CN107025382A - A kind of engineering system health analysis system and method based on critical phase transformation theory - Google Patents
A kind of engineering system health analysis system and method based on critical phase transformation theory Download PDFInfo
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Abstract
The present invention relates to a kind of engineering system health analysis system and method based on critical phase transformation theory, the system includes：Signal gathering unit, signal analysis unit and the part of interactive display unit three, this method include following basic step：Signal acquisition, Signal Pretreatment, random fluctuation signal extraction, critical phase transformation pre-warning signal index analysis and signal analysis displaying.The present invention is by the critical phase-change characteristic application project system of engineering system, new point of penetration is provided for engineering system health analysis, the transition of system mode when the present invention can reflect thrashing comprehensively, realize the diagnosis and prediction of the system failure, with preferable applicability.
Description
Technical field
The present invention relates to the health analysis field to engineering system, and in particular to a kind of engineering based on critical phase transformation theory
System health analysis system and method.
Background technology
At present, engineering system health analysis research is broadly divided into three aspects：Fault diagnosis, failure predication and health pipe
Reason.In terms of fault diagnosis, existing research carries out fault diagnosis using the Intelligent Decision-making Method based on model to Mechatronic Systems, together
When also have research use case reasoning (Case-based reasoning, abbreviation CBR) intelligent method, such as tasted in air line
The scheme and genetic algorithm for pinging case-based reasioning carry out fault diagnosis to Boeing-747-700；In terms of failure predication, have
Research is predicted analysis using neural-fuzzy technology to the failure of system, also has part to study based on neutral net and to predict is
Unite failure and assessment equipment residual life, current hydroelectric system, which has, to be made some in terms of neural network prediction failure and taste
Examination, suspension mechanical system analyzes the remaining life with assessment system part by using the decision making approach based on model,
There is research to be absorbed in the service life of the method evaluation means driven using statistics；In terms of health control, there is research to make
Health control is carried out to engineering system with machine learning and Methods of Knowledge Discovering Based, aircraft control system is by using based on model
Decision-making technique is attempted to carry out health control to system.
However, the studies above method is confined to single part or single change mostly to the fault diagnosis of engineering system and prediction
The state-detection and fault diagnosis of amount, because part and number of states are numerous in engineering system, are associated increasingly between each part
Complexity, the fault diagnosis of part aspect is not easy to reflect system level state transition comprehensively.And health analysis system is known mechanism
Know dependency degree larger, and be difficult to use in real-time estimate.There is a small amount of research to make trial in terms of system-level health control at present, such as
Some researchers using Bayesian network design early warning system safe condition integrated approach, the research using gas burning system for answer
With, not only the security to part is analyzed, and also system-level hidden danger is made prediction, and obtains certain achievement, but for being
The research of irrespective of size status change and health control is relatively fewer, still in the immature stage.
Complex System Theory is a kind of theoretical foundation of the system-level specificity analysis of progress emerging in recent years, M Scheffer
《Nature》The theory of upper proposition critical transformation temperature of forecasting system in complication system, causes all circles' concern, complicated at present
It is system-level to carry out that Systems Theory has been applied to economics, ecology, finance, medical science, statistical physics every subjects
Research.Complication system is intelligent, adaptivity main body the system for making action based on local message with moderate number.
Generally there is the critical transformation temperature of system in complication system so that system is changed into another suddenly at this time point from a state
Individual state.There is the spontaneous system failure, such as epilepsy or asthma attack in terms of medical science；There is system crash in financial sector to show
As；In terms of weather, there is Observed Abrupt Climatic Change.When system closes on critical transformation temperature, system just plays pendulum.
