CN113537044A - Aircraft engine fault diagnosis method based on STFT and improved DenseNet - Google Patents
Aircraft engine fault diagnosis method based on STFT and improved DenseNet Download PDFInfo
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Abstract
The invention discloses an aircraft engine fault diagnosis method based on STFT and improved DenseNet, and relates to the problem that the existing method has a poor diagnosis effect on a variable-speed fault data set of an engine in the field of aircraft engine fault diagnosis. The operation conditions of the aircraft engine in the production environment are complex and changeable, and the aircraft engine often undergoes variable speed processes of variable acceleration and variable deceleration, and although the existing method has good effect in a single speed data set, the effect in the variable speed data set is poor; in order to solve the problem, the invention provides an aircraft engine fault diagnosis method based on STFT and improved DenseNet; the method comprises the steps of enabling original signals to generate time-frequency images of various faults through STFT, and then classifying the fault images by using an improved DenseNet model; the fault diagnosis method has the advantages that the fault diagnosis effect on the aeroengine is good through sufficient experimental verification. The invention is applied to fault diagnosis of mechanical equipment such as an aircraft engine and the like.
Description
Technical Field
The invention relates to fault diagnosis of mechanical equipment such as an aircraft engine
Background
The aircraft engine is a highly complex and precise machine, is the heart of a spacecraft, is known as the bright pearl on an industrial crown, and is the most complex technical process, the highest technical difficulty and the most expensive part in the aerospace industry. The working environment of the engine is very harsh, and the engine is very easy to have faults, such as corrosion and strike, when the engine runs in the environment with high temperature, high pressure, high stress and strong corrosion for a long time, so that certain parking faults and even destructive damage are brought to the aircraft engine. Therefore, the fault diagnosis of the aircraft engine can effectively improve the working efficiency and the service life of the aircraft engine and reduce the possibility of accidents. However, the aircraft engine has a complicated structure and a large number of parts, so that the failure types are complicated, a series of failure problems including related air passages, oil passages, vibration and the like exist, specific failures such as fouling, thermal deformation, seal fatigue, corrosion and the like are included in the air passage failures, and the vibration failures include specific failures such as fan stall vibration, blade cracks and the like. The key factors caused by different types of fault types are different, for example, the rotating speed of the blades, the actual pressure value of the blades and the like can be used as the factors causing the vibration fault, and the temperature of the air inlet of the engine, the pressure of the air pressure port and the like can be used as the factors causing the air circuit fault.
The fault of the mechanical equipment is usually reflected to the vibration of the surface of the mechanical equipment, so for a long time, the fault diagnosis of the mechanical equipment mainly utilizes a vibration acceleration sensor to collect vibration signals of the surface of the equipment, and then utilizes an intelligent algorithm to diagnose the signals. The existing fault diagnosis research of mechanical equipment is based on a bearing fault data set acquired at a single rotating speed of western university, MFPT and the like, however, in an actual working environment, the operating conditions (different rotating speeds and loads) of the mechanical equipment are constantly changed, particularly, an aircraft engine and an accessory subsystem thereof work under a variable rotating speed environment, and the equipment can experience various complex conditions of repeated acceleration, deceleration, variable acceleration, variable deceleration and the like. But also the resonance of the test bed and the influence of mechanical noise. The difficulty of the data set is high, the existing methods at home and abroad cannot effectively extract and classify the characteristics of the signals, and the extremely poor effect is shown on the Dongan variable-speed data set.
In order to solve the above problems, an aircraft engine fault diagnosis method based on STFT and improved DenseNet is proposed, as shown in fig. 1: the method comprises the steps of carrying out spectrum analysis on an original signal by using STFT to generate a fault time-frequency image, and then training a DenseNet network by using the fault image to classify the fault image; the improved DenseNet network is SEDenseNet (SENet-DenseNet), SEDenseNet is a method of fusing SENet and DenseNet, and an SE module is embedded on the basis of DenseNet-BC, so that not only is the feature reuse and deep information transmission effectively enhanced, but also the recalibration of feature channels is realized, the importance degree of each feature channel is determined through learning, the useful channel features are enhanced according to the weight of each channel, and the channel features which are not useful for the current task are inhibited, and the feature extraction of a time-frequency image is better realized; through a large number of experiments, the method provided by the invention achieves the highest accuracy of 97.32% on a propulsion speed reducer of 'XXX' model of Dongan engine Co., Ltd of China.
