# Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

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## Abstract

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## 1. Introduction

- A statistical method together with a simple frequency response-based approach is proposed for the feature extraction method, using a simple function, such as minimum, maximum, and mean.
- We investigate the effectiveness of a simple linguistic rule-based fuzzy logic technique as a classification model for the fault diagnosis.

## 2. Related Works

## 3. The Proposed Approach

## 4. Case Study

- Assuming R1 = R2 = R, C1 = C2 = C = 10 nf.
- The cutoff frequency is selected to be 1 khz.
- Based on the formula (2πf) = 1/(R.C), the resistor value is calculated as 16 kΩ.
- To guarantee the Butterworth filter response, the gain of the filter is selected to be 1.586; therefore, based on the formula 1.586 = R3/R4 + 1, if the value of R3 = 10 kΩ, then R4 is equal to 5.86 kΩ.

## 5. Evaluation and Results

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**The proposed approach for analog electronic circuit fault detection using a fuzzy logic classifier.

**Figure 5.**The statistical analysis of the circuit frequency responses. (

**a**) Minimum values of each fault. (

**b**) Maximum values of each fault. (

**c**) Mean values of each fault.

**Figure 6.**The membership function for input-1 to the fuzzy classifier based on the mean values of frequency responses.

**Figure 7.**The membership function for input-2 to the fuzzy classifier based on the max values of frequency responses.

**Figure 8.**The membership function for input-3 to the fuzzy classifier based on the min values of frequency responses.

Fault Class | Mean (db) | Max (db) | Min (db) |
---|---|---|---|

Normal | −15.84 | 3.95 | −63.2 |

R1 short | −2.6387 | 3.95 | −26 |

R2 open | −74.33 | −54.9 | −112 |

R2 short | −2.6387 | 3.95 | −26 |

R3 open | −15.84 | 0.00024 | −63.1 |

R3 short | 5.91314 | 30.2 | −50.8 |

R4 open | 5.91314 | 47.6 | −50.8 |

R4 short | −15.84 | 0.00023 | −63.2 |

C1 open | −2.6387 | 3.95 | −42.2 |

C1 short | −100.49 | −54.9 | −135 |

C2 open | −2.6387 | 3.95 | −32.8 |

C2 short | −125.63 | −54.9 | −207 |

Rule | Mean | Max | Min | Fault Class |
---|---|---|---|---|

1 | H | M | ML | Normal |

2 | H | M | HHH | R1 short |

3 | ML | LL | L | R2 open |

4 | H | M | HHH | R2 short |

5 | MH | L | ML | R3 open |

6 | HH | H | MH | R3 short |

7 | HH | HH | MH | R4 open |

8 | MH | L | ML | R4 short |

9 | H | M | H | C1 open |

10 | L | LL | LL | C1 short |

11 | H | L | HH | C2 open |

12 | LL | LL | LLL | C2 short |

Actual Fault Class | |||
---|---|---|---|

Positive (P) | Negative (N) | ||

Predicted Fault Class | Positive (P) | True Positive (TP) | False Positive (FP) |

Negative (N) | False Negative (FN) | True Negative (TN) |

Fault Class | Precision | Recall | F-Score |
---|---|---|---|

Normal | 0.99 | 1.00 | 0.99 |

R1 short | 0.97 | 1.00 | 0.98 |

R2 open | 0.99 | 1.00 | 0.99 |

R2 short | 0.98 | 0.98 | 0.98 |

R3 open | 0.99 | 0.97 | 0.98 |

R3 short | 0.99 | 0.96 | 0.98 |

R4 open | 0.96 | 0.98 | 0.97 |

R4 short | 0.99 | 0.98 | 0.99 |

C1 open | 0.98 | 1.00 | 0.99 |

C1 short | 0.99 | 0.96 | 0.98 |

C2 open | 0.97 | 0.98 | 0.97 |

C2 short | 0.99 | 0.98 | 0.99 |

Average F-score | 0.98 |

Work | Approach | Feature Extraction Method | Classifier | Accuracy |
---|---|---|---|---|

[10] | Output Time-Domain Response | Discrete Volterra Series | Nearest Neighbor Algorithm. | 89.4% |

[11] | Circuit Time Domain Response | Transit Response Analysis | SVM | 90% |

[12] | Output Time-Domain Response | Autoencoder | Neural Network | 90% |

[13] | Output Time-Domain Response | Wavelet Packet Decomposition | Clone Selection Algorithm. | 99% |

[15] | Output Current Time-Domain Response | Wavelet Features Extracted | Deep Neural Network | 96% |

[16] | Frequency Response | Frequency Domain Analysis, Wavelet Analysis | Optimized SVM | 99.3% |

[17] | Frequency Response | Wavelet Analysis | Optimized SVM | 99% |

[18] | Frequency Response | Particle Swarm Optimization | SVM | 99% |

Proposed work | Frequency Response | Statistical Analysis | Fuzzy Logic Classifeir | 98% |

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**MDPI and ACS Style**

Nasser, A.R.; Azar, A.T.; Humaidi, A.J.; Al-Mhdawi, A.K.; Ibraheem, I.K.
Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier. *Electronics* **2021**, *10*, 2888.
https://doi.org/10.3390/electronics10232888

**AMA Style**

Nasser AR, Azar AT, Humaidi AJ, Al-Mhdawi AK, Ibraheem IK.
Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier. *Electronics*. 2021; 10(23):2888.
https://doi.org/10.3390/electronics10232888

**Chicago/Turabian Style**

Nasser, Ahmed R., Ahmad Taher Azar, Amjad J. Humaidi, Ammar K. Al-Mhdawi, and Ibraheem Kasim Ibraheem.
2021. "Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier" *Electronics* 10, no. 23: 2888.
https://doi.org/10.3390/electronics10232888