It is published on《Nature》On research show that, when complication system closes on critical transformation temperature, it is universal that system would generally possess some
Characteristic：First, when complication system transformation temperature critical close to system, system returns to normal condition after being interfered
Speed can slow down, i.e. critical slowing down (critical slowing down)；Second, in time series before and after system
State becomes more and more similar；3rd, system is when closing on system critical point, and the autocorrelation of system can gradually strengthen.It is above-mentioned
Complex System Theory is applied in multiple fields, with the critical transformation temperature of searching system.Such as in terms of meteorology, pass through
The autocorrelation enhancing reflection system critical slowing down of weather system is analyzed, to judge system whether close to critical phase transformation
Point, so that anticipation weather occurs drastically to change.In terms of ecology, by monitor the state variance of Lakes whether increase come
Predict this critical transformation temperature of lake eutrophication.In terms of sociology, there is critical slowing down by analysis system come pre-
Survey population and collapse this system sudden change state.
But, still no researcher expects becoming critical phase and is applied in engineering system at present, because critical phase
Become theory mainly to apply in the system that ecology, environment, society, finance etc. possess complex systematic dynamics characteristic, in these all
With larger randomness, and its kinetics equation is difficult that description is parsed with kinetics equation, and engineering system is because be
Artificially design, its system dynamics is often believed to describe by accurate system dynamics equation, therefore, in view of
Huge difference of the engineering system with system above in form causes association area scientific research personnel not expect managing critical phase transformation
By apply to engineering system health analysis research in.
The content of the invention
For the transition of system level state, and health analysis system pair can not be reflected in engineering system health analysis comprehensively
Mechanism knowledge dependency is larger, the problem of be difficult to real-time estimate, and the system-level Stochastic Dynamics of utilizing works system of the present invention are special
Property, based on critical phase transformation theory, by engineering system pre-warning signal index analysis, realizing to system-level health analysis theory
Support.
Applicant of the present invention has found, although the system such as engineering system and ecology, environment, society, finance has in form
There is huge difference, but critical phase transformation theoretical origin still has larger applicability in engineering system, and can preferably carry out engineering
System health is analyzed.
Specifically, the present invention provides a kind of engineering system health analysis system based on critical phase transformation theory, its feature
It is, the system includes such as lower unit：
Signal gathering unit, the signal gathering unit is used to gather in the engineering system to work with the engineering system
The related signal with timing of state；
Signal analysis unit, the signal analysis unit is used to pre-processing the signal gathered and being based on pretreatment
Signal afterwards carries out critical Phase Transition Analysis.
In a kind of preferred implementation, the signal analysis unit also includes：Interactive display unit, the interactive display
Unit is used for the result for exporting or showing the critical Phase Transition Analysis.
In another preferred implementation, the signal analysis unit is used to carry out and institute based on pretreated signal
State the purification of the related random fluctuation signal of engineering system failure.
In another preferred implementation, pretreatment is carried out to the signal gathered to be included carrying out the signal gathered
Resampling.
In another preferred implementation, the random fluctuation purifying signal process includes：Remove thrashing unwise
The related random fluctuation signal of trend signal under sense state and working state of system, only extraction system failure.
On the other hand, the present invention provides a kind of engineering system health analysis method based on critical phase transformation theory, its feature
It is, methods described comprises the following steps：
1) signal in the engineering system with timing is gathered,
2) signal gathered is pre-processed,
3) critical Phase Transition Analysis is carried out based on pretreated signal.
In another preferred implementation, methods described also includes：Export or show the result of critical Phase Transition Analysis.
In a kind of preferred implementation, methods described also includes：Carried out and the engineering based on pretreated signal
The purification of the related random fluctuation signal of thrashing.
In another preferred implementation, pretreatment is carried out to the signal gathered to be included carrying out the signal gathered
Resampling.
