# Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Fast Fourier Transform

#### 2.2. Wavelet Transform

#### 2.3. Statistical Features

_{i}is the acoustic emission signal.

#### 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 signalsOutput: Binary valueStep 1: Calling raw input samples (127 samples) one by one, each sample would be as follows:$\left({x}_{11}\dots {x}_{1l}\right)$$\in {M}_{n\times l}\left(R\right)$, Where ${M}_{n\times l}\left(R\right)$ is the vector of 1×12,000,000 over R. Step 2: Extracting the name of each sampleStep 3: Assigning 0 and 1 labels to each sampleIf the filename starts with bubble, thenlabel = 1 else label = 0Step 4: Splitting each sample into 120 equal size buckets, each bucket would have the following shape:$\left({x}_{11}\dots {x}_{1k}\right)$ $\in {M}_{n\times k}\left(R\right)$, Where ${M}_{n\times k}\left(R\right)$ 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 $\left(\begin{array}{ccc}{x}_{11}& \cdots & {x}_{1l}\\ \vdots & \ddots & \vdots \\ {x}_{n1}& \cdots & {x}_{nl}\end{array}\right)$ $\in {M}_{n\times l}\left(R\right)$, where ${M}_{n\times l}$ 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- 1D max pooling layer with length 2
- Flatten layer
- 2 dense layers with the capacities of 128 and 64, following with sigmoid activation function
- Adding L2 regularization with the weight-coefficient value of 0.001
- Dropout layer
- Dense layer with the capacity of 1, following with sigmoid activation function
Step 11: Compiling the model: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 predictionGenerating 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 thenthe related value shows the accuracy of fault detection, elseit 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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**Figure 1.**Flowchart of the proposed methods. 1D-CNN: 1D convolution neural network; FFT: fast Fourier transform.

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 | $\overline{x}=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$N$}\right.{\displaystyle {\displaystyle \sum}_{i=1}^{N}}{x}_{i}$ |

Standard deviation | $SD=\sqrt{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$N$}\right.{\displaystyle {\displaystyle \sum}_{n=1}^{N}}{\left({x}_{i}-\overline{x}\right)}^{2}}$ |

Kurtosis | K = $\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$N$}\right.\sum _{i=1}^{N}\frac{{\left({x}_{i}-\overline{x}\right)}^{4}}{{\sigma}^{4}}$ |

Skewness | S = $\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$N$}\right.\sum _{i=1}^{N}\frac{{\left({x}_{i}-\overline{x}\right)}^{3}}{{\sigma}^{3}}$ |

Root mean square | RMS = $\sqrt{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$N$}\right.\sum _{i=1}^{N}{x}_{i}{}^{2}}$ |

Crest factor | C = $\raisebox{1ex}{$\mathrm{max}value$}\!\left/ \!\raisebox{-1ex}{$RMS$}\right.$ |

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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**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Rahimi, 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