Disclosure of Invention
The invention aims to solve the problem that the conventional intelligent detection algorithm cannot realize effective fault diagnosis on an aircraft engine and an accessory subsystem thereof, and provides an aircraft engine fault diagnosis method based on STFT and improved DenseneNet.
The above object of the invention is mainly achieved by the following technical scheme:
the aircraft engine fault diagnosis method based on STFT and improved DenseNet is characterized in that the STFT is used for time-frequency analysis on an original signal to generate a fault time-frequency image, and then an image classification network is used for classification; the method comprises the following steps:
s1, collecting vibration signals of various faults of the engine under the condition of variable-speed operation;
in the data acquisition process, pass through the magnet with vibration acceleration sensor and adsorb on the surface of engine to be connected with the signal acquisition appearance through the connecting wire, use the vibration signal on engine surface of vibration acquisition client software collection of independently developing, in the acquisition process, the workman makes the process of variable rotational speed through the rotational speed and the load of manual control engine, and the software can real-time recording the vibration signal that this in-process produced simultaneously.
S2, constructing an image classification network based on the fusion of SeNet and DenseNet;
embedding an SE module on the basis of DenseNet-BC by using a method of fusing a SEnet and a DenseNet, wherein the SE module mainly comprises four Block modules and four SEnet modules, and the four Block modules respectively comprise (n is 3, 3, 6, 4) DenseLayers; each Block module is followed by a convolution (Translation layer structure) of 1x1 for feature dimension reduction, so that not only is the feature reuse and deep information transmission effectively enhanced, but also the feature channel recalibration is realized, the importance degree of each feature channel is determined through model autonomous learning, the useful channel features are enhanced according to the weight of each channel, the channel features with little use for the current task are inhibited, and the feature extraction of the time-frequency image is better realized; finally, classification is realized by using softmax function
S3, performing framing and windowing on the original signal, performing STFT and normalization, and generating a single-channel gray image;
short-time Fourier transform (STFT) is a mathematical transform related to the Fourier transform to determine the frequency and phase of the local area sinusoid of a time-varying signal; the idea is to assume that the analysis window function g (t) is stationary (pseudo stationary) in a short time interval, and to move the window function so that f (t) g (t) is stationary signal in different finite time widths, thereby calculating the power spectrum at each different time; in the short-time Fourier transform process, the length of a window determines the time resolution and the frequency resolution of a spectrogram, the longer the window is, the longer the intercepted signal is, the longer the signal is, the higher the frequency resolution is after Fourier transform, and the worse the time resolution is; conversely, the shorter the window length is, the shorter the intercepted signal is, the worse the frequency resolution is, and the better the time resolution is, that is, in the short-time fourier transform, the time resolution and the frequency resolution cannot be obtained at the same time, and should be chosen according to specific requirements; after the signals are subjected to frame division and windowing, each frame is subjected to fast Fourier transform and mapped to a pixel representation domain of a single-channel image, and the signals of each frame are superposed to generate a time-frequency image.
S4, making a training set and a testing set;
according to the principle of 7-3 points, the collected 6 fault types (including normal data) of the east-Ann aircraft engine under the variable rotating speed are selected, 70% of samples of each fault are selected as a training set, 30% of samples are selected as a test set, then the training set and the test set are scattered randomly, and the randomness of the samples is increased.
