CN109472110A - A kind of aero-engine remaining life prediction technique based on LSTM network and ARIMA model - Google Patents
A kind of aero-engine remaining life prediction technique based on LSTM network and ARIMA model Download PDFInfo
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Abstract
The aero-engine remaining life prediction technique based on LSTM network and ARIMA model that the invention discloses a kind of, this method comprises: establishing n engine health indicator (LSTM-HI) models based on LSTM deep neural network according to engine history degraded data；According to the passing sensing data of engine, training ARIMA model and backward multi-step prediction engine sensor parameter；By the sensor parameters predicted, whether failure is degenerated to according to LSTM-HI metrics evaluation engine, obtains engine residual service life and its probability distribution.The invention proposes a kind of novel aero-engine remaining life prediction technique, accuracy rate and feasibility with higher play the role of actively promoting for the management of aero-engine real time health, reduction maintenance cost.
Description
Technical field
The invention belongs to aero-engine remaining life technical fields, more particularly to one kind to be based on LSTM (Long
Short-Term Memory) network and ARIMA (Autoregressive Integrated Moving Average Model)
The aero-engine remaining life prediction technique of model.
Background technique
Aero-engine is worked throughout the year under high temperature, high pressure, the harsh conditions of high load, and its working condition often changes
Become, which results in engine failure to take place frequently." heart " of the aero-engine as aircraft, health status directly determines
Passenger personal safety and civil aviaton flight safety.To guarantee its safety, engine must repair in time.
However premature maintenance will certainly cause the increase of airline operation cost, therefore, it is necessary to maintenance time is grasped in due course, from
And the use value of engine is realized to greatest extent.Accurately prediction aero-engine remaining life (RUL) is to ensure
Flight safety and the important measures for reducing maintenance cost.
With the development of artificial intelligence technology, the method for diagnosing faults based on data is to solve being good for for complicated nonlinear system
Health forecasting problem provides a kind of new solution route.For intelligent engine of today control, the sensor of data-driven
Fault diagnosis and analytic redundancy technology also have attracted much attention.Deep learning network is obtained because of its powerful non-linear mapping capability
Extensive concern and research.Recurrent neural network (RNN) can model variation of the data in time series, certainly
The fields such as right Language Processing, speech recognition achieve success.Shot and long term memory network (LSTM) is sent out on the basis of RNN
A kind of deep learning network of the special long-rang dependence for solving the problems, such as data of Zhan Erlai.Its advantage is that not needing complexity
Ginseng is adjusted, default can remember the long-term information of data, to solve long-term Dependence Problem.
In addition, the rapid development of sensor technology and on-line monitoring technique, so as to engine in aircraft operational process
Performance and status data are integrated, are transmitted, being analyzed and draw becomes possibility, this is engine residual life forecast
Provide advantageous technical support and sufficient data preparation.
Summary of the invention
In view of the above technical problems, it is surplus to provide a kind of aero-engine based on LSTM network and ARIMA model by the present invention
Remaining life forecast method, this method can accuracy rate and feasibility with higher, for aero-engine real time health pipe
Reason, reduction maintenance cost play the role of actively promoting.
Technical solution: to achieve the above object, the technical solution adopted by the present invention are as follows:
A kind of aero-engine remaining life prediction technique based on LSTM network and ARIMA model, including it is following
Step:
It is strong to establish the n engines based on LSTM deep neural network according to engine history degraded data for step 1)
Kang Zhibiao (LSTM-HI) model, the health indicator model library of building reflection remaining life；
Step 2), according to the passing sensor data set of spreadsheet engine to be measured, training ARIMA model, design prediction window is long
Spend l, and the engine sensor parameter of prediction window length l backward；
Step 3) resolves LSTM-HI and refers to according to the sensor parameters of prediction and LSTM network health index model library
Whether mark, engine evaluated degenerate to failure, if being more than failure threshold, Fitted probability distribution characteristics parameter obtains engine
Remaining life；Otherwise, step 2) is gone to, prediction window length l is further added by, is successively calculated.