In another preferred implementation, the random fluctuation purifying signal process includes：Remove thrashing unwise
The related random fluctuation signal of trend signal under sense state and working state of system, only extraction system failure.Institute in the present invention
The signal gathering unit mentioned can carry out signal acquisitions with all method or apparatus that can gather timing signal, including but
It is not limited to manual measurement or installs sensing device subsidiary etc. additional.It should be noted that the timing letter being previously mentioned in the present invention
Number refer to：The sensor signal gathered according to time series.Any signal with timing feature, has change with the time
The signal of property is intended to be included within, such as：The operation of the vibration signal, train that are gathered in bearing rotation process
During axle temperature transducing signal.
It should be noted that in the present invention, removing the insensitive state of failure and referring to analyze coherent signal based on system features
Less signal will be influenceed to reject thrashing.Trend signal under working state of system can be asked by trend learning method
Take and remove system integrality trend.The trend learning method can select but be not limited to Gauss curve fitting mode, specifically can base
In system features selection fitting learning method.In the present invention, the critical phase transformation pre-warning signal analysis：Refer to from suitably finger
Mark indicates critical phase transition phenomena.Main characterize includes but is not limited to critical slowing (Critical Slowing Down) and symmetrical
Offset (Deviating Skewness).Wherein critical slowing down mainly includes auto-correlation (Autocorrelation) and become
Different (Variance) index signal enhancing.
The beneficial effects of the invention are as follows：
(1) present invention jumps out the traditional approach of the health analysis used at present to engineering system, is conceived to complicated electromechanics
The Stochastic Dynamics characteristic of system, health analysis research is carried out with new system-level perspective in research.It is that one kind is directed to engineering system
General-purpose system and method.
(2) present invention is by originally it is believed that the critical phase transformation theory for being not particularly suited for engineering system is applied to engineering system
Health analysis.Critical phase transformation theory is applied in engineering system, new point of penetration is provided for engineering system health analysis；Meaning
Good analytical effect is have received outside.The present invention can reflect the transition of system level state comprehensively, realize real-time estimate, and have
Preferable applicability.
Brief description of the drawings
Fig. 1 is the schematic architectural diagram of the engineering system health analysis system of the present invention；
Fig. 2 is the implementing procedure figure of the engineering system health analysis method of the present invention；
Fig. 3 is the collector signal figure of used igbt in engineering system；
Fig. 4 is random fluctuation purifying signal process schematic in the present invention, and wherein a, b, c represents removal thrashing respectively
The process of insensitive state, the process of removal trend signal and the related random fluctuation signal of the thrashing finally given；
Fig. 5 is the critical phase transformation pre-warning signal index of igbt in engineering system；
Fig. 6 be engineering system in bearing vibration signal；
Fig. 7 is critical phase transformation pre-warning signal index example in the present invention；
Fig. 8 be the embodiment of the present invention 3 in analysis method indicative flowchart.
Embodiment
Such as lower unit is included based on critical phase transformation theory engineering system health analysis system in the present embodiment：Signal acquisition list
Member, signal analysis unit, interactive display unit.
Wherein signal gathering unit mainly uses all methods that can gather clock signal, such as manual measurement, installs additional
Sensing device subsidiary etc., obtains any signal with timing from engineering system.Signal analysis unit is mainly completed
The pretreatment of data works with critical Phase Transition Analysis.Interactive display unit mainly carries out exhibition to critical phase change signal analysis result
Show.
Embodiment 1
Comprised the following steps in the present embodiment based on critical phase transformation theory engineering system health analysis method：
1) engineering system signal acquisition
The health analysis of the present invention is mainly based upon the health analysis of physical signalling, is based especially on the thing that sensor is measured
Manage the analysis that signal is carried out.In engineering system, such as, in order to measure the rotating speed or pressure of aero-engine；Motor
Curtage, igbt can be used.
Here, the detailed description of signal processing is only carried out with igbt.Obtain insulated gate bipolar brilliant
Collector current (collector current) signal of body pipe (Insulated Gate Bipolar Translator), is obtained
The signal taken is as shown in Figure 3.The strong of engineering system where the transistor can be analyzed by analyzing the signal of the transistor
Health state.