S5, training a model to realize the classification of the fault image;
effects of the invention
The invention mainly aims at the problem that the fault diagnosis accuracy rate of the existing method is low under the variable working condition operating environment of an aircraft engine and an accessory subsystem thereof, and provides an aircraft engine fault diagnosis method based on STFT and improved DenseNet, which is shown in figure 1: the method comprises The steps that through a strong time-frequency analysis tool of FFT (fast Fourier transform), signals are subjected to framing and windowing by utilizing The short-time stationary characteristic of The short-time Fourier transform, The signals are subjected to fast Fourier transform and are mapped to a pixel representation domain of a single-channel image, a single-channel gray image is generated, finally, an image classification network is trained to diagnose fault images, and The model respectively carries out multiple experiments on Bearing data (CWRU) of American Kaiser university, Rolling Bearing Test bench data set (The Politecnico Di Torino Rolling Bearing Test rig) and a reducer 4 channel data set of Dongan 'XX' model type of Chinese aviation; the experimental results are shown in fig. 2; on the public data set of single rotating speed of CWRU and university of Metropolis, the method is not away from other parties, especially the accuracy of the CWRU data set, except that MLP is basically over 95%, and the accuracy of the analysis method based on STFT and HHT reaches 100%. On the basis of a variable rotating speed data set, the method based on original signal feature extraction and classification is obviously in a disadvantage, and the accuracy rate of a relatively influential wide convolution shallow model WDCNN on a CWRU data set is only 35%; the accuracy of MSCNN is only 33.24%; the best ALSTM-FCN is expressed in the time sequence classification task, the accuracy rate is slightly higher than the accuracy rate of the ALSTM-FCN, and the accuracy rate of 45% is obtained. Obviously, the model based on time series classification is better than that based on 1DCNN, because the LSTM is added, the time sequence characteristics in the signals can be effectively extracted, and the ALSTM-FCN benefits from the introduction of attention mechanism weighting, and the accuracy is higher than that of the LSTM-FCN by 2 percentage points on average. The method of the invention is obviously better than the diagnostic method based on the original signal, and the average accuracy of 96.4 percent is obtained on a plurality of data sets of Dongan variable rotating speed; also comparing STFT and HHT, it was found that the method based on STFT and the model of the invention was 47 percentage points higher than the average HHT; the effect based on STFT and ResNet34 was even 50 percentage points higher than the average HHT; from the above analysis it follows: the aircraft engine fault diagnosis method based on STFT and improved DenseNet provided by the invention has good effect on the above 6 data sets.
Drawings
Fig. 1 schematic diagram of fault diagnosis based on STFT and modified DenseNet;
FIG. 2 is a summary of the accuracy of the model on each data set;
detailed description of the invention
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
the aircraft engine fault diagnosis method based on STFT and improved DenseNet provided by the invention has a method schematic diagram as shown in figure 1, and mainly comprises 2 parts of short-time Fourier transform and deep learning classification network:
the model training comprises the following steps:
s1, collecting vibration signals of various faults of the engine under the condition of variable-speed operation;
s2, constructing an image classification network based on the fusion of SeNet and DenseNet;
s3, performing framing and windowing on the original signal, performing STFT and normalization, and generating a single-channel gray image;
s4, making a training set and a testing set;
and S5, training a model to realize the classification of the fault image.
As shown in fig. 1: the method comprises the steps of firstly performing frame windowing on an input signal, performing Fast Fourier Transform (FFT) on each frame, mapping the FFT to a pixel representation domain of a single-channel image, overlapping the images of each frame to generate a fault image, and finally training an SEDenseNet network to classify the fault image.
The following examples illustrate the invention in detail:
1. collecting vibration signals of various faults of an engine under the condition of variable rotating speed operation:
as shown in the upper left part of fig. 1, the experimental vehicle platform of the propulsion retarder of east ampere engine ltd of china aviation, model XXX, is provided with 4 vibration measuring points which are respectively positioned at the left, right, above and below of the equipment; the test bed drives the rotating shaft through the motor to push the speed reducer to rotate; in the data acquisition process, a vibration acceleration sensor is adsorbed on the surface of an engine through a magnet and is connected with a signal acquisition instrument through a connecting line, the vibration acquisition instrument adopts DH5922D of Donghua test, the sensor adopts CA7002A of Huishi technology, vibration signals on the surface of the engine are acquired by using self-developed vibration acquisition client software, the sampling frequency is 20khz, in the acquisition process, workers manually control the rotating speed and the load of the engine to enable the engine to complete the acceleration and deceleration process within 10S, and meanwhile, the software can record vibration signals generated in the process in real time; a total of 6 types of fault data including normal state were collected, and 100 samples for each type were collected for 600 samples.
