Fuel gas pipeline leakage diagnosis method based on unbalanced data

Abstract

The invention relates to the technical field of leakage diagnosis, in particular to a gas pipeline leakage diagnosis method based on unbalanced data, and the method comprises the steps: collecting acoustic emission signals in different leakage states, and constructing a training and testing data set; performing principal component analysis on the time-frequency domain signal of the acoustic emission signal to obtain a related signal; reconstructing the related signal into a grayscale image; constructing a five-layer multi-layer feature extraction module of a four-layer structure convolution branch and a side convolution branch to perform feature extraction on the grayscale image; constructing a triple weighting module based on channel weighting, position weighting and identical mapping, and inputting an output feature map of the multi-level feature extraction module into the triple weighting module; and inputting the output characteristic pattern of the triple weighting module into a working condition discrimination module, and outputting the pipeline leakage type. The method solves the problem that the prediction accuracy of unbalanced data in the gas pipeline monitoring data under the normal working condition far more than the abnormal working condition needs to be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of leakage diagnosis, and in particular to a gas pipeline leakage diagnosis method based on imbalance data. Background Art

[0002] Urban gas pipeline systems are generally built directly outdoors, and non-buried gas pipelines are exposed to the outside, which is easily affected by a variety of external factors, causing corrosion, damage, perforation, cracks and other leaks in the gas pipelines, which seriously threaten the safety of urban residents and bring huge economic losses.

[0003] In addition, since most gas pipelines are located near residential areas, other ground public facilities are often interlaced around the pipelines. The complex use environment further increases the possibility of pipelines being hit, squeezed and twisted by humans, resulting in an increased risk of leakage. However, as a special equipment, gas pipelines are in far more normal working conditions than leakage or abnormal conditions, and leakage usually only occurs in very rare cases. This results in the fact that in the gas pipeline monitoring data, normal operating data far outweighs leakage data, forming a typical unbalanced data structure. This long-tail data characteristic makes it difficult for conventional fault diagnosis methods to accurately detect leakage signals in a small amount of abnormal data, and it is easy to cause the model to overfit to the normal state, weakening the ability to identify the abnormal state.

[0004] Existing gas pipeline detection methods such as magnetic particle method, magnetic leakage method, ultrasonic detection method, etc., perform poorly in the case of unbalanced data. They have disadvantages such as limited detection objects, complex operation procedures, and high false detection and missed detection rates. It is difficult to effectively deal with a small number of abnormal signals. In order to ensure the safe and stable operation of gas pipelines and reduce the economic losses and social impacts caused by leakage, it is particularly necessary to design an intelligent gas pipeline detection method that can efficiently diagnose leaks in the case of unbalanced data. Summary of the invention

[0005] In view of the shortcomings of the existing methods, the present invention solves the problem that the prediction accuracy of unbalanced data under normal conditions far outweighs that under abnormal conditions in gas pipeline monitoring data needs to be improved.

[0006] The technical solution adopted by the present invention is: a gas pipeline leakage diagnosis method based on unbalanced data comprises the following steps:

[0007] Step 1: Collect acoustic emission signals under different leakage conditions and construct training and test data sets;

[0008] As a preferred embodiment of the present invention, the leakage status includes: healthy, slight leakage, moderate leakage and severe leakage.

[0009] As a preferred embodiment of the present invention, the leakage state is achieved by adjusting the opening of the pipeline valve.

[0010] Step 2: Perform principal component analysis on the time-frequency domain signals of the acoustic emission signals to obtain relevant signals;

[0011] As a preferred embodiment of the present invention, the acoustic emission signal includes: mean, root mean square, kurtosis, energy, amplitude, rise time, skewness, correlation coefficient, corner rate, frequency, spectrum peak, peak slope, and coherence.

[0012] As a preferred implementation manner of the present invention, the related signals include: energy, mean value, root mean square, kurtosis, rise time and count.

