CN114781600A - Generation method of countermeasure sample and defense method of countermeasure sample - Google Patents
Generation method of countermeasure sample and defense method of countermeasure sample Download PDFInfo
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- CN114781600A CN114781600A CN202210357576.8A CN202210357576A CN114781600A CN 114781600 A CN114781600 A CN 114781600A CN 202210357576 A CN202210357576 A CN 202210357576A CN 114781600 A CN114781600 A CN 114781600A
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Abstract
The invention relates to a generation method of a confrontation sample, which comprises the following steps: step 1, preprocessing an original vibration signal sample to obtain a preprocessed sample xt(ii) a Step 2, sample xtInputting the loss function into a target network f, and calculating a loss function; the initial value of t is 0; step 3, calculating the updated sample xt+1(ii) a Step 4, updating the sample xt+1Measuring to obtain the sample xt+1The result of the measurement of (2); step 5, judging whether the metric result in the step 4 is larger than a preset metric threshold value, if so, judging that x is larger than the preset metric threshold valuet+1As xtThe confrontation sample of (1), end; if not, the value t is updated after adding 1 to the value t, and the step 2 is carried out. A method of defending against a sample is also disclosed. The invention has the advantages that: the confrontation sample constructed in the invention can deceive a target network with higher success rate, ensure the quality of the constructed vibration signal confrontation sample and prevent mechanical failureThe robustness research of the barrier diagnosis system has a promoting effect.
Description
Technical Field
The invention relates to the field of mechanical fault diagnosis, in particular to a countermeasure sample generation method and a countermeasure sample defense method.
Background
With the rapid development of heavy industry and aerospace industry, the diagnosis of mechanical faults is more and more important for ensuring the safe operation and production of equipment. Aiming at the problem that the traditional fault diagnosis method is difficult to solve the uncertainty of manual extraction, a large number of deep learning feature extraction methods are provided, and the development of mechanical fault diagnosis is greatly promoted, wherein the current mainstream method is based on the convolutional neural network for diagnosis. The mechanical fault diagnosis system records the vibration signals of the machine and inputs the vibration signals into the trained diagnosis system to obtain the current state of the mechanical equipment, so that the fault position is located. However, most of the existing mechanical fault diagnosis systems are based on deep learning methods, and under ideal conditions, very high identification accuracy can be achieved. Although the mechanical fault diagnosis method based on the neural network has advanced greatly, the existing diagnosis methods have poor robustness, and the identification accuracy is easily affected by disturbance from the outside, thereby causing identification errors.
For this purpose, confrontation samples need to be generated to perform robustness evaluation on the current model, and the robustness of the model can also be enhanced through confrontation training. The confrontation sample is a sample which is intentionally added with slight disturbance by an attacker, and the main purpose of the confrontation sample is to cause the performance of the deep neural network to be invalid and even induce the deep learning network to make judgment specified by the attacker. The challenge sample should have the property of being identical to its corresponding normal sample, but disabling the deep neural network, in human perception.
However, most of the existing generation technologies of confrontation samples are directed to the directions of images, voices and the like, and for example, chinese patent application No. CN202111390854.1 (application publication No. CN114049537A) discloses a confrontation sample defense method based on a convolutional neural network. In the generation of antagonistic samples of the vibration signal, targeted methods are yet to be explored. In addition, for the image and the audio, the disturbance in the image can be seen by human eyes, the disturbance in the audio can be heard by human ears, and the distortion measurement method of the image and the audio is usually judged by adopting a signal-to-noise ratio (SNR) so as to ensure that the disturbance is not easily perceived as much as possible. However, since the vibration signal is complex, the disturbance cannot be directly recognized by human eyes or human ears, and thus the conventional distortion measurement method against the sample is not suitable for the vibration signal. Further improvements are needed for this purpose.
Disclosure of Invention
The first technical problem to be solved by the present invention is to provide a method for generating a countermeasure sample for a vibration signal, which is suitable for generating a countermeasure sample for a vibration signal to improve the robustness of a mechanical failure diagnosis model.
The second technical problem to be solved by the present invention is to provide a defense method for the countermeasure sample generated by the above method in view of the above prior art.