It is preferred: according to engine history degraded data in the step 1), to establish n based on LSTM depth nerve net
Specific step is as follows for engine health indicator (LSTM-HI) model of network:
Step 1.1) is degenerated to the historical data of failure according to aero-engine by health, and suitable sensor is selected to join
Number, and noise reduction, smoothing processing are carried out, form n training dataset；
Step 1.2) is referred to based on deep learning network LSTM and training dataset building aero-engine health status evaluation
LSTM-HI is marked, expression is as follows:
Wherein,Indicate that the sensor parameters of selection, k indicate the quantity of the sensor parameters of selection, t indicates the moment；
Step 1.3), training deep learning network LSTM, obtains n LSTM-HI model, establishes aero-engine health
State evaluation index model library.
Preferred: training deep learning network LSTM in the step 1.3) obtains n LSTM-HI model, establishes boat
Specific step is as follows for empty engine health status evaluation index model library:
Step 1.3.1), data are observed for one group of engine life-cycleWherein,
xtIndicate the sensor parameters of selection,Indicate cycle to failure i.e. its service life of engine, HItIt is the strong of arteface
Kang ZhibiaoAssuming that the input data at current time is xt, the output h of last moment LSTM networkt-1, then when current
The output calculating for carving LSTM network is as follows:
ft=σ (wf·[ht-1,xt]+bf)
it=σ (wi·[ht-1,xt]+bi)
ot=σ (wo·[ht-1,xt]+bo)
ht=ot·Φ(St)
Wherein, σ (), g () indicate that activation primitive, w indicate weight, and b indicates biasing, StIndicate the memory of LSTM network
Quantity of state, itIndicate the output of input gate, ftIndicate the output of forgetting door, otIndicate the output of out gate；
Step 1.3.2), by the output of the corresponding LSTM network of all input samples of step 1.3.1 forward calculation, and remember
Record the error of each training sample；
Step 1.3.3), the error obtained according to forward calculation, by error gradient descent method training LSTM network until
Precision is met the requirements；
Step 1.3.4), the n group engine life-cycle is observed into data, n LSTM network is respectively trained, obtains n LSTM-
HI model, establishes model library.
It is preferred: according to the passing sensing data of engine, training ARIMA model and backward multistep described in step 2)
Predict that specific step is as follows for engine sensor parameter:
Step 2.1), if the sensing data of the passing degeneration of known engine isWherein,
xtIndicate that the sensor measures parameters of selection, r indicate engine previous cycle number；For the sensor parameters of every one-dimensional selection,
Its ARIMA prediction model is established, expression formula is as follows:
wt=φ1·wt-1+φ2·wt-2+…+φp·wp-2
+δ+μt+θ1·μt-1+θ2·μt-2+…+θq·μq-2
Wherein, μtIt is white noise sequence, δ is to indicate sequence data without the constant of 0 equalization, and φ and respectively θ indicate poor
The coefficient of subitem and rolling average item, wtIndicate that data in the Difference Terms of t moment, indicate are as follows:
Wherein, d indicates to carry out the order of difference, and L is delay factor；
Step 2.2), after the order for determining ARIMA model, according to passing data, by Least Square Method
The parameter of ARIMA model；
Step 2.3) carries out multi-step prediction to each sensor parameters respectively according to obtained ARIMA model, obtains each
The prediction data of a sensor.
It is preferred: by the sensor parameters predicted described in step 3), according to LSTM-HI metrics evaluation engine whether
Failure is degenerated to, obtaining engine residual service life and its probability distribution, specific step is as follows:
Step 3.1), calculates the corresponding health indicator HI sequence of sensor prediction data, and definition failure threshold is θf, when
HI judges that engine degenerates to failure when being more than threshold value, and it is engine residual that corresponding recurring number, which subtracts previous cycle number,
Service life RUL；
Step 3.2), the sensor parameters prediction data obtained by LSTM-HI model library and step 2), calculates separately out n
A HI sequence obtains n engine RUL predicted value according to step 3.1)；
Step 3.3) is fitted n engine RUL predicted value using gauss of distribution function, obtains engine RUL
Probability distribution.
The utility model has the advantages that the invention proposes a kind of novel aero-engine remaining life prediction technique, have compared with
High accuracy rate and feasibility has the work actively promoted for the management of aero-engine real time health, reduction maintenance cost
With.
Detailed description of the invention
Fig. 1 is aero-engine remaining life prediction technique schematic diagram.
Fig. 2 is C-MAPSS system structure diagram.
Fig. 3 is the curve after characteristic parameter real change curve and smoothing processing.