2) Signal Pretreatment,
The step mainly divides two parts, and Part I is to obtain time series data by handling, and Part II is random fluctuation
Purifying signal.
Sensing data can usually have inconsistent, missing and the number repeated because being influenceed by a variety of environmental factors
According to.In order to the time series data of time spacing such as obtain, we remove repetition and inconsistent data first, on this basis to lacking
Lose data and enter row interpolation, finally the data to interpolation carry out resampling as the input of random fluctuation signal extraction process.
Assuming that we have clock signal { x1,x2,x3,…,xn, the moment { t is corresponded to respectively1,t2,t3,…,tnObservation.
Any missing values present in clock signal are expressed as (tk,xk).We can use linear interpolation (1) to calculate missing values.Also may be used
To be returned (2) to known clock signal using p order polynomials, then missing values xk=f (tk).We can also make
With p spline interpolation, it is necessary first to which clock signal is divided into disjoint segmentation, s1,s2,s3,…sm, for each segmentation
Recurrence learning is carried out using p order polynomials to obtain (3), then missing values xk=g (tk).
(ta,xa),(tb,xb) it is missing point (tk,xk) before and after two adjacent points of observation.
Unlike signal is different to thrashing sensitiveness, some signals even can not sensory perceptual system failure, therefore in order to carry
Rise the intensity of critical phase transformation pre-warning signal, the mode such as priori of this example based on system, expertise and correlation analysis
The related random fluctuation purifying signal of thrashing is carried out to the signal of acquisition.Decomposability based on time series data, Wo Menli
Signal is decomposed with effective method and the random fluctuation signal with stability of a system height correlation is extracted.
Random fluctuation purifying signal mainly includes removing the trend under the insensitive state of thrashing and working state of system
Signal, so as to extract the related random fluctuation signal of thrashing as analysing content.
Wherein the insensitive state of removal failure is mainly based upon system features analysis coherent signal and thrashing will be influenceed
Less signal is rejected.By taking the signal of igbt as an example, in being signal as shown in from Fig. 3 shown in accompanying drawing 4a
Interception a fragment, we using visual analyzing combination system correlation priori determine working condition number (such as Fig. 3,
4a, signal works in two operating conditions, and senior engineer poses and low operating conditions), and identify to the sensitive operating conditions of thrashing (such as
Fig. 4 a, 4b, the low operating conditions of signal is sensitive state) therefore, the pretreatment module of signal analysis unit can be in high workload
The signal of state is removed, and it is signal shown in figure b to leave the sensitive low operating conditions signal of failure.
Sensitive signal is also needed to remove the trend signal under working state of system, that is, rejects the son for meeting a certain given rule
Signal is to obtain residual error, and residual error herein namely reflects the random fluctuation signal of the stability of a system.For rule subsignal
Extraction, we can use general method, such as polynomial regression (formula 2), Gauss kernel regression (formula 4), autoregression
Methods such as (formula 5) obtains to be fitted clock signal.In addition, we can also use base in the related method of system, this example
Rule subsignal is obtained in expertise and meets equation (6), and based on this, we can use (6) to extract rule subsignal (such as
Fig. 4 b solid lines) and reject to obtain residual error (such as Fig. 4 c) from original signal by it.Signal shown in Fig. 4 c is final after purifying
Random fluctuation signal and the basis that will be calculated as warning index.
K is the kernel function that bandwidth (bandwith) is h, and x is input, and y is output
yt=AR (p)=α0+α1yt-1+α2yt-2+…+αt-pyt-p (5)
yt=exp (α1t+α0) (6)
3) critical phase transformation pre-warning signal index analysis,
The step is realized by signal analysis unit.In this step, critical phase transition phenomena is indicated from suitable index.
Available characterize includes but is not limited to critical slowing (Critical Slowing Down) and symmetrical deflection (Deviating
Skewness).Wherein critical slowing down mainly includes auto-correlation (Autocorrelation) and variation (Variance) index
Signal enhancing.Fig. 5 gives critical phase transformation pre-warning signal index in this example.
