CN113821974A - Engine residual life prediction method based on multiple failure modes - Google Patents

Engine residual life prediction method based on multiple failure modes Download PDF

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CN113821974A
CN113821974A CN202111042036.2A CN202111042036A CN113821974A CN 113821974 A CN113821974 A CN 113821974A CN 202111042036 A CN202111042036 A CN 202111042036A CN 113821974 A CN113821974 A CN 113821974A
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residual life
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李珍
吴建国
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Abstract

The invention provides a multi-fault-mode-based engine residual life prediction method, which comprises the steps of firstly extracting features from multi-channel sensing data by using a long-time memory network (LSTM) network, then constructing a sequential neural network by adopting a function structure based on the neural network and simultaneously considering the logical relationship between a fault mode discrimination model and a residual life prediction regression model, and finally outputting residual life prediction. Compared with the traditional method, the method is suitable for residual life prediction under the condition of multiple fault modes, can improve the accuracy of the estimation result, and provides a more accurate prediction result.

Description

Engine residual life prediction method based on multiple failure modes
Technical Field
The invention belongs to a prediction technology of the service life of an engine, and particularly relates to a method for fusing multi-source sensor signals, constructing a sequential multi-task learning model and predicting the residual life of the engine.
Background
Nowadays, with the rapid development of sensors and information technology, multiple sensors are commonly embedded in a complex machine system to form a sensor network for machine condition monitoring and residual service life prediction. Therefore, it is essential to develop appropriate data fusion and feature extraction techniques based on high-dimensional sensor data. However, most existing engine life prediction methods only address one failure mode, ignoring the differences between the multiple potential failure modes. In fact, as manufacturing processes evolve, complex systems are susceptible to a variety of failure modes, such as communication systems and large rotating machinery. Under different failure modes, the degradation process may exhibit significantly different degradation paths, which makes condition monitoring and remaining useful life prediction more challenging. Since different failure modes have significant impact on the degradation trajectory, using only one unified predictive model for remaining life prediction may result in low generalization performance across different failure modes or high model complexity due to approximating piecewise functions. Therefore, residual life prediction, which takes into account failure mode identification, is an essential step for achieving accurate and robust life analysis.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for predicting the remaining life of an engine based on multiple fault modes, and particularly provides a method for predicting the remaining life of the engine based on multiple fault mode judgments, which is based on a neural network constructed by adopting a function structure based on the neural network and simultaneously considering the logical relationship between a fault mode judgment model and a remaining life prediction regression model. The method comprises the following steps:
(1) processing multi-sensor data into a fixed length L having S dimensions
Figure BDA0003249654890000011
A time window of which Qn＝max(Tn-L +1, 1); wherein:
let N denote the total number of machines and T ═ T (T)1，...，TN) Representing the total life cycle of the N engines, assuming corresponding multi-sensor data
Figure BDA0003249654890000012
Is shown in which
Figure BDA0003249654890000013
Multi-sensor data for machine n with failure mode k, size TnIs multiplied by S, given as
Figure BDA0003249654890000014
Figure BDA0003249654890000021
Is the s-th sensor timing data for machine n with failure mode k,
Figure BDA0003249654890000022
is the sensor data for engine n, the s-th sensor observation data point during observation period t;
(2) the long-time memory network layer composed of multiple layers of LSTMs can better extract time characteristics and characterize degradation modes from time sequence data, a classification subtask aiming at each failure mode is designed to be a neural network of a full-connection layer, and the full-connection layer can effectively compile all neurons and learn a potential nonlinear function between input and output characteristics of the full-connection layer, wherein:
long-short time memory network (LSTM) unit state ctForgetting gate gammafUpdating the gate gammauCandidate state
Figure BDA0003249654890000023
Output gate gammaoAnd the final output value htComposition, let W and b denote weights and biases in neural networks, footnotes f, u, c and o denote forget-to-gate, update-gate and candidate cells and outputs, respectively
Γf＝σ[Wf(ht-1，xt)]+bf(formula 2)
Γu＝σ[Wu(ht-1，xt)]+bu(formula 3)
Then can be based on a control gate (forgetting gate Γ)fUpdating the gate gammau) Updating the New cell State ctAs shown below
Figure BDA0003249654890000024
Figure BDA0003249654890000025
Final output htBased on cell state ctBut will pass through the output gate ΓoThe Sigmoid (sigma) function of (a) is filtered,
ht＝Γo⊙ct(formula 7)
(3) The fully-connected layer uses the ReLU function as the activation function for the middle layer, while the Softmax operation is used for the output of the last fully-connected layer,
Figure BDA0003249654890000026