Fig. 4 is trend chart of the characteristic parameter after a window is predicted.
Fig. 5 is #1 test engine remaining life prediction result.
Fig. 6 is #2 test engine remaining life prediction result.
Fig. 7 is #3 test engine remaining life prediction result.
Fig. 8 is 20 engine residual life prediction results of test set.
Specific embodiment
A specific embodiment of the invention is further described with reference to the accompanying drawing.
The present invention illustrates a kind of based on the prediction of the aero-engine remaining life of LSTM network and ARIMA model
Method, specifically includes the following steps:
It is strong to establish the n engines based on LSTM deep neural network according to engine history degraded data for step 1)
Kang Zhibiao (LSTM-HI) model, the health indicator model library of building reflection remaining life；
Step 1.1) is degenerated to the historical data of failure according to aero-engine by health, and suitable sensor is selected to join
Number, and noise reduction, smoothing processing are carried out, form n training dataset；
Step 1.2), needle are based on deep learning network LSTM and training dataset building aero-engine health status evaluation
Index LSTM-HI, expression are as follows:
Wherein,Indicate that the sensor parameters of selection, k indicate the quantity of the sensor parameters of selection, t indicates the moment.
Step 1.3), training deep learning network LSTM, obtains n LSTM-HI model, establishes aero-engine health
State evaluation index model library.
Step 1.3.1), data are observed for one group of engine life-cycleWherein,
xtIndicate the sensor parameters of selection,Indicate cycle to failure i.e. its service life of engine, HItIt is the strong of arteface
Kang ZhibiaoAssuming that the input data at current time is xt, the output h of last moment LSTM networkt-1, then currently
The output of moment LSTM network calculates as follows:
ft=σ (wf·[ht-1,xt]+bf)
it=σ (wi·[ht-1,xt]+bi)
ot=σ (wo·[ht-1,xt]+bo)
ht=ot·Φ(St)
Wherein, σ (), g () indicate that activation primitive, w indicate weight, and b indicates biasing, StIndicate the memory of LSTM network
Quantity of state, itIndicate the output of input gate, ftIndicate the output of forgetting door, otIndicate the output of out gate.
Step 1.3.2), by the output of the corresponding LSTM network of all input samples of step 1.3.1 forward calculation, and remember
Record the error of each training sample；
Step 1.3.3), the error obtained according to forward calculation, by error gradient descent method training LSTM network until
Precision is met the requirements；
Step 1.3.4), the n group engine life-cycle is observed into data, n LSTM network is respectively trained, obtains n LSTM-
HI model, establishes model library.
Step 2), according to the passing sensor data set of spreadsheet engine to be measured, training ARIMA model, design prediction window is long
Spend l, and the engine sensor parameter of prediction window length l backward；
Step 2.1), if the sensing data of the passing degeneration of known engine isWherein,
xtIndicate that the sensor measures parameters of selection, r indicate engine previous cycle number.For the sensor parameters of every one-dimensional selection,
Its ARIMA prediction model is established, expression formula is as follows:
wt=φ1·wt-1+φ2·wt-2+…+φp·wp-2
+δ+μt+θ1·μt-1+θ2·μt-2+…+θq·μq-2
Wherein, μtIt is white noise sequence, δ is to indicate sequence data without the constant of 0 equalization, and φ and respectively θ indicate poor
The coefficient of subitem and rolling average item, wtIndicate that data in the Difference Terms of t moment, indicate are as follows:
Wherein, d indicates to carry out the order of difference, and L is delay factor.
Step 2.2), after the order for determining ARIMA model, according to passing data, by Least Square Method
The parameter of ARIMA model；
Step 2.3) carries out multi-step prediction to each sensor parameters respectively according to obtained ARIMA model, obtains each
The prediction data of a sensor.
Step 3) resolves LSTM-HI and refers to according to the sensor parameters of prediction and LSTM network health index model library
Whether mark, engine evaluated degenerate to failure, if being more than failure threshold, Fitted probability distribution characteristics parameter obtains engine
Remaining life；Otherwise, step 2) is gone to, prediction window length l is further added by, is successively calculated；
Step 3.1) calculates the corresponding health indicator HI sequence of sensor prediction data.Definition failure threshold is θf, work as HI
Judge that engine degenerates to failure when more than threshold value, it is that engine residual makes that corresponding recurring number, which subtracts previous cycle number,
With service life RUL；
Step 3.2), the sensor parameters prediction data obtained by LSTM-HI model library and step 2), calculates separately out n
A HI sequence obtains n engine RUL predicted value according to step 3.1)；
Step 3.3) is fitted n engine RUL predicted value using gauss of distribution function, obtains engine RUL
Probability distribution.