[0013] Step 3: reconstruct the relevant signal into a grayscale image;

[0014] Step 4: Construct a five-layer multi-layer feature extraction module with four-layer structure convolution branches and side convolution branches to extract features from the grayscale image;

[0015] As a preferred embodiment of the present invention, the first layer of the multi-layer feature extraction module is: 8×8 convolution, hole convolution, 3×3 convolution and 1×1 convolution; the second layer is 7×7 convolution, hole convolution, 3×3 convolution and 1x1 convolution; the third layer is 6×6 convolution, hole convolution, 3×3 convolution and 1×1 convolution; the fourth layer is 5×5 convolution, hole convolution, 3×3 convolution and 1×1 convolution; the fifth layer is 3×3 convolution and 1×1 convolution; the five-layer branch outputs the feature map through softmax.

[0016] Step 5: construct a triple weighted module based on channel weighting, position weighting and identity mapping, and input the output feature map of the multi-level feature extraction module into the triple weighted module;

[0017] As a preferred embodiment of the present invention, the triple weighted module includes: channel weighting adjusts the input C×H×W of the spatial dimension of the input feature map to the specified output C×1×1 through adaptive average pooling, and converts the input C×1×1 into 2C×1×1 and then into C×1×1 through the lifting and lowering channel operation; position weighting is the input feature map after global average pooling, edge filling, element averaging and bilinear interpolation; the channel-weighted and position-weighted feature maps are multiplied with the input feature map to obtain the weighted feature map.

[0018] Step 6: Input the output feature map of the triple weighted module into the working condition discrimination module to output the pipeline leakage type;

[0019] As a preferred embodiment of the present invention, the operating condition discrimination module is a fully connected neural network.

[0020] As a preferred embodiment of the present invention, a gas pipeline leakage diagnosis system based on unbalanced data includes: a memory for storing instructions executable by a processor; a processor for executing the instructions to implement a gas pipeline leakage diagnosis method based on unbalanced data.

[0021] As a preferred embodiment of the present invention, a computer readable medium stores computer program code, and the computer program code implements a gas pipeline leakage diagnosis method based on imbalance data when executed by a processor.

[0022] Beneficial effects of the present invention:

[0023] 1. The present invention introduces acoustic emission technology and neural network models in machine learning into gas pipeline leakage diagnosis, constructs a multi-level feature extraction module, and extracts different features in the leakage image through five branches. The feature extraction based on the gas pipeline acoustic emission signal can quickly obtain the pipeline characterization parameters, and combined with the neural network, it can accurately diagnose pipeline leakage;

[0024] 2. The present invention constructs a triple weighted module to reduce the risk of overfitting and realizes double weighting of important features in key channels and key positions;

[0025] 3. The test cost of the present invention is low. It only needs to collect the acoustic emission signal of the pipeline operation through the acoustic emission system, and then a simple neural network can be established through computer processing to obtain the test results. The characteristic parameter processing and calculation of the acoustic signal are flexible, and multiple different input neurons can be set according to the diagnosis requirements. This method can predict and diagnose the leakage of the gas pipeline.

[0026] 4. The training data set of the neural network of the present invention is gradually updated and enriched as the number of tests increases, which can significantly improve the efficiency and accuracy of subsequent tests;

[0027] 5. The present invention can be effectively applied in gas pipeline leakage diagnosis and obtain excellent diagnostic results, and is suitable for fault diagnosis of engineering equipment and instruments such as gas pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of a gas pipeline leakage diagnosis method based on unbalanced data of the present invention;

[0029] Figure 2 It is the principal component analysis result of acoustic emission signal;

[0030] Figure 3 This is a simulation diagram of a special leakage valve for gas pipeline leakage test;

[0031] Figure 4 It is a schematic diagram of the structure of the model of the present invention;

[0032] Figure 5 is the confusion matrix result;

[0033] Figure 6 These are the accuracy results when the ratio of normal data to abnormal data is 1:4, 1:10, 1:30, and 1:50. DETAILED DESCRIPTION

[0034] The present invention is further described below in conjunction with the accompanying drawings and embodiments. This figure is a simplified schematic diagram, which only illustrates the basic structure of the present invention in a schematic manner, and therefore it only shows the components related to the present invention.

[0035] like Figure 1 As shown, a gas pipeline leakage diagnosis method based on imbalance data includes the following steps:

[0036] Step 1: Combine the gas pipeline test platform and gas pipeline leakage simulation to collect acoustic emission signals under different leakage conditions and construct training and test data sets;

[0037] The gas pipeline test platform is a three-layer coiled structure of the gas pipeline with a total length of 55 meters, a total width of 2 meters, a total height of 1.05 meters, a total wall thickness of 5 mm, and a pipeline diameter of 25 mm.