the Softmax activation function of (1) is as follows:
Figure BDA0003249654890000027
the probability that an instance n at time t belongs to failure mode k is derived
Figure BDA0003249654890000028
Wherein e is a natural constant e;
(4) branching fully-connected layers into multiple regression sub-network models based on different failure modes, generating a residual life prediction estimate for each regression auto-network
Figure BDA0003249654890000029
Targeting each regression subnetwork to a different failure mode
Figure BDA00032496548900000210
Generated by
Figure BDA00032496548900000211
By integrating the probability outputs to improve the predictionAccuracy, final output residual life prediction as
Figure BDA00032496548900000212
Compared with the prior art, the invention has the beneficial effects that: (1) establishing branch sub-networks for input of residual life prediction aiming at multiple failure modes, and approximating a complex function through a sub-network model with low complexity; (2) the invention can realize data expansion and knowledge migration of the residual life prediction task between the fault mode diagnosis and the residual life prediction and under different fault modes.
Drawings
FIG. 1 is a schematic diagram of the specific mechanism of the LSTM of the present invention;
FIG. 2 is a schematic diagram of the LSTM network architecture of the present invention;
FIG. 3 is a schematic flow chart of the present invention.
Detailed Description
The invention will be further described by way of examples, without in any way limiting the scope of the invention, with reference to the accompanying drawings.
Let N denote the total number of machines and T ═ T (T)1，...，TN) Representing the total life cycle of the N engines. Assuming corresponding multi-sensor data
Figure BDA0003249654890000031
Is shown in which
Figure BDA0003249654890000032
Multi-sensor data for machine n with failure mode k, size TnIs multiplied by S, given as
Figure BDA0003249654890000033
Figure BDA0003249654890000034
Machine with failure mode kThe s-th sensor timing data for n,
Figure BDA0003249654890000035
is the observed data collected by the s-th sensor of engine n during observation period t. The key point of the invention is to identify potential failure modes according to the state monitoring signals and further predict the remaining service life. Thus, to describe the sequential degradation process and capture the long-term dependence of the sensor signal, the present invention employs an RNN-based mechanism to process the time data. The basic idea of a circular memory network (RNN) is to establish connections between history cells starting with a directed loop. In this process, the key to memorize the valuable information is the transfer function H, the conversion formula of which is
ht＝H(xt，ht-1)， (3)
Where H accepts the input vector x for the current timetAnd a hidden output vector ht-1The latter is the internal state of the previously input memory for updating the current hidden output ht. Through this feedback mechanism, the historical state interacts with the current input and helps to retain important information. However, RNN is fundamentally a very deep feed-forward network in which all layers share the same weights, which makes it difficult to preserve long-term information. To address the gradient vanishing or explosion issues that may be encountered when training conventional RNNs, long-short memory networks (LSTM) were developed as an important variant of RNN architecture. Since different temporal patterns captured by the LSTM are crucial for task learning, it is well suited to classify and predict long-term sequence data. The LSTM specific mechanism may be described as fig. 1.
Since it is derived from a standard recurrent neural network, there is also a feedback connection in the LSTM. Under the mechanism, the output of the LSTM unit at the previous time point t-1 is compared with the current time point xtAre combined and fed into the next cell. These sequential elements form an LSTM network to process the time data of multiple sensors.
Specifically, the LSTM cell is defined by cell state ctForgetting gate gammafUpdating the gate gammauCandidate state
Figure BDA0003249654890000041
Output gate gammaoAnd the final output value htAnd (4) forming. The unit will remember the values in any time interval and the gate will select the valuable information to pass to the LSTM. Fig. 2 depicts the structure of an LSTM single element network.
In particular, the gradual information transition in the LSTM may be explained as follows. Let W and b denote weights and biases in the neural network, and footnotes f, u, c and o denote forget-to-gate, update-gate and candidate cells, and outputs, respectively. The first step of the LSTM is to determine which information in the cell state should be discarded, which is accomplished by a "forget gate". Forgetting gate gammafThe method is composed of an S-shaped neuron layer and point-by-point multiplication. sigmoid function on last step ht-1Output and current input xtTo generate a vector. Since the sigmoid function has an output range (denoted as σ) between 0 and 1, a "forgetting gate" will adjust the flow of information that should be shifted out of the cell according to the following operations,
Γf＝σ[Wf(ht-1，xt)]+bf， (3)
it then needs to be determined which information should be stored in the cell state, including the following two parts. First, ΓuThe sigmoid function in (1) determines a value to be updated, and then the tanh layer creates a candidate vector
Figure BDA0003249654890000042
The above operation can be expressed by the following formula:
Γu＝σ[Wu(ht-1，xt)]+bu， (4)
the new cell state c may then be updated according to the control gatetAs shown below
Figure BDA0003249654890000043
Figure BDA0003249654890000044
