediction module, the combined prediction module, frequency component amplitude prediction module, adaptive optimization is selected module, the fault prediction device that display module and system control module are formed, the slewing vibration trend is predicted, therefore such modularization is handled the needs that can not only adapt to different rotary equipment, can also realize the slewing state is carried out the real-time online forecast function.2, the utility model is owing to adopted time series forecasting module, gray prediction module, combined prediction module and four kinds of forecast models of frequency component amplitude prediction module, slewing is carried out Fault Forecast Analysis, and multiple vibration trend forecast model there is adaptive determination method, therefore makes the optimum prediction result that very high judgement success ratio be arranged.3, the utility model is owing to adopt the trend forecasting method of multiple forecast model, can not only provide multiple forecast model to handle predicting the outcome of obtaining, and can show the prediction error value that historical data is predicted in real time by display module, have higher judgement and be worth.4, the utility model utilizes the high computing velocity and the large buffer memory of computing machine because the vibration trend forecast model can be constantly expanded in employing, can realize the preservation of a large amount of historical datas and complicated trend prediction digitizing calculating.The utility model can be widely used in the failure prediction check and analysis of various slewings.
Description of drawings
Fig. 1 is a whole apparatus structure synoptic diagram of the present utility model,
Fig. 2 is that adaptive optimization of the present utility model is selected the modular structure synoptic diagram.
Embodiment
Below in conjunction with drawings and Examples the utility model is described in detail.
As shown in Figure 1, the utility model comprises a supply module 1, a sensor 2, a data acquisition module 3, a data classifying module 4, a time sequence prediction module 5, a gray prediction module 6, a combined prediction module 7, a frequency component amplitude prediction module 8, an adaptive optimization selection module 9, a display module 10 and a system control module 11; The output terminal of supply module 1 is connected with data acquisition module 3 with sensor 2 respectively, is embodied as its function of supplying power.Sensor 2 is sent into detected vibration signal in the data acquisition module 3, after by data acquisition module 3 vibration signal being sent into the processing of classifying in the data classifying module 4 again, be sent to respectively in time series forecasting module 5, gray prediction module 6, combined prediction module 7 and the frequency component amplitude prediction module 8 and carry out forecast analysis.And the signal after the forecast analysis is sent into an adaptive optimization select to do optimization selection in the module 9, optimal data shows real-time status waveform and storage by display module 10.Wherein, data acquisition module 3 and data classifying module 4 can be come the setting data acquisition state by system control module 11, and can be guaranteed the reliability service of system by system control module 11 Control work.
As shown in Figure 2, adaptive optimization of the present utility model selects module 9 to comprise relatively counter 18 of the history data store module 16 of the history data store module 15 of the history data store module 14 of the history data store module 13 of four comparers 12, a time series forecasting, a gray prediction, a combined prediction, a frequency component amplitude prediction, optimum solution objective function module 17 and.The input end of each comparer 12 connects the output terminal of each prediction module and each the history data store module 13～16 corresponding with each prediction module respectively, the output terminal one tunnel of each comparer 12 is connected with the input end of corresponding historical data memory module 13～16 separately respectively, and the current actual vibration signal of each prediction module output and each history data store module 13～16 interior historical datas are compared; Another Lu Junyu optimum solution objective function module 17 connects, the output terminal of optimum solution objective function module 17 is connected with an input end that compares counter 18 with display module 10 respectively, the optimum prediction value is sent in the comparison counter 18, and the current actual vibration signal that compares in the counter 18 with all inputs compares; Relatively another input end of counter 18 is connected with the output terminal of each prediction module, and relatively the output terminal of counter 18 is connected with optimum solution objective function module 17.
In the foregoing description, it is the 16 channel data capture cards of PXIe-6251 that data acquisition module 3 adopts based on model.The signal that this capture card can be gathered comprises: vibration signal is the acceleration transducer signals of piezoelectric type; Temperature signal is a PT100 platinum rhodium resistance temperature transducer signal; Pulse signal is frequency range 100Hz～50MHz, pulsewidth and delay 10ns～5ms, front and back edge≤5ns, the pulse signal of responding range 0.5～10V.
