Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal
Abstract
:1. Introduction
2. Methodology
2.1. Fast Fourier Transform
2.2. Wavelet Transform
2.3. Statistical Features
2.4. Convolution Neural Network
3. Experimental Procedure
3.1. Experimental Set-Up
3.2. Dataset and Algorithm
Algorithm 1 Pseudo-code of FFT_1D CNN Algorithm |
Input: Raw healthy and faulty signals Output: Binary value Step 1: Calling raw input samples (127 samples) one by one, each sample would be as follows: , Where is the vector of 1×12,000,000 over R. Step 2: Extracting the name of each sample Step 3: Assigning 0 and 1 labels to each sample If the filename starts with bubble, then label = 1 else label = 0 Step 4: Splitting each sample into 120 equal size buckets, each bucket would have the following shape: , Where is the vector of 1 × 100,000 over R. Each bucket is obtained during batch processing ((i−1) × Δ + 1) → (I × Δ), where Δ is the sampling rate 100 kHz and i = 1, …, 120, corresponds to the number of batches at time T= 1 sec. Step 5: Calculating FFT of each bucket, the collected FFT values of all buckets (127 × 120) can be arranged in a the matrix as , where is the vector of 15,240 × 1000 over R. Step 6: Making equal the number of healthy and faulty samples so that the FFT matrix size would be 4840 × 1000. Step 7: Specifying training, validation, and test datasets. Each dataset would be as follows: The training dataset is a vector of 2172 × 1000, and the validation dataset would be a vector of 1070 × 1000 Moreover, the test dataset is 1598 × 1000. Step 8: Expanding the shape of training, validation, and test datasets so they would be as follows: The training dataset is a vector of 2172 × 1000 × 1. The validation dataset would be a vector of 1070 × 1000 × 1 Moreover, the test dataset would be 1598 × 1000 × 1. Step 9: Creating a sequential model Step 10: Defining the proper layers
Specifying binary cross-entropy as loss function and Adam as an optimizer, finally calling compile ( ) function on the model Step 12: Fitting the model: A sample of data should be trained by calling the fit ( ) function on the model. Step 13: Making a prediction Generating predictions on new data by calling evaluate ( ) function. The output of this step would be two values, 0 and 1, with their accuracy as follows: If index = 1 then the related value shows the accuracy of fault detection, else it shows healthy accuracy. End |
4. Experimental Results and Analysis
- Adding dropout layers;
- Trying different architectures, e.g., adding or removing layers;
- Adding L1 and/or L2 regularization;
- Trying different hyper-parameters (such as the number of units per layer or the optimizer’s learning rate) to find the optimal configuration;
- Optionally, iterating on feature engineering, e.g., adding new features or removing features that were not informative.
5. Conclusions
- Three types of feature extraction methods, i.e., FFT transform, wavelet, and time-domain features of the signal, were implemented to differentiate healthy and faulty states.
- 1D-CNN-based architecture was used to determine leakage along with the three different feature extraction methodologies.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Signal Analysis | Feature | Sensor Used | Process |
---|---|---|---|
Time-domain | Root mean square (RMS) | Acoustic emission (AE) and power | Monitoring grinding operation [21] |
Skewness | Vibration | Condition monitoring for milling [22] | |
Kurtosis | AE | Tool flank wear recognition [23] | |
Frequency- domain | FFT | Vibration | Fault diagnosis of the rotating machine [24] |