Final output htBased on cell state ctBut will be filtered by the Sigmoid (σ) function to determine which part of the cell state to output in the following equation.
ht＝Γo⊙ct (7)
With the aforementioned unit storage function mechanism, the LSTM unit vector can artificially forget its previously stored memory and can add new information during information transmission.
The present invention provides a method for predicting the remaining life of an engine, as shown in FIG. 3, which mainly comprises four parts, namely filling, sharing representation, classification and regression. In particular, the method of manufacturing a semiconductor device,
the first partial fill is to perform the necessary data preprocessing on the raw multi-channel monitoring data, and typically the model built should be able to predict engines with various data lengths, however, the LSTM model defines a time series with inputs of fixed length. Therefore, in addition to basic preprocessing operations such as normalization, a padding layer is required to process sequence batches having a duration less than a predetermined sequence length. In this layer, the sequence is batch-padded to a fixed length with a mask value that is preset to be completely different from the actual sensor signal possible value. Thus, the filled batch sequence can be directly distinguished by the learned network model through masking techniques, i.e., processing the multi-sensor data into a fixed length L having S dimensions
Figure BDA0003249654890000051
A time window of which Qn＝max(Tn-L+1，1)。
The second part is composed of a shared long-and-short memory network layer composed of multiple layers of LSTM and a fully-connected layer using ReLU (f (x) ═ max (0, x)) as an activation function. The long-time memory network layer can better extract time characteristics and characterize degradation patterns from time sequence data, and the fully-connected layer can effectively compile all neurons and learn potential nonlinear functions between input and output characteristics of the fully-connected layer.
The third classification is failure mode discrimination, the classification subtask for each failure mode is designed as a fully-connected neural network, the ReLU function is used as the activation function of the middle layer, and Softmax operation is used for the output of the last fully-connected layer
Figure BDA0003249654890000052
Figure BDA0003249654890000053
The Softmax activation function of (1) is as follows:
Figure BDA0003249654890000054
the probability that an instance n at time t belongs to a failure mode k can be calculated by the formula
Figure BDA0003249654890000055
The fourth part is regression, and for the prediction task, since different failure modes lead to different degradation processes, a prerequisite for accurate prediction is to group the data into corresponding failure classes and then model the degradation processes separately. Therefore, the shared representation layer before the classification layer is branched into a plurality of regression sub-network models based on different failure modes, and the regression sub-network models are generated for each regression self-network
Figure BDA0003249654890000056
Finally, each regression subnetwork is directed to a different failure mode
Figure BDA0003249654890000057
Generated by
Figure BDA0003249654890000058
Is integrated to extractHigh accuracy of prediction result, and final output of residual life prediction
Figure BDA0003249654890000059
The present invention uses joint learning or optimization to train model parameters. Let θ be [ θ ]c，θr]Representing model parameters, where θcAll parameters, θ, representing the shared presentation layer and the classification layerrAll parameters of the regression layer are represented. For the remaining life prediction task, the true value τ of the remaining lifen，tAnd the estimated value
Figure BDA0003249654890000061
Root Mean Square Error (RMSE) between to define the loss of the predicted task, given by
Figure BDA0003249654890000062
Since the failure mode diagnostic task is essentially a multi-classification problem, the goal of this task is to label categories
Figure BDA0003249654890000063
Approximated and estimated failure mode distribution
Figure BDA0003249654890000064
Setting a cross entropy function for an optimization objective of the classification subtasks, in particular
Figure BDA0003249654890000065
Then, a joint loss function of the two interrelated tasks is calculated, the overall objective being to minimize the joint loss function,
Figure BDA0003249654890000066
where λ is the weight used to adjust the two losses.
The invention relates to an objective function optimization algorithm based on first-order gradient. This step calculates the partial derivative of the loss function:
Figure BDA0003249654890000067
wherein
Figure BDA0003249654890000071
And
Figure BDA0003249654890000072
Figure BDA0003249654890000073
Figure BDA0003249654890000081
Figure BDA0003249654890000082
representing the square of the element. The invention sets the hyper-parameter as alpha-0.001, beta in the training process1＝0.9,β20.999 and e 10-8。
From equations (15) - (16), it can be found that the training of the two tasks is jointly optimized. Unlike traditional multi-task learning (the loss function simply accumulates the loss for each task), the present invention directly couples the fault mode diagnosis and the remaining life prediction. Therefore, by optimizing the joint loss function, the accuracy of the failure mode diagnosis and the remaining life prediction can be further enhanced.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make numerous possible variations and modifications to the present invention, or modify equivalent embodiments, using the methods and techniques disclosed above, without departing from the scope of the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.