The utility model is according to slewing vibration trend situation, and then selects the failure prediction model, realizes the slewing state is carried out prediction.Its concrete steps are as follows:
1) according to slewing ruuning situation, determine in normal operation, the vibration signal that is fit to time series forecasting module, gray prediction module, combined prediction module and frequency component amplitude prediction module by the data acquisition module online acquisition in the fault prediction device accordingly, and will deposit knowledge base in after the vibration signal pre-service, as the historical data of various forecast models.
2) according to slewing ruuning situation, gather vibration signal under the various situations by the data acquisition module real-time online in the fault prediction device, and with the knowledge base that deposits in after the vibration signal pre-service in the data classifying module, as the current demand signal data of various forecast models.
3) utilize by time series predicting model, grey forecasting model (Gray model, abbreviation GM), the built-up pattern of forming by grey forecasting model (GM) and autoregressive model (AR) (GMAR), and the trend forecasting method of frequency component amplitude forecast model composition, the historical data that is kept in the knowledge base is analyzed, obtain slewing running status development trend in the future, it may further comprise the steps:
Step 1, at different forecast models, unified Definition target selection function F (M
p) be:
F(M
p)＝f(M，R，w，θ，S，δ) (1)
Wherein, M
pBe the optimal objective forecast model, p is a kind of in four kinds of forecast models, i.e. p=1,2,3,4; M is a forecast model;
Be the precision of prediction of different forecast models, wherein, Y={Y (M, k) | k=1,2 ..., n} is an original series, (M k) is the required vibration raw data of various forecast models to Y;
Be predicted data;
Be degree of confidence; (M k) is threshold value to θ;
For objective function is separated root-mean-square error with actual value; K is the historical data number of prediction and calculation.
ε
0(M，k)＝{ε
0(M，Y)|Y＝1，2，...，n} (2)
Can obtain mean absolute error ε by formula (2)
1For:
Can obtain predicated error sequence ε by formula (2), (3)
0Standard deviation S
1For:
Step 3, according to target selection function F (M
s) and predicated error sequence ε
0Standard deviation S
1, four kinds of models in the trend forecasting method are optimized selection, and then obtain minimum predicated error
Degree of confidence χ with maximum
Max(M) be respectively:
χ
max(M
2)＝Max(w
M/θ
M) (6)
The minimum predicated error that step 4, basis are obtained by formula (5) and formula (6)
With maximum confidence χ
Max(M
2), obtain optimal objective function F (M
p), and then definite optimum prediction model M
p, optimal objective function F (M wherein
p) be:
4), after next up-to-date analysis of vibration signal of gathering constantly handled, compare by the result that four kinds of models in the trend forecasting method obtain with previous moment according to slewing ruuning situation.
5) analyze comparative result, according to slewing, according to optimal objective function F (M
s) the optimum prediction model M that obtains
s,, and show next predicted value constantly by display module as the accurately predicting method in this moment.
In sum, the utility model can be realized at different rotary equipment, set up different fault mode reason collection and inference mechanism, and determine the judgment criterion of dynamic trend prediction decision optimization according to national standard, historical archives, expertise, objective basis and change of external conditions, different application objects and different faults pattern are realized the judgement and the decision-making of adaptive optimization.
The various embodiments described above only are preferred implementations of the present utility model, and are every based on the changes and improvements on the technical solutions of the utility model in the present technique field, should not get rid of outside protection domain of the present utility model.
Claims (3)
1. slewing fault prediction device is characterized in that: it comprises that a supply module, a sensor, a data acquisition module, a data classifying module, a time sequence prediction module, a gray prediction module, a combined prediction module, a frequency component amplitude prediction module, an adaptive optimization select module, a display module and a system control module;
The output terminal of described supply module is connected with a data acquisition module with a sensor respectively, described sensor is sent into detected vibration signal in the described data acquisition module, and by described data acquisition module with vibration signal send into classify in the data classifying module handle after, be sent to respectively in a time sequence prediction module, a gray prediction module, a combined prediction module and the frequency component amplitude prediction module and carry out forecast analysis; And the signal after the forecast analysis is sent into an adaptive optimization select to do optimization selection in the module, optimal data is shown and storage by a display module; Described data acquisition module and data classifying module are by a system control module Control work.