In order to verify the aero-engine sensor intelligent analytic redundancy designed by the present invention based on KEOS-ELM algorithm
The validity of design method has carried out relevant Digital Simulation under MATLAB environment.In this emulation, C-MAPSS is used
Data in data set in " train_FD001.txt " and " test_FD001.txt " both of these documents as training data and
Test data, the link of the modules of C-MAPSS and layout are as shown in Figure 2.Wherein, it includes 100 groups that training data is concentrated altogether
Different engines observe data from the life-cycle that health status degenerates to failure, and therefore, the present invention trains 100 LSTM-HI altogether
Model, and establish LSTM-HI model library.Different degrees of observation number is degenerated to comprising 100 groups of difference engines in test set
According to taking the prediction of 20 groups of carry out remaining lifes at random therefrom in order to verify the performance of the proposed method of the present invention.By right
The analysis of 21 sensing datas, choosing wherein effective 5 parameters includes that T24, Nf, Ps30, NRf and BPR carry out engine
Performance parameter prediction, the explanation of each parameter are as shown in table 1.
1 engine sensor measurement parameter of table
Since there are biggish noises for original data, standardization first is carried out to it and smoothing processing obtains more
True engine data.Fig. 3 is that each sensor parameters of a certain group of training data after noise reduction and smoothing processing are bent
Line.Fig. 4 is the result for carrying out parameter multi-step prediction by ARIMA model with first group of Engine test data (#1).Fig. 5,
Fig. 6 and Fig. 7 is to choose 1-3 group Engine test data (#1, #2 and #3), and HI change curve and obtained residue use the longevity
Order prediction result and its probability distribution.Table 2 is whole 20 groups of obtained prediction results of Engine test data.
The whole 20 groups of Engine test data remaining life prediction results of table 2
In conjunction with Fig. 5, Fig. 6 and Fig. 7 and table 2 as can be seen that engine residual life forecast side proposed by the present invention
Method has higher precision of prediction, and Relative Error is 10% or so, and absolute error is largely all located at 10 circulations
Within.Fig. 8 is the result curve of all 20 groups of Engine test datas predictions.But for individual engine such as #4, #10
It is larger with the prediction error of #16 engine, this may be since its current degradation recurring number is smaller, passing sensing data compared with
Few, ARIMA model cannot effectively model its degenerative process, therefore cause to predict that error is larger.But generally speaking,
A kind of aero-engine remaining life prediction technique based on LSTM network and ARIMA model proposed by the present invention can be with
Effectively predict the remaining life of aero-engine, accuracy rate and feasibility with higher.
It should be pointed out that the above description is merely a specific embodiment, but protection scope of the present invention is not
Be confined to this, anyone skilled in the art in the technical scope disclosed by the present invention, the change that can be readily occurred in
Change and replace, should be covered by the scope of protection of the present invention.Therefore, protection scope of the present invention should be with the claim
Protection scope subject to.
Claims (5)
1. a kind of aero-engine remaining life prediction technique based on LSTM network and ARIMA model, it is characterised in that:
The following steps are included:
Step 1) establishes the n engine health indicators based on LSTM deep neural network according to engine history degraded data
(LSTM-HI) model, the health indicator model library of building reflection remaining life；
Step 2), according to the passing sensor data set of spreadsheet engine to be measured, training ARIMA model designs prediction window length l,
And the engine sensor parameter of prediction window length l backward；
Step 3) resolves LSTM-HI index, comments according to the sensor parameters of prediction and LSTM network health index model library
Whether valence engine degenerates to failure, if being more than failure threshold, Fitted probability distribution characteristics parameter, obtaining engine residual makes
Use the service life；Otherwise, step 2) is gone to, prediction window length l is further added by, is successively calculated.