[0038] Step 2: By controlling the opening, simulate the leakage condition and collect the signals collected at different openings; for the acoustic emission signal;

[0039] Gas pipeline leakage simulation refers to controlling the leakage degree by adjusting the opening of the special leakage valve of the gas pipeline;

[0040] like Figure 3 When the gas pipeline valve opening is 0°, the gas pipeline is in a healthy state and can operate normally; when the gas pipeline valve opening is 15-30°, the gas pipeline is in a slight leakage state, and the gas pipeline emits a slight leakage sound; when the gas pipeline valve opening is 30-45°, the gas pipeline is in a moderate leakage state, and the gas pipeline emits an obvious leakage sound; when the gas pipeline valve opening is 45-60°, the gas pipeline is in a severe leakage state, and the gas pipeline emits a loud leakage sound.

[0041] The gas pipeline leakage acoustic emission signal is feature extracted, including time domain and frequency domain signals. The time domain features include mean, root mean square, kurtosis, energy, amplitude, rise time, skewness, correlation coefficient, corner rate and count; the frequency domain features include signal frequency, spectrum peak, peak slope and coherence.

[0042] like Figure 2Using principal component analysis, the six most relevant signals are analyzed, including acoustic emission energy (AEEnergy), root mean square value (Root Mean Square, RMS), acoustic emission mean value (AE Mean Value), kurtosis, rise time (AE Rise Time), and count (Ring Count).

[0043] Counting refers to the number of times a signal exceeds a set threshold. After the acoustic emission signal is collected, the signal is compared with the preset threshold based on the voltage changes recorded continuously over time, thereby calculating the number of times the threshold is exceeded.

[0044] like Figure 4 , step 3, processing the collected one-dimensional acoustic signal, and then reconstructing it into a two-dimensional reconstructed grayscale image;

[0045] Two-dimensional reconstructed grayscale image is to reconstruct the one-dimensional signal data into a two-dimensional matrix, and then convert it into a grayscale image for visualization or feature extraction;

[0046] Step 4: construct a multi-level feature extraction module to extract features from the two-dimensional reconstructed grayscale image;

[0047] The multi-layer feature extraction module consists of four layers of similar-structured convolutional branches and one side convolutional branch. The first to fourth layers of branches have convolutional layers of different sizes (8×8, 7×7, 6×6, 5×5), dilated convolutions, 3×3 convolutional kernels, and 1×1 convolutional kernels; the fifth layer of side branch mechanism consists of 3×3 convolutional kernels and 1×1 convolutional kernels.

[0048] The convolution layer gradually decreases from 8×8, 7×7, 6×6, and 5×5, and its receptive field gradually decreases, which can help the model gradually go from coarse-grained global features to finer-grained local features, making the information at different levels richer; the module can extract features layer by layer at different scales, which helps to capture the continuity of global patterns and local details in the signal; the hollow convolution enhances the receptive field without increasing the amount of calculation, thereby extracting multi-scale features; the combination of 3×3 convolution kernel and 1×1 convolution kernel is designed to complement each other in the asymmetric multi-scale feature extraction module, so that the model can not only extract multi-scale features but also control the complexity of the model.

[0049] The side branches only have 3×3 and 1×1 convolutions to prevent the model from being designed too deep. The gradient may decay during propagation, resulting in information loss or training difficulties.

[0050] The five-layer branches of the multi-layer feature extraction module are summarized and output through the softmax function.

[0051] Step 5: construct a triple weighted module, and use the output features of the multi-level feature extraction module as the input of the triple weighted module;

[0052] The triple weighted module consists of three parts: channel weighted part, position weighted part, and identity mapping part;

[0053] In the channel weighting part, the input spatial dimension of the input feature map C×H×W is first adjusted to the specified output C×1×1 through adaptive average pooling;

[0054] Channel weighting can reduce the data size and subsequent calculation amount, thereby reducing the risk of overfitting; pooling window height and width k h and k w Calculated by the following formula:

[0055]

[0056] Among them, k h is the pooling window height, k w is the pooling window width, H×W is the original output size, and H′×W′ is the specified output size.