Claims (5)

1. An engine residual life prediction method comprises the following steps:
(1) processing multi-sensor data into a fixed length L having S dimensions
Figure FDA0003249654880000011
A time window of which Qn＝max(Tn-L +1, 1); wherein:
let N denote the total number of machines and T ═ T (T)1，...，TN) Representing the total life cycle of the N engines, assuming corresponding multi-sensor data
Figure FDA0003249654880000012
Is shown in which
Figure FDA0003249654880000013
Multi-sensor data for machine n with failure mode k, size TnIs multiplied by S, given as
Figure FDA0003249654880000014
Figure FDA0003249654880000015
S th sensor timing data for machine n with failure mode k，
Figure FDA0003249654880000016
Is the sensor data for engine n, the s-th sensor observation data point during observation period t;
(2) extracting time characteristics of multi-sensor data by adopting a long-time memory network layer and representing a degradation mode from time sequence data, designing classification subtasks of each fault mode into a fully-connected neural network, compiling all neurons by the fully-connected layer and learning a potential nonlinear function between input and output characteristics of the fully-connected layer;
(3) the fully-connected layer uses the ReLU function as the activation function of the middle layer, while the Softmax operation is used for the output of the last fully-connected layer
Figure FDA0003249654880000017
Figure FDA0003249654880000018
The Softmax activation function of (1) is as follows:
Figure FDA0003249654880000019
the probability that the instance n at the time t belongs to the failure mode k is calculated by the formula
Figure FDA00032496548800000110
(4) Branching fully-connected layers into multiple regression sub-network models based on different failure modes, generating a residual life prediction estimate for each regression auto-network
Figure FDA00032496548800000111
Targeting each regression subnetwork to a different failure mode
Figure FDA00032496548800000112
Generated by
Figure FDA00032496548800000113
The probability output is integrated to improve the accuracy of the prediction result, and the final output residual life prediction is as follows:
Figure FDA00032496548800000114
2. the method of predicting remaining engine life as set forth in claim 1, wherein said long-short memory network comprises a cell state ctForgetting gate gammafUpdating the gate gammauAnd candidate states
Figure FDA0003249654880000021
Let W and b denote weights and biases in the neural network, footnotes f, u, c and o denote forget-to-gate, update-gate and candidate cells and outputs, respectively
Wherein the content of the first and second substances,
Γf＝σ[Wf(ht-1，xt)]+bf(formula 4)
Γu＝σ[Wu(ht-1，xt)]+bu(formula 5)
The cell state c is then updated according to the control gatetAs follows:
Figure FDA0003249654880000022
Figure FDA0003249654880000023
3. the method of predicting remaining engine life according to claim 2, wherein the long-short term memory network final outputOut value htBased on cell state ctThrough an output gate ΓoThe Sigmoid (sigma) function of (a) is filtered,
ht＝Γo⊙ct(formula 8).
4. The engine residual life prediction method of claim 1, characterized in that the true value τ of residual lifen，tAnd the estimated value
Figure FDA0003249654880000024
The root mean square error between is used to define the loss of the prediction task, given by:
Figure FDA0003249654880000025
category label
Figure FDA0003249654880000026
Probability of approaching failure mode k
Figure FDA0003249654880000027
Setting a cross entropy function aiming at the classification subtasks, specifically:
Figure FDA0003249654880000028
5. the method for predicting the remaining life of an engine according to claim 4, wherein a joint loss function is calculated, specifically:
Figure FDA0003249654880000029
where λ is the weight used to adjust the two losses.
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Publication number Priority date Publication date Assignee Title
US20200184131A1 (en) * 2018-06-27 2020-06-11 Dalian University Of Technology A method for prediction of key performance parameter of an aero-engine transition state acceleration process based on space reconstruction
CN109343505A (en) * 2018-09-19 2019-02-15 太原科技大学 Gear method for predicting residual useful life based on shot and long term memory network
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