# Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

## Abstract

**:**

**V**or PCA of matrix

**V**or histogram of matrix

**V**. Three different cases of thermal images were considered: healthy angle grinder, angle grinder with 1 blocked air inlet, angle grinder with 2 blocked air inlets. The classification of feature vectors was carried out using two classifiers: Support Vector Machine and Nearest Neighbor. Total recognition efficiency for 3 classes (TR

_{AG}) was in the range of 98.5–100%. The presented technique is efficient for fault diagnosis of electrical devices and electric power tools.

## 1. Introduction

**V**or PCA of matrix

**V**or histogram of matrix

**V**. Support Vector Machine and Nearest Neighbor classified feature vectors. The paper has the following sections: (1) Introduction, (2) Theoretical background (3) Processing and recognition of the thermal image (4) Analyzed states of the angle grinder (5) Results of the analysis of thermal images (6) Conclusions.

## 2. Theoretical Background

## 3. Processing and Recognition of the Thermal Image

## 4. Analyzed States of the Angle Grinder

#### 4.1. BCAoMID−F (Binarized Common Areas of Maximum Image Differences−Fusion)

- Convert all thermal images into grayscale images (256 colors, value of the pixel is in the range of <0–1>).
- Compute differences between training thermal images of the angle grinder: |
**hag-ag1b|**, |**hag-ag2b|**,**|ag1b-ag2b|**, where**hag**− matrix (320 × 240) of the thermal image of the healthy angle grinder,**ag1b**−matrix (320 × 240) of the thermal image of the angle grinder with 1 blocked air inlet,**ag2b**−matrix (320 × 240) of the thermal image of the angle grinder with 2 blocked air inlets. - For computed differences |
**hag-ag1b|**, |**hag-ag2b|**,**|ag1b-ag2b|**, set the proper value of threshold of binarization (t<|**hag-ag1b|**<u, for example: t = 0.1, u = 1). - Compute binary images (binarization threshold = 0.1).
- Compute the sum of all binary images of the computed differences. It is denoted as matrix
**S**. - Compute maximum value (Max) of the computed sum
**S**. - Compute matrix
**K**=**S**/Max. - Compute binary image−matrix
**K**, where z <**K**< 1 (set 0 for values of matrix K less than z, set 1 for values of matrix K greater than z), z is set experimentally <0, 1>. For the analysis, the author set z = 0.5. Computed binary image**K**has values 0 and 1. - For each training and test thermal image denoted as matrix
**B**, compute matrix**G**=**B**+**K**. - Compute matrix (
**G**< 1.001) = 0 (set 0 for values of matrix**G**less than 1.001). - Compute matrix
**V**, where**V**=**G**− 1 - Compute three features: compute the sum of pixels of the matrix
**V**, compute a histogram of the matrix**V**, compute PCA (Principal Component Analysis) of the matrix**V**.

**hag-ag1b|**, |

**hag-ag2b|**,

**|ag1b-ag2b|**are presented (Figure 8, Figure 9 and Figure 10).

**K**for all training thermal images of the angle grinder is presented in Figure 14.

**K**. Binarization threshold was equal to 0.5 (0.5 <

**K**< 1). It is shown in Figure 15.

**V**for the healthy angle grinder was presented in Figure 16. Histograms and PCA values for the healthy angle grinder were presented in Figure 17.

**V**for the angle grinder with 1 blocked air inlet was presented in Figure 18. Histograms and PCA values for the angle grinder with 1 blocked air inlet were presented in Figure 19.

**V**for the angle grinder with 2 blocked air inlets was presented in Figure 20. Histograms and PCA values for the angle grinder with 2 blocked air inlets were presented in Figure 21.

#### 4.2. Principal Component Analysis

#### 4.3. Classification Using the Nearest Neighbor

- (1)
- load training and test feature vectors,
- (2)
- set k = 1 (k − number of nearest neighbors),
- (3)
- Compute the distance d, where d = Σ|
**a**-**b**|,**a**—test feature vector,**b**—training feature vector, - (4)
- for all computed distances d, select the nearest distance,
- (5)
- select the label of the predicted class.

#### 4.4. Support Vector Machine

## 5. Results of the Analysis of Thermal Images

_{AG}—recognition efficiency of a selected class of the angle grinder, RC

_{AG}—number of test thermal images recognized correctly, A

_{AG}—number of all test thermal images of a selected class of the angle grinder.

_{AG}was defined as (2):

_{AG1}−R

_{AG}for the healthy angle grinder, R

_{AG2}−R

_{AG}for the angle grinder with 1 blocked air inlet, R

_{AG3}−R

_{AG}for the angle grinder with 2 blocked air inlets.

_{AG}were in the range of 98.5–100%. Values of R

_{AG}were in the range of 97.7–100%. The highest values of TR

_{AG}were computed for the Nearest Neighbor classifier (Table 1, Table 2 and Table 3). The lowest values of TR

_{AG}were computed for BCAoMID−F, PCA, and SVM (Table 6). We can notice, that features were extracted properly. NN and SVM also classified data successfully.