2. a kind of aero-engine remaining life based on LSTM network and ARIMA model according to claim 1
Prediction technique, it is characterised in that: according to engine history degraded data in the step 1), establish n based on LSTM depth mind
Specific step is as follows for engine health indicator (LSTM-HI) model through network:
Step 1.1) is degenerated to the historical data of failure by health according to aero-engine, selects suitable sensor parameters, and
Noise reduction, smoothing processing are carried out, n training dataset is formed；
Step 1.2) constructs aero-engine health status evaluation index based on deep learning network LSTM and training dataset
LSTM-HI, expression are as follows:
Wherein,Indicate that the sensor parameters of selection, k indicate the quantity of the sensor parameters of selection, t indicates the moment；
Step 1.3), training deep learning network LSTM, obtains n LSTM-HI model, establishes aero-engine health status and comment
Valence index model library.
3. a kind of aero-engine remaining life based on LSTM network and ARIMA model according to claim 2
Prediction technique, it is characterised in that: training deep learning network LSTM in the step 1.3) obtains n LSTM-HI model, builds
Specific step is as follows for vertical aero-engine health status evaluation index model library:
Step 1.3.1), data are observed for one group of engine life-cycleWherein, xtTable
Show the sensor parameters of selection,Indicate cycle to failure i.e. its service life of engine, HItIt is that the health of arteface refers to
MarkAssuming that the input data at current time is xt, the output h of last moment LSTM networkt-1, then current time
The output of LSTM network calculates as follows:
ft=σ (wf·[ht-1,xt]+bf)
it=σ (wi·[ht-1,xt]+bi)
ot=σ (wo·[ht-1,xt]+bo)
ht=ot·Φ(St)
Wherein, σ (), g () indicate that activation primitive, w indicate weight, and b indicates biasing, StIndicate the memory state of LSTM network
Amount, itIndicate the output of input gate, ftIndicate the output of forgetting door, otIndicate the output of out gate；
Step 1.3.2), by the output of the corresponding LSTM network of all input samples of step 1.3.1 forward calculation, and record each
The error of training sample；
Step 1.3.3), the error obtained according to forward calculation, by error gradient descent method training LSTM network until precision
It meets the requirements；
Step 1.3.4), the n group engine life-cycle is observed into data, n LSTM network is respectively trained, obtains n LSTM-HI mould
Type establishes model library.
4. a kind of aero-engine remaining life based on LSTM network and ARIMA model according to claim 1
Prediction technique, it is characterised in that: according to the passing sensing data of engine described in step 2), training ARIMA model and backward
Specific step is as follows for multi-step prediction engine sensor parameter:
Step 2.1), if the sensing data of the passing degeneration of known engine isWherein, xtIt indicates
The sensor measures parameters of selection, r indicate engine previous cycle number；For the sensor parameters of every one-dimensional selection, it is established
ARIMA prediction model, expression formula are as follows:
wt=φ1·wt-1+φ2·wt-2+…+φp·wp-2+δ+μt+θ1·μt-1+θ2·μt-2+…+θq·μq-2
Wherein, μtWhite noise sequence, δ be indicate sequence data without the constant of 0 equalization, φ and respectively θ indicate Difference Terms and
The coefficient of rolling average item, wtIndicate that data in the Difference Terms of t moment, indicate are as follows:
Wherein, d indicates to carry out the order of difference, and L is delay factor；
Step 2.2), after the order for determining ARIMA model, according to passing data, by Least Square Method ARIMA model
Parameter；
Step 2.3) carries out multi-step prediction to each sensor parameters respectively, obtains each biography according to obtained ARIMA model
The prediction data of sensor.
5. a kind of aero-engine remaining life based on LSTM network and ARIMA model according to claim 1
Prediction technique, it is characterised in that: by the sensor parameters predicted described in step 3), according to LSTM-HI metrics evaluation engine
Whether failure is degenerated to, and obtaining engine residual service life and its probability distribution, specific step is as follows:
Step 3.1), calculates the corresponding health indicator HI sequence of sensor prediction data, and definition failure threshold is θf, when HI is more than
Judge that engine degenerates to failure when threshold value, it is engine residual service life that corresponding recurring number, which subtracts previous cycle number,
RUL；
Step 3.2), the sensor parameters prediction data obtained by LSTM-HI model library and step 2), calculates separately out n HI
Sequence obtains n engine RUL predicted value according to step 3.1)；
Step 3.3) is fitted n engine RUL predicted value using gauss of distribution function, obtains the probability of engine RUL
Distribution.
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