[0057] Channel weighting is achieved through channel changes. Through channel up- and channel down-operations, the input C×1×1 is transformed into a 2C×1×1 and then into a C×1×1 structure as the output; the up-channel enhances the feature expression, and the down-channel generates weights and restores the dimension, ensuring the effective flow and weighting of information.

[0058] The z of the channel weighted part is the feature vector before the channel up operation, z' is the feature vector after the channel up operation (before the channel down operation), and z" is the feature vector before the channel down operation. 1 is the convolution matrix for channel-up operation, W 2 The convolution matrix for the channel reduction operation is as follows:

[0059] z / =W 1 z(2)

[0060] z / / =W 2 z / (3)

[0061] The channel weighting part performs sigmoid activation on the output features after channel reduction, so that the weight of each channel is mapped to the range of [0,1]; the weight value represents the importance of each channel. The closer the value is to 1, the more important the feature of the channel is; the closer the value is to 0, the less important the feature of the channel is.

[0062] The position weighted part includes: global average pooling, edge filling, element averaging, and bilinear interpolation.

[0063] The position weighted part is compressed to C×1×1 through global average pooling of the spatial dimensions C×H×W;

[0064]

[0065] Among them, Z c represents the global average value of the cth channel obtained by global average pooling, x c (i, j) represents the feature value of the cth channel at position (i, j); H and W are the height and width of the feature map respectively.

[0066] The position weighted part is expanded to C×(H+pad)×(W+pad) by edge padding operation with spatial dimensions of C×1×1;

[0067] The position-weighted part is averaged over all elements, and the formula is:

[0068]

[0069] The position weighting part uses bilinear interpolation to adjust the size of the feature map. It is mainly used to scale the two-dimensional data from C×(H+pad)×(W+pad) to C×H×W. It calculates the pixel value of the target position by taking the values ​​of the four surrounding known pixels and combining their relative distances to the target position for weighted averaging. The formula is:

[0070] x'(i'j')=w 00 x(i 0 j 0 )+w 01 x(i 0 j 1 )+w 10 x(i 1 j 0 )+w 11 x(i 1 j 1 ) (6)

[0071] Where x'(i'j') is the target position (i ' ,j ' ) the four nearest known locations around; w 00 ,w 01 ,w 10 ,w 11 It is the position weight calculated based on the distance between the target position and the surrounding pixels, and the weight satisfies w 00 +w 01 +w 10 +w 11=1.

[0072] The identity mapping part effectively solves the problems of gradient vanishing, information loss, and training difficulties by simply passing the input directly to the output without any transformation or processing;

[0073] x 3 =x 3 (7)

[0074] The output y of the triple weighted module consists of the channel weighted part x 1 , position weighted part x 2 , identity mapping x 3 The three parts multiply the channel and position weights, and multiply the calculated local mean weights to obtain the weighted feature map as shown in formula (8). This step realizes the double weighting of important features in key channels and key positions. The formula is:

[0075] y=x 1 x 2 x 3 (8)

[0076] Step 6: Construct a working condition discrimination module and use the working condition discrimination model to identify the pipeline leakage type;

[0077] The working condition discrimination module is composed of a fully connected neural network, including an input layer, multiple hidden layers and an output layer; the multiple hidden layers include the convolution part and the fully connected part for feature extraction; among them, the convolution part includes: convolution layer 1, pooling layer 1, convolution layer 2, pooling layer 2, convolution layer 3, global pooling layer in sequence; the fully connected part includes fully connected layer 1 and fully connected layer 2; fully connected layer 1 is composed of 128 neurons, the activation function is ReLU, and the Dropout is set to 0.5; fully connected layer 2 is composed of 64 neurons, the activation function is ReLU, and the Dropout is set to 0.2; the output layer uses a softmax activation function containing 4 neurons to convert the output of the model into the probability of four categories; the cross entropy loss function is used to train the model, which is often used for multi-classification problems.

[0078] In this embodiment, the principal component analysis of the acoustic emission signal of the gas pipeline is performed, and the average acoustic emission energy (AE Energy), root mean square value (Root Mean Square, RMS), acoustic emission mean (AE Mean Value), kurtosis (Kurtosis), rise time (AE Rise Time), and count (Ring Count) are selected as network input neurons in the time domain signal. After analysis, the required neurons can better characterize the pipeline working conditions.