The technical scheme adopted by the invention for solving the first technical problem is as follows: a method of generating challenge samples for adding a perturbation to a vibration signal, comprising: the method comprises the following steps:
step 1, preprocessing an original vibration signal sample to obtain a preprocessed sample xt(ii) a The sample xtThe sampling sequence of (a) is in turn: x is the number oft(1)、xt(2)、…xt(n)…xt(N); n belongs to {1, 2 … N }; n is sample xtThe total number of sampling sequences of (a);
step 2, the sample xtInputting the calculated loss function into a target network f
the initial value of t is 0;
step 3, calculating the updated sample xt+1(ii) a Sampling sequence points x according to the following formulat(n) updating to obtain updated sampling sequence point xt+1(n)；
Wherein alpha is a preset threshold value, and alpha is more than 0 and less than 1;
as a function of the loss
Finding the sampling sequence point xtA gradient of (n);
to calculate
The sign function of (a);
the updated sampling sequence point x is obtained by sequentially changing N to 1 and 2 … Nt+1(1)、xt+1(2)、…xt+1(N), namely obtaining an updated sample xt+1；
Step 4, updating the sample xt+1Measuring to obtain the sample xt+1The result of the measurement of (2);
the method specifically comprises the following steps: using sliding window to convert sample xt+1Is divided into a plurality of segments of equal length, the metric result of each segment is calculated by the following calculation formula, and the average value of the metric results of each segment is taken as the sample xt+1The measurement result of (2);
the measurement result cost(s) of a certain segment is calculated by the formula:
wherein mean () is a function of the calculated mean; s is the length of each fragment; s is the starting position of a certain segment; | x0(k)|2To calculate x0(k) The square of the absolute value of; | xt+1(k)-x0(k)|2To calculate xt+1(k)-x0(k) The square of the absolute value of;
step 5, judging whether the metric result in the step 4 is larger than a preset metric threshold value, if so, judging that x is larger than the preset metric threshold valuet+1As xtThe confrontation sample of (1), end; if not, the value t is updated after adding 1 to the value t, and the step 2 is carried out.
As an improvement, the step 1 is to perform preprocessing on the original vibration signal sample, specifically:
normalizing each sample sequence data in the original vibration signal sample to [0,1 ]; the normalized calculation formula is:
where x is sample sequence data, and k (x) is a value normalized by the sample sequence x.
Preferably, the target network f in step 2 is a neural network. The neural network can be a convolutional neural network, a deep neural network and the like which are common in the prior art.
The technical solution adopted by the present invention to solve the second technical problem is as follows: a countermeasure sample defense method for defending against a vibration signal countermeasure sample generated by the countermeasure sample generation method, characterized in that: the method comprises the following steps:
step a, preprocessing an acquired vibration signal sample;
b, adding a data processing layer between different neural network layers in the target network f used in the generation of the confrontation sample to construct a defense network;
the calculation formula of the data processing layer is as follows:
wherein, yiFor the input of data-processing layersOut, xiGamma and delta are parameters to be learned for the input of the data processing layer;
m is the total number of data input into the data processing layer in each batch; epsilon is a preset constant;
and c, training the constructed defense network by using the plurality of preprocessed vibration signal samples in the step a to obtain the trained defense network.
In this embodiment, the preprocessing in step a is specifically to normalize the acquired vibration signal samples to [0,1 ].
Compared with the prior art, the invention has the advantages that: the confrontation sample constructed in the invention can deceive a target network with a higher success rate, and ensures the quality of the constructed vibration signal confrontation sample, and in addition, the invention has a promoting effect on the robustness research of a mechanical fault diagnosis system. In addition, the invention provides a simple but very effective defense method aiming at the attack method, and the defense method improves the robustness of the model by adding a data processing layer in the target network, thereby improving the defense capability of the target network, greatly reducing the attack success rate of resisting the attack and simultaneously accelerating the convergence speed of the target network during training.
Drawings
FIG. 1 is a schematic block diagram of a method for generating a challenge sample according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a defense network in an embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the following examples of the drawings.
The vibration signal is acquired by collecting the mechanical equipment, and the mechanical fault diagnosis result is generally obtained by analyzing the vibration signal.