2. Constructing an image classification network based on the fusion of SeNet and DenseNet:
the deep learning model used by the invention is shown in figure 1: embedding an SE module on the basis of DenseNet-BC by using a method of fusing a SENSet and a DenseNet, wherein the SE module mainly comprises four Block modules and four SENSet modules, the four Block modules respectively comprise (n is 3, 3, 6, 4) DenseLayers, and each DenseLayer comprises a 1x1 convolution and a 3x3 convolution; each Block module is followed by a convolution (Translation layer structure) of 1x1 for feature dimension reduction, which not only effectively enhances feature reuse and deep information transfer, but also realizes the re-calibration of feature channels, determines the importance degree of each feature channel through model autonomous learning, enhances useful channel features according to the weight of each channel and inhibits the channel features with little use for the current task, and aims to better realize the feature extraction of time-frequency images.
3. Performing frame windowing on the original signal, performing STFT and normalization, and generating a single-channel gray image:
short-time Fourier transform (STFT) is a mathematical transform related to the Fourier transform to determine the frequency and phase of the local area sinusoid of a time-varying signal; the idea is to assume that the analysis window function g (t) is stationary (pseudo stationary) in a short time interval, and to move the window function so that f (t) g (t) is stationary signal in different finite time widths, thereby calculating the power spectrum at each different time; in the short-time Fourier transform process, the length of a window determines the time resolution and the frequency resolution of a spectrogram, the longer the window is, the longer the intercepted signal is, the longer the signal is, the higher the frequency resolution is after Fourier transform, and the worse the time resolution is; conversely, the shorter the window length is, the shorter the intercepted signal is, the worse the frequency resolution is, and the better the time resolution is, that is, in the short-time fourier transform, the time resolution and the frequency resolution cannot be obtained at the same time, and should be chosen according to specific requirements; as shown in fig. 1: the method selects a Hamming window with the frame length of 25ms and the frame shift of 15ms to window an original signal, respectively performs fast Fourier transform according to each divided frame, and maps frequency spectrum information after the Fourier transform to a pixel representation domain of a single-channel image through normalization processing, wherein the normalization formula is as follows.
And superposing the signals of the frames after the FFT to generate a time-frequency image.
4. Making a training set and a testing set:
the method is characterized in that 6 fault types (including normal data) of the Dongan aeroengine under the variable rotating speed are collected together, 100 samples of each fault type are selected, 70 samples of each fault type are selected as a training set, 30 samples of each fault type are selected as a test set, then the training set and the test set are scattered randomly, and the randomness of the samples is improved.
5. Training a model to realize classification of fault images:
the input fault image is firstly subjected to convolution of 7x7 and Maxbonding, and then is subjected to four consecutive Dense blocks and SENet, wherein each of the four Block blocks has (n ═ 3, 3, 6, 4) DenseLayers, and each DenseLayer comprises a 1x1 convolution and a 3x3 convolution; each Block module is followed by a convolution (Translation layer structure) of 1x1, and finally, global average pooling is carried out, and then the final full link layer and softmax are used for realizing classification.