[0079] By controlling the opening of the special leakage simulation valve of the gas pipeline, as shown in the attached Figure 3 The leakage degree of the gas pipeline is defined as four states: healthy pipeline, slightly leaking pipeline, moderately leaking pipeline, and severely leaking pipeline. When the gas pipeline valve opening is 0°, the gas pipeline is in a healthy state and can operate normally; when the gas pipeline valve opening is 15-30°, the gas pipeline is in a slightly leaking state, and the gas pipeline emits a slight leakage sound; when the gas pipeline valve opening is 30-45°, the gas pipeline is in a moderate leakage state, and the gas pipeline emits an obvious leakage sound; when the gas pipeline valve opening is 45-60°, the gas pipeline is in a severely leaking state, and the gas pipeline emits a loud leakage sound.

[0080] In this embodiment, the data format of the neural network data set is:

[0081] [Input layer: [acoustic emission energy x1, root mean square value x2, acoustic emission mean x3, kurtosis x4, rise time x5, count x6];

[0082] [Output layer: healthy pipeline y1, slightly leaky pipeline y2, moderately leaky pipeline y3, severely leaky pipeline y4];

[0083] In this embodiment, 3000 sets of acoustic emission data are collected through the gas pipeline test platform to construct a data set, of which 2400 sets are normal operation acoustic signals and 600 sets are leakage signals of various degrees; 1800 sets are used for learning the neural network and 1200 sets are used for testing the neural network. The specific collection process of the gas pipeline acoustic emission signal is as follows:

[0084] Before the formal collection, the basic collection settings of the acoustic emission collection system are performed. The sampling frequency of the acoustic emission signal collector is set to 2500kHz, the gain of the preamplifier is set to 40dB, the lock time of the signal collection is set to 2000us, and the peak interval of the signal collection is set to 1000us. Before the formal data collection, the environmental noise is measured, and then 5-10dB is added to the environmental noise level as the threshold voltage value of the instrument. After the measurement, the threshold value of the acoustic emission instrument is set to 40dB.

[0085] Connect the acoustic emission sensor to the input port of the end-to-end connection through a professional transmission cable, and connect the output port to the output port. Before connecting, it is necessary to visually check whether the acoustic emission sensor used has visible damage and cracks. If there are, the sensor should be replaced in time. When using multiple acoustic emission sensors at the same time, the transmission cables need to be sorted and numbered to achieve the correspondence between the processor interface, transmission cable and sensor interface, and the cables need to be sorted to avoid the cables being tangled and difficult to distinguish during use;

[0086] Use pipe sandpaper to remove the yellow paint on the pipe surface to expose the real pipe body. Apply an even amount of vaseline to the bottom of the sensor for coupling. Use the sensor magnetic bracket to fix the sensor to the polished part of the gas pipe.

[0087] The Hsu-Nielsen signal source was used to perform the lead-breaking operation to eliminate the possibility that micro cracks and damage to the acoustic emission sensor would affect the experiment. The sensor calibration result was normal and the sensor could be used normally.

[0088] Control pipeline leakage by simulating valves to collect acoustic emission signals.

[0089] In this embodiment, x=[x 1 , x 2 , ..., x d ] T is the input of the network, y=[y 1 ,y 2 , …, y d ] T is the output of the network. The number of neurons in the input layer and the output layer depends on the number of feature categories and prediction categories of the data set, respectively.

[0090] The input one-dimensional sound signal features are processed into a two-dimensional image. After passing through a multi-level feature extraction module, various information in the image and its links are extracted to obtain more complete information; then it passes through a multiple weighting module; finally, through a convolutional neural network, the processed signal is identified and classified.

[0091] The confusion matrix is ​​a table layout that supports the visualization of the classification performance of the algorithm model and can intuitively express the target value and the predicted value. This embodiment evaluates and analyzes the gas pipeline diagnosis results. The confusion matrix is ​​shown in the attached figure. Figure 5 , given 960 pipeline signals (actual) from the healthy state, the algorithm model accurately classified 960 signals (predicted) as the healthy working state of the pipeline; for mild leakage, among 80 leakage signals (actual), the algorithm model accurately classified 77 signals (predicted) as mild leakage; for moderate leakage, among 80 leakage signals (actual), the model accurately classified 75 signals (predicted) as moderate signals, and incorrectly classified 5 signals as severe leakage signals; for severe leakage, among 80 leakage signals (actual), the model accurately classified 78 signals (predicted) as severe signals, and incorrectly classified 2 signals as moderate leakage signals.