As shown in fig. 1, the method for generating a challenge sample in this embodiment includes the following steps:
step 1, preprocessing an original vibration signal sample to obtain a preprocessed sample xt(ii) a The sample xtThe sampling sequence of (a) is in turn: x is the number oft(1)、xt(2)、…xt(n)…xt(N); n belongs to {1, 2 … N }; n is sample xtThe total number of sampling sequences of (a); x is a radical of a fluorine atomt(1)、xt(2)、xt(n)、xt(N) are respectively a sample xtThe 1 st, 2 nd, nth and nth sample sequences;
in this embodiment, the pretreatment method specifically includes:
normalizing each sample sequence data in the original vibration signal sample to [0,1 ]; the normalized calculation formula is:
wherein x is sample sequence data, and k (x) is a numerical value after the sample sequence x is normalized;
in addition, the pretreatment also comprises conventional treatments such as noise reduction, noise removal and the like;
step 2, the sample xtInputting the calculated loss function into a target network f
the initial value of t is 0;
the target network f is a neural network, which may be a trained convolutional neural network, a deep neural network, a BP neural network, or a neural network that inputs a vibration signal in other prior art, and the specific composition of the neural network refers to the prior art and is not described herein again; for example: the BP neural network is involved in the gearbox fault diagnosis method based on the improved PSO-BP neural network disclosed in the patent number ZL 202110472851.6;
step 3, calculating the updated sample xt+1: sampling sequence points x according to the following formulat(n) updating to obtain updated sampling sequence point xt+1(n)；
Wherein alpha is a preset threshold value, and alpha is more than 0 and less than 1;
as a function of the loss
Finding the sampling sequence point xt(n) a gradient;
to calculate
The sign function of (a);
the updated sampling sequence point x is obtained by sequentially changing N to 1 and 2 … Nt+1(1)、xt+1(2)、…xt+1(N), namely obtaining an updated sample xt+1；
Step 4, updating the sample xt+1Measuring to obtain the sample xt+1The result of the measurement of (2);
the method comprises the following specific steps: using sliding window to convert sample xt+1Is divided into a plurality of segments of equal length, the measurement result of each segment is calculated by the following calculation formula, and the average value of the measurement results of all the segments is taken as the sample xt+1The measurement result of (2);
the measurement result cost(s) of a certain segment is calculated by the formula:
wherein mean () is a function of the calculated mean; s is the length of each fragment; s is the starting position of a certain segment; | x0(k)|2To calculate x0(k) Squared absolute value of; | xt+1(k)-x0(k)|2To calculate xt+1(k)-x0(k) Squared absolute value of;
for images and audio containing information that is directly understandable to humans, distortion measures include signal-to-noise ratios, and distances may ensure that the perturbations are as imperceptible as possible. However, since the vibration signal is complex, the conventional distortion measurement method against the sample is not applicable. Limiting the attack implementation under mechanical fault diagnostic conditions is how to add noise. In fact, noise may appear through the following forms: (i) mechanical movement of the motor; (ii) circuitry of a device within the system; (iii) malicious attacks from computer viruses to alter the original data; (iv) by physical attack by applying an external force to the sensor. For this reason, in the case of an attack, the attack cost can be understood as the external energy applied to the vibration source; therefore, the counterattack sample can be effectively evaluated and measured through the measurement calculation formula of the sample;
step 5, judging whether the metric result in the step 4 is larger than a preset metric threshold value, if so, judging xt+1As xtThe confrontation sample of (1), end; if not, the value t is updated after adding 1 to the value t, and the step 2 is carried out.
In order to defend the challenge sample of the vibration signal generated by the generation method of the challenge sample, the defense method of the challenge sample in the embodiment includes the following steps:
step a, preprocessing an acquired vibration signal sample;
wherein the preprocessing specifically comprises normalizing the collected vibration signal sample to [0,1 ];
b, adding a data processing layer between different neural network layers in the target network f used in the generation of the confrontation sample to construct a defense network; as shown in fig. 2, the data processing layer is disposed between any one of the fully connected layers or all of the fully connected layers;
the calculation formula of the data processing layer is as follows:
wherein, yiAs output of the data processing layer, xiGamma and delta are parameters to be learned for the input of the data processing layer;
m is the total number of data input into the data processing layer in each batch; epsilon is a preset constant;
however, a target network without this data processing layer loses robustness because the difference between the challenge sample and the original input is small and difficult to distinguish. Moreover, the instability in the confrontation training can be relieved by processing the data in the same way, and the confrontation training is helped to enhance the robustness of the model;
and c, training the constructed defense network by using the plurality of preprocessed vibration signal samples in the step a to obtain the trained defense network.