Claims (5)
1. The aircraft engine fault diagnosis method based on STFT and improved DenseNet is characterized in that the method uses STFT to perform spectrum analysis on an original signal to generate a fault time-frequency image, and then uses the fault image to train a DenseNet network to realize classification of the fault image; the improved DenseNet network is SEDenseNet (SENet-DenseNet), SEDenseNet is a method of fusing SENet and DenseNet, and an SE module is embedded on the basis of DenseNet-BC, so that not only is the feature reuse and deep information transmission effectively enhanced, but also the recalibration of feature channels is realized, the importance degree of each feature channel is determined through learning, the useful channel features are enhanced according to the weight of each channel, and the channel features which are not useful for the current task are inhibited, and the feature extraction of a time-frequency image is better realized; the method comprises the following steps:
s1, collecting vibration signals of various faults of the engine under the condition of variable-speed operation;
s2, constructing an image classification network based on the fusion of SeNet and DenseNet;
s3, performing framing and windowing on the original signal, performing STFT and normalization, and generating a single-channel gray image;
s4, making a training set and a testing set;
and S5, training a model to realize the classification of the fault image.
2. The STFT and DenseNet-based aircraft engine fault diagnosis method according to claim 1, wherein the principle of collecting vibration signals of the engine under variable-speed operation conditions in step S1 is as follows:
in the data acquisition process, the vibration acceleration sensor is adsorbed on the surface of the engine through a magnet and is connected with the signal acquisition instrument through a connecting wire, vibration signals on the surface of the engine are acquired by using self-developed vibration acquisition client software, in the acquisition process, workers manually control the rotating speed and the load of the engine to enable the engine to complete the process of changing the rotating speed, and meanwhile, the software can record the vibration signals generated in the process in real time.
3. The STFT and DenseNet-based aircraft engine fault diagnosis method according to claim 1, wherein the image classification network based on fusion of SENET and DenseNet in step S2 has the following principles:
embedding an SE module on the basis of DenseNet-BC by using a method of fusing a SEnet and a DenseNet, wherein the SE module mainly comprises four Block modules and four SEnet modules, and the four Block modules respectively comprise (n is 3, 3, 6, 4) DenseLayers; each Block module is followed by a convolution (Translation layer structure) of 1x1 for feature dimension reduction, so that not only is the feature reuse and deep information transmission effectively enhanced, but also the feature channel recalibration is realized, the importance degree of each feature channel is determined through model autonomous learning, the useful channel features are enhanced according to the weight of each channel, the channel features with little use for the current task are inhibited, and the feature extraction of the time-frequency image is better realized; finally, classification is realized by using a softmax function.
4. The STFT and enhanced DenseNet-based aircraft engine fault diagnosis method according to claim 1, wherein the short-time Fourier transform generating the time-frequency image in step S3 is based on the following principle:
short-time Fourier transform (STFT) is a mathematical transform related to the Fourier transform to determine the frequency and phase of the local area sinusoid of a time-varying signal; the idea is to assume that the analysis window function g (t) is stationary (pseudo stationary) in a short time interval, and to move the window function so that f (t) g (t) is stationary signal in different finite time widths, thereby calculating the power spectrum at each different time; in the short-time Fourier transform process, the length of a window determines the time resolution and the frequency resolution of a spectrogram, the longer the window is, the longer the intercepted signal is, the longer the signal is, the higher the frequency resolution is after Fourier transform, and the worse the time resolution is; conversely, the shorter the window length is, the shorter the intercepted signal is, the worse the frequency resolution is, and the better the time resolution is, that is, in the short-time fourier transform, the time resolution and the frequency resolution cannot be obtained at the same time, and should be chosen according to specific requirements; after the signals are subjected to frame division and windowing, each frame is subjected to fast Fourier transform and mapped to a pixel representation domain of a single-channel image, and the signals of each frame are superposed to generate a time-frequency image.
5. The STFT and enhanced DenseNet-based aircraft engine fault diagnosis method according to claim 1, wherein the method for creating the training set and the test set in step S4 is as follows:
according to the principle of 7-3 points, the collected 6 fault types (including normal data) of the east-Ann aircraft engine under the variable rotating speed are selected, 70% of samples of each fault are selected as a training set, 30% of samples are selected as a test set, then the training set and the test set are scattered randomly, and the randomness of the samples is increased.
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