## 6. Conclusions

_{AG}) was in the range of 98.5–100%. The presented approach was efficient for fault diagnosis of electrical devices such as power tools. The conclusions of the paper are following:

- (1)
- Thermal images should be captured at the same distance (thermal imaging camera–analyzed motor).
- (2)
- The proposed method is efficient for measurement distance equal to 0.4 m. Slight shifts of thermal imaging camera(+/−0.1 m) do not cause major changes for recognition results.
- (3)
- Recognition rate for considered states was high (TR
_{AG}is in the range of 98.5–100%). - (4)
- The same conditions and equipment should be used for measurements. The temperature of air in a room should be similar for all training and test thermal images.
- (5)
- Different types of motors can be diagnosed by the proposed method successfully.
- (6)
- The proposed method is fast. It can be implemented as a condition online monitoring system.
- (7)
- The method is non-invasive. It can be used for conditions of difficult access.
- (8)
- The proposed method uses a computer and thermal imaging camera. The cost of the experimental setup is low (about 1400–1600$).

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 8.**Difference of thermal images: |

**hag-ag1b|**, where

**hag**−matrix (320 × 240) of the thermal image of the healthy angle grinder,

**ag1b**−matrix (320 × 240) of the thermal image of the angle grinder with 1 blocked air inlet.

**Figure 9.**Difference of thermal images: |

**hag-ag2b|**, where

**hag**−matrix (320 × 240) of the thermal image of the healthy angle grinder,

**ag2b**−matrix (320 × 240) of the thermal image of the angle grinder with 2 blocked air inlets.

**Figure 10.**Difference of thermal images: |

**ag1b-ag2b|**, where

**ag1b**−matrix (320 × 240) of the thermal image of the angle grinder with 1 blocked air inlet,

**ag2b**−matrix (320 × 240) of the thermal image of the angle grinder with 2 blocked air inlets.

**Figure 16.**Values of the matrix

**V**for the thermal image of the healthy angle grinder (indicated by the green line).

**Figure 17.**(

**a**) Histogram of the matrix

**V**for the thermal image of the healthy angle grinder (

**b**) Values of PCA of the matrix

**V**for the thermal image of the healthy angle grinder.

**Figure 18.**Values of the matrix

**V**for the thermal image of the angle grinder with 1 blocked air inlet (indicated by the green line).

**Figure 19.**(

**a**) Histogram of the matrix

**V**for the thermal image of the angle grinder with 1 blocked air inlet (

**b**) Values of PCA of the matrix

**V**for the thermal image of the angle grinder with 1 blocked air inlet.

**Figure 20.**Values of the matrix

**V**for the thermal image of the angle grinder with 2 blocked air inlets (indicated by the green line).

**Figure 21.**(

**a**) Histogram of the matrix

**V**for the thermal image of the angle grinder with 2 blocked air inlets (

**b**) Values of PCA of the matrix

**V**for the thermal image of the angle grinder with 2 blocked air inlets.

State of the Angle Grinder | R_{AG} [%] |
---|---|

Healthy AG | 100 |

AG with 1 blocked air inlet | 100 |

AG with 2 blocked air inlets | 100 |

TR_{AG} [%] | |

3 states of the AG | 100 |

State of the Angle Grinder | R_{AG} [%] |
---|---|

Healthy AG | 100 |

AG with 1 blocked air inlet | 100 |

AG with 2 blocked air inlets | 100 |

TR_{AG} [%] | |

3 states of the AG | 100 |

State of the Angle Grinder | R_{AG} [%] |
---|---|

Healthy AG | 100 |

AG with 1 blocked air inlet | 100 |

AG with 2 blocked air inlets | 100 |

TR_{AG} [%] | |

3 states of the AG | 100 |

State of the Angle Grinder | R_{AG} [%] |
---|---|

Healthy AG | 100 |

AG with 1 blocked air inlet | 100 |

AG with 2 blocked air inlets | 100 |

TR_{AG} [%] | |

3 states of the AG | 100 |

State of the Angle Grinder | R_{AG} [%] |
---|---|

Healthy AG | 100 |

AG with 1 blocked air inlet | 100 |

AG with 2 blocked air inlets | 100 |

TR_{AG} [%] | |

3 states of the AG | 100 |

State of the Angle Grinder | R_{AG} [%] |
---|---|

Healthy AG | 100 |

AG with 1 blocked air inlet | 97.7 |

AG with 2 blocked air inlets | 97.7 |

TR_{AG} [%] | |

3 states of the AG | 98.5 |

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**MDPI and ACS Style**

Glowacz, A.
Ventilation Diagnosis of Angle Grinder Using Thermal Imaging. *Sensors* **2021**, *21*, 2853.
https://doi.org/10.3390/s21082853

**AMA Style**

Glowacz A.
Ventilation Diagnosis of Angle Grinder Using Thermal Imaging. *Sensors*. 2021; 21(8):2853.
https://doi.org/10.3390/s21082853

**Chicago/Turabian Style**

Glowacz, Adam.
2021. "Ventilation Diagnosis of Angle Grinder Using Thermal Imaging" *Sensors* 21, no. 8: 2853.
https://doi.org/10.3390/s21082853