In the embodiment, a continuous vibration signal with a mechanical driving end sampling at 12kHz is adopted, and in order to facilitate training, data needs to be amplified firstly, in the embodiment, 2048 is adopted as a single sample length, and the step length is 28, so that the original vibration signal data is amplified to increase the data volume; after sampling, 1000 samples are respectively obtained for each type of mechanical state. Meanwhile, signal data needs to be normalized to [0,1] by adopting the normalization method in the step 1; and finally, randomly disordering the data and dividing the data set into a training set, a verification set and a test set according to the ratio of 6:2: 2. The constructed defense network can be trained and verified by using the samples in the training set and the verification set, and finally the defense network after training is tested by using the samples in the testing set.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the technical principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (5)
1. A method of generating challenge samples for adding a perturbation to a vibration signal, comprising: the method comprises the following steps:
step 1, preprocessing an original vibration signal sample to obtain a preprocessed sample xt(ii) a The sample xtThe sampling sequence of (a) is in turn: x is the number oft(1)、xt(2)、...xt(n)...xt(N); n belongs to {1, 2.. N }; n is sample xtThe total number of sampling sequences of (a);
step 2, the sample xtInputting the calculated loss function into a target network f
the initial value of t is 0;
step 3, calculating the updated sample xt+1: sampling sequence points x according to the following formulat(n) updating to obtain updated sampling sequence point xt+1(n)；
Wherein alpha is a preset threshold value, and alpha is more than 0 and less than 1;
as a function of the loss
Finding the sample sequence point xt(n) a gradient;
to calculate
The sign function of (a);
sequentially changing N to 1 and 2t+1(1)、xt+1(2)、…xt+1(N), namely obtaining an updated sample xt+1；
Step 4, updating the sample xt+1Measuring to obtain the sample xt+1The measurement result of (2);
the method specifically comprises the following steps: using sliding window to convert sample xt+1Is divided into a plurality of segments of equal length, the metric result of each segment is calculated by the following calculation formula, and the average value of the metric results of all segments is taken as the sample xt+1The result of the measurement of (2);
the measurement result cost(s) of a certain segment is calculated by the formula:
wherein mean () is a function of the calculated mean; s is the length of each fragment; s is the starting position of a certain fragment; | x0(k)|2To calculate x0(k) The square of the absolute value of; | xt+1(k)-x0(k)|2To calculate xt+1(k)-x0(k) The square of the absolute value of;
step 5, judging whether the metric result in the step 4 is larger than a preset metric threshold value, if so, judging xt+1As xtThe confrontation sample of (1), end; if not, the value t is updated after adding 1 to the value t, and the step 2 is carried out.
2. The method of generating a challenge sample according to claim 1, wherein: in the step 1, the original vibration signal sample is preprocessed, specifically:
normalizing each sample sequence data in the original vibration signal sample to [0,1 ]; the normalized calculation formula is:
where x is sample sequence data, and k (x) is a value normalized by the sample sequence x.
3. The method of generating a challenge sample according to claim 1, wherein: the target network f in the step 2 is a neural network.
4. A method for protecting a challenge sample produced by the method for producing a challenge sample according to any one of claims 1 to 3, comprising: the method comprises the following steps:
step a, preprocessing an acquired vibration signal sample;
b, adding a data processing layer between different neural network layers in the target network f used in the generation of the confrontation sample to construct a defense network;
the calculation formula of the data processing layer is as follows:
wherein, yiAs output of the data processing layer, xiGamma and delta are parameters to be learned for the input of the data processing layer;
m is the total number of data input into the data processing layer in each batch; epsilon is a preset constant;
and c, training the constructed defense network by using the plurality of preprocessed vibration signal samples in the step a to obtain the trained defense network.
5. The method of defending against a specimen according to claim 4, wherein: the preprocessing in the step a is specifically to normalize the collected vibration signal samples to [0,1 ].
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| CN115481719A (en) * | 2022-09-20 | 2022-12-16 | 宁波大学 | Method for defending gradient-based attack countermeasure |
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| CN115481719A (en) * | 2022-09-20 | 2022-12-16 | 宁波大学 | Method for defending gradient-based attack countermeasure |
| CN115481719B (en) * | 2022-09-20 | 2023-09-15 | 宁波大学 | Method for defending against attack based on gradient |
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