We use xt, t=1,2 ..., n represents the signal value of t.The equal of clock signal can be calculated according to (7) formula
Value, (8) formula calculates variance (variance), and (9) formula calculates the auto-correlation (lag- at delayed 1 time point
1autocorrelation), (10) formula calculates deflection (skewness).It should be noted that index herein is based on sliding
The form calculus of dynamic window.
Critical phase transformation index analysis can be carried out based on formula (7)-(10), and then finds the phenomenons such as critical slowing.
Understood for the critical phase transformation pre-warning signal index analysis of the igbt signal shown in Fig. 5, when
(Variance) index that makes a variation and auto-correlation (Autocorrelation) index become big suddenly, that is, critical slowing down occur；When
There is critical slowing (Critical Slowing Down) phenomenon and symmetrical deflection (Deviating Skewness) phenomenon simultaneously
When, i.e., there is critical phase transformation in explanation, and system can provide warning.
4) signal analysis is shown
The step is realized by signal analysis display unit, in the step, mainly according to the critical phase change signal of engineering system
Analysis result, warning is provided for the health analysis prediction and early warning of engineering system.
Embodiment 2
Comprised the following steps in the present embodiment based on critical phase transformation theory engineering system health analysis method：
1) engineering system signal acquisition
This example use all methods that can gather clock signal, including but not limited to manual measurement or install additional sensing dress
Subsidiary is put, the vibration signal (vibration signal) of engineering system middle (center) bearing (bearing), the signal of acquisition is obtained
As shown in Figure 6.
2) Signal Pretreatment,
Because the signal obtained in this example is excessively intensive, therefore the signal obtained in this example is carried out in this step
Resampling.
3) critical phase transformation pre-warning signal index analysis,
In this step, critical phase transition phenomena is indicated from suitable index.Available sign includes but is not limited to face
Boundary's slowing down (Critical Slowing Down) and symmetrical deflection (Deviating Skewness).Wherein critical slowing down
Mainly include auto-correlation (Autocorrelation) and variation (Variance) index signal enhancing.
Fig. 7 gives critical phase transformation pre-warning signal index in this example.
In the present embodiment, it can be seen from the critical phase transformation pre-warning signal index analysis of the bearing signal shown in Fig. 7, work as change
Different (Variance) index and auto-correlation (Autocorrelation) index becomes big suddenly, that is, critical slowing down occurs；When same
When there is critical slowing (Critical Slowing Down) phenomenon and symmetrical deflection (Deviating Skewness) phenomenon
When, i.e., there is critical phase transformation in explanation, and system can provide warning.
4) signal analysis is shown
It is the health analysis of engineering system mainly according to the critical phase change signal analysis result of engineering system in the step
Prediction and early warning provide warning.
Embodiment 3
The 3rd step above is adjusted in the present embodiment, the method for critical Phase Transition Analysis is as shown in figure 8, this point
Analysis method comprises the steps：
1), analyzed, obtained by typical complex dynamics of mechanic electric system specificity analysis and system sensing signal random noise
The system-level Stochastic Dynamics characteristic of complex electromechanical systems；
2) the critical phase transformation theory of complication system is utilized, the critical phase-change characteristic of complex electromechanical systems Stochastic Dynamics is divided
Analysis；
3), on the basis of complex electromechanical systems Stochastic Dynamics attributive analysis, with reference to the complicated machine based on Physical Mechanism
Electric system base reference model and system operating signal log recording complete the study of base reference model parameter, from Physical Mechanism angle
Degree modeling, directly to reflect the relation between system parameters；
4), it is based on dynamic graph model with reference to system operating signal log recording and completes complex electromechanical systems random perturbation parameter
Study, using Dynamic Graph model modeling, preferably to describe relation and parameter between the parameter changed at random in itself because making an uproar
There is the fluctuation caused in sound.The system-level Stochastic Dynamics model built based on this two parts can more fully describe complicated electromechanics
The running status of system, sets up the basis of system-level critical phase transformation state model after being also
5) next, using noise excitation simulation and critical phase transformation state evaluation, the depth driven by noise is strong
Change mode of learning and complete critical phase transformation study, the critical phase transformation state model of constructing system level.In learning process, by setting
System sensing signal parameter value simulates noise excitation, and continuous repetitive exercise makes system actively reach critical transition, leads to
The variation characteristic of overwriting system status parameters data analysis system state parameter, can be according to the fortune of system currently based on this model
The response trend of row Signal Analysis System, so as to complete system health status analysis.