[0092] F 1 The score is the classification performance value of the algorithm model based on precision and recall, so an evaluation of precision and recall can be performed.

[0093] This example evaluates and analyzes the gas pipeline diagnosis results. The F1 score and the total accuracy are as follows: Figure 6 The F1 scores of the method of the present invention for the healthy state, slight leakage, moderate leakage and severe leakage of the gas pipeline are 99.7%, 100%, 95.6% and 95.7% respectively, and the total accuracy is 97.8%.

[0094] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A gas pipeline leakage diagnosis method based on unbalanced data, characterized in that: The following steps are involved: Step 1: Collect acoustic emission signals under different leakage conditions and construct training and test data sets; Step 2: Perform principal component analysis on the time-frequency domain signals of the acoustic emission signals to obtain relevant signals; Step 3: reconstruct the relevant signal into a grayscale image; Step 4: Construct a five-layer multi-layer feature extraction module with four-layer structure convolution branches and side convolution branches to extract features from the grayscale image; Step 5: construct a triple weighted module based on channel weighting, position weighting and identity mapping, and input the output feature map of the multi-level feature extraction module into the triple weighted module; Step 6: Input the output feature map of the triple weighted module into the working condition discrimination module and output the pipeline leakage type.

2. The gas pipeline leakage diagnosis method based on imbalance data according to claim 1 is characterized in that: The first layer of the multi-layer feature extraction module is: 8×8 convolution, dilated convolution, 3×3 convolution and 1×1 convolution; the second layer is 7×7 convolution, dilated convolution, 3×3 convolution and 1×1 convolution; the third layer is 6×6 convolution, dilated convolution, 3×3 convolution and 1×1 convolution; the fourth layer is 5×5 convolution, dilated convolution, 3×3 convolution and 1×1 convolution; the fifth layer is 3×3 convolution and 1×1 convolution; the five-layer branch outputs the feature map through softmax.

3. The gas pipeline leakage diagnosis method based on imbalance data according to claim 1 is characterized in that: The triple weighted module includes: channel weighting adjusts the input spatial dimension of the input feature map C×H×W to C×1×1 through adaptive average pooling; then through the lifting and lowering channel operation, C×1×1 is converted to 2C×1×1 and then to C×1×1; position weighting performs global average pooling, edge filling, element averaging and bilinear interpolation on the input feature map; the output feature map of channel weighting and position weighting is multiplied with the input feature map to obtain the weighted feature map.

4. The gas pipeline leakage diagnosis method based on imbalance data according to claim 1 is characterized in that: The working condition identification module is a fully connected neural network.

5. The method for gas pipeline leakage diagnosis based on imbalance data according to claim 1, characterized in that: Leakage status includes: Healthy, Minor Leakage, Moderate Leakage, and Major Leakage.

6. The method for diagnosing gas pipeline leakage based on imbalance data according to claim 5, characterized in that: The leakage status is determined by adjusting the pipeline valve opening.

7. The gas pipeline leakage diagnosis method based on imbalance data according to claim 1 is characterized in that: Acoustic emission signals include: mean, root mean square, kurtosis, energy, amplitude, rise time, skewness, correlation coefficient, corner rate, count, frequency, spectrum peak, peak slope, and coherence.

8. The method for diagnosing gas pipeline leakage based on imbalance data according to claim 7, characterized in that: Signals of interest include: Energy, Mean, RMS, Kurtosis, Rise Time, and Count.

9. A gas pipeline leakage diagnosis system based on imbalance data, characterized in that: include: a memory for storing instructions executable by a processor; A processor, used to execute instructions to implement the gas pipeline leakage diagnosis method based on imbalance data as described in any one of claims 1-8.

10. A computer readable medium storing computer program code, characterized in that: The computer program code, when executed by a processor, implements the gas pipeline leakage diagnosis method based on imbalance data as described in any one of claims 1 to 8.

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