2. a kind of slewing fault prediction device as claimed in claim 1, it is characterized in that: described adaptive optimization selects module to comprise four comparers, the history data store module of the history data store module of the history data store module of a time series forecasting, the history data store module of a gray prediction, a combined prediction, a frequency component amplitude prediction, an optimum solution objective function module and a comparison counter;
The input end of each described comparer connects the output terminal of each prediction module and the described history data store module corresponding with each prediction module respectively, the output terminal one tunnel of each described comparer is connected with the input end of each self-corresponding described history data store module respectively, the described optimum solution objective function of another Lu Junyu module connects, the output terminal of described optimum solution objective function module is connected with an input end that compares counter with described display module respectively, described relatively another input end of counter is connected with the output terminal of each described prediction module, and the described relatively output terminal of counter is connected with described optimum solution objective function module.
3. a kind of slewing fault prediction device as claimed in claim 1 or 2 is characterized in that: it is the 16 channel data capture cards of PXIe-6251 that described data acquisition module adopts based on model.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495944A (en) * | 2011-11-11 | 2012-06-13 | 苏州大学 | Time series forecasting method and equipment and system adopting same |
| CN106662505A (en) * | 2014-09-12 | 2017-05-10 | 株式会社神户制钢所 | Rotating machine abnormality detection device, method and system, and rotating machine |
| CN107063427A (en) * | 2016-02-10 | 2017-08-18 | 株式会社神户制钢所 | The abnormality detection system and the method for detecting abnormal of whirler of whirler |
| CN108009174A (en) * | 2016-10-28 | 2018-05-08 | 沈阳高精数控智能技术股份有限公司 | A kind of vibration event time sequencing differentiating method based on pattern match |
-
2010
- 2010-01-27 CN CN2010201018226U patent/CN201637483U/en not_active Expired - Fee Related
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495944A (en) * | 2011-11-11 | 2012-06-13 | 苏州大学 | Time series forecasting method and equipment and system adopting same |
| CN102495944B (en) * | 2011-11-11 | 2014-11-05 | 苏州大学 | Time series forecasting method and equipment and system adopting same |
| CN106662505A (en) * | 2014-09-12 | 2017-05-10 | 株式会社神户制钢所 | Rotating machine abnormality detection device, method and system, and rotating machine |
| US10401329B2 (en) | 2014-09-12 | 2019-09-03 | Kobe Steel, Ltd. | Rotating machine abnormality detection device, method and system, and rotating machine |
| CN106662505B (en) * | 2014-09-12 | 2019-12-27 | 株式会社神户制钢所 | Abnormality detection device for rotary machine, method and system thereof, and rotary machine |
| CN107063427A (en) * | 2016-02-10 | 2017-08-18 | 株式会社神户制钢所 | The abnormality detection system and the method for detecting abnormal of whirler of whirler |
| US10422774B2 (en) | 2016-02-10 | 2019-09-24 | Kobe Steel, Ltd. | System and method for detecting abnormality of rotating machines |
| CN107063427B (en) * | 2016-02-10 | 2020-06-30 | 株式会社神户制钢所 | Abnormality detection system for rotary machine and abnormality detection method for rotary machine |
| CN108009174A (en) * | 2016-10-28 | 2018-05-08 | 沈阳高精数控智能技术股份有限公司 | A kind of vibration event time sequencing differentiating method based on pattern match |
| CN108009174B (en) * | 2016-10-28 | 2021-06-01 | 沈阳高精数控智能技术股份有限公司 | Vibration event time sequence distinguishing method based on pattern matching |
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| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20101117 Termination date: 20130127 |