The engineering system health analysis system and method based on critical phase transformation theory that the present invention is provided, based on engineering system
Transducing signal analyze critical phase-change characteristic, build the critical phase transformation elasticity theory of engineering system Stochastic Dynamics.By to engineering
There is provided in a kind of new angle research engineering system from system for the research of the critical phase transformation elasticity theory of system Stochastic Dynamics
The method of relation between all parts, a new visual angle is provided for the health analysis of engineering system.
Although the principle of the present invention is described in detail above in conjunction with the preferred embodiments of the present invention, this area skill
Art personnel are it should be understood that above-described embodiment is only the explanation of the exemplary implementation to the present invention, not to present invention bag
Restriction containing scope.Details in embodiment is simultaneously not meant to limit the scope of the invention, in the spirit without departing substantially from the present invention and
In the case of scope, any equivalent transformation based on technical solution of the present invention, simple replacement etc. are obvious to be changed, and is all fallen within
Within the scope of the present invention.
Claims (10)
1. a kind of engineering system health analysis system based on critical phase transformation theory, it is characterised in that the system includes as follows
Unit：
Signal gathering unit, the signal gathering unit is used to gather in the engineering system and the engineering system working condition
The related signal with timing；
Signal analysis unit, the signal analysis unit is used to the signal gathered is pre-processed and is based on pretreated
Signal carries out critical Phase Transition Analysis.
2. engineering system health analysis system according to claim 1, it is characterised in that the signal analysis unit is also wrapped
Include：Interactive display unit, the interactive display unit is used for the result for exporting or showing the critical Phase Transition Analysis.
3. engineering system health analysis system according to claim 1 or 2, it is characterised in that the signal analysis unit
Purification for carrying out the random fluctuation signal related to engineering system failure based on pretreated signal.
4. engineering system health analysis system according to claim 1 or 2, it is characterised in that enter to the signal gathered
Row pretreatment includes carrying out resampling to the signal gathered.
5. engineering system health analysis system according to claim 1 or 2, it is characterised in that the random fluctuation signal
Purification process includes：The trend signal under the insensitive state of thrashing and working state of system is removed, only extraction system fails
Related random fluctuation signal.
6. a kind of engineering system health analysis method based on critical phase transformation theory, it is characterised in that methods described includes as follows
Step：
1) signal in the engineering system with timing is gathered,
2) signal gathered is pre-processed,
3) critical Phase Transition Analysis is carried out based on pretreated signal.
7. engineering system health analysis method according to claim 6, it is characterised in that methods described also includes：Output
Or the result of the critical Phase Transition Analysis of displaying.
8. engineering system health analysis method according to claim 6, it is characterised in that methods described also includes：It is based on
Pretreated signal carries out the purification of the random fluctuation signal related to engineering system failure.
9. engineering system health analysis method according to claim 6, it is characterised in that carried out to the signal gathered pre-
Processing includes carrying out resampling to the signal gathered.
10. method according to claim 6, it is characterised in that the random fluctuation purifying signal process includes：Remove system
Trend signal under the insensitive state of system failure and working state of system, the only related random fluctuation signal of extraction system failure.
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