FFT | Vibration | Bearing fault diagnosis [25] | |
FFT | Vibration, AE, force | Tool wear monitoring [26] | |
Wavelet | Morlet wavelet | Piezoelectric sensor | Delamination detection [27] |
Daubechies-4 | Vibration | Structural damage detection [28] | |
Daubechies-4 | Power | Power quality monitoring [29] |
Features | Expression |
---|---|
Mean value | |
Standard deviation | |
Kurtosis | K = |
Skewness | S = |
Root mean square | RMS = |
Crest factor | C = |
Peak-to-peak (PPV) value | PPV = max value min value |
Label | Mean | STD | Skewness | Kurtosis | RMS | Peak-to-Peak | Crest-Factor |
---|---|---|---|---|---|---|---|
1 | −0.00303 | 0.932716 | 0.001772 | −1.55262 | 0.932721 | 3.166601 | 1.694493 |
1 | 0.001391 | 0.934791 | 0.002414 | −1.55813 | 0.934792 | 3.135015 | 1.649781 |
1 | 0.004578 | 0.942283 | −0.00174 | −1.50837 | 0.942295 | 3.411634 | 1.81576 |
1 | −0.00252 | 0.936973 | 0.005309 | −1.52431 | 0.936976 | 3.431415 | 1.81483 |
0 | 5.40 ×10−5 | 0.813413 | −0.00043 | −1.56246 | 0.813413 | 2.636973 | 1.594723 |
0 | 0.001712 | 0.813213 | 0.002127 | −1.57254 | 0.813215 | 2.619106 | 1.583342 |
0 | 0.001307 | 0.815626 | 0.001999 | −1.57703 | 0.815627 | 2.609215 | 1.582571 |
0 | 0.000724 | 0.812949 | 0.002658 | −1.57934 | 0.812949 | 2.607301 | 1.604661 |
Variables | Quantity |
---|---|
Data | 127 samples |
Number of buckets | 120 |
Each bucket | (100,000, 1) |
Full data array | (15,240, 100,000) |
Full label array | (15,240, 1) |
Train data | (2172, 1000) |
Validation data | (1070, 1000) |
Test data | (1598, 1000) |
Parameters Methods | Activation Function (Tanh) | Dropout (0.5) | Having 2 Dense Layers | Having 2 Convolution Layers | Optimizer Stochastic Gradient Descent (SGD) |
---|---|---|---|---|---|
FFT_1D-CNN | 74.53 | 81.66 | 83.85 | 83.55 | 56.45 |
Wavelet_1D-CNN | 47.68 | 47.68 | 47.68 | 47.68 | 47.68 |
Time-domain features_1D-CNN | 52.88 | 53.69 | 53.94 | 56.45 | 48.19 |
Layer | Name | Specification |
---|---|---|
1 | Convolution | 2×2×1 |
2 | Relu | N/A |
3 | Max pooling | 2×2 |
4 | Flatten | 998 |
5 | Dense | 128 |
6 | Sigmoid | N/A |
7 | Dense | 64 |
8 | Sigmoid | N/A |
9 | Dropout | 20% |
10 | Fully Connected | 1 |
11 | Sigmoid | N/A |
12 | Classification | Binary cross-entropy |
Layers | Methods | ||
---|---|---|---|
FFT | Wavelet | Time Domain | |
Shape Param | Shape Param | Shape Param | |
Conv 1D | (None, 999, 2) 6 | (None, 6, 2) 6 | (None, 999, 2) 6 |
MaxPooling1 | (None, 449, 2) 0 | (None, 2) 0 | (None, 449, 2)0 |
Flatten | (None, 998) 0 | (None, 6) 0 | (None, 998)0 |
Dense | (None, 128) 127,872 | (None, 128) 896 | (None, 128) 127,872 |
Dense | (None, 64) 8256 | (None, 64) 8256 | (None, 64) 8256 |
Dropout | (None, 64) 0 | (None, 64) 0 | (None, 64) 0 |
Output (Dense) | (None, 1) 65 | (None, 1) 65 | (None, 1) 65 |
Total Params | 136199 | 9223 | 136199 |
Methods | Accuracy (%) | ||
---|---|---|---|
Epoch = 50 | Epoch = 100 | ||
1 | FFT_1D-CNN | 84.23 | 86.42 |
2 | Wavelet_1D-CNN | 52.88 | 54.57 |
3 | Time-domain Features_1D-CNN | 54.32 | 53.94 |
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Rahimi, M.; Alghassi, A.; Ahsan, M.; Haider, J. Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal. Informatics 2020, 7, 49. https://doi.org/10.3390/informatics7040049
Rahimi M, Alghassi A, Ahsan M, Haider J. Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal. Informatics. 2020; 7(4):49. https://doi.org/10.3390/informatics7040049
Chicago/Turabian StyleRahimi, Masoumeh, Alireza Alghassi, Mominul Ahsan, and Julfikar Haider. 2020. "Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal" Informatics 7, no. 4: 49. https://doi.org/10.3390/informatics7040049