# The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

## Abstract

**:**

^{−5}. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.

## 1. Introduction

## 2. Circuit Pressure Test Problems Based on Electric Flux

**Definition**

**1.**

## 3. Neural Network Model Based on BP Algorithm

#### 3.1. Algorithm Description

#### 3.2. Diagnosis Steps of Neural Network Information Fusion Fault

## 4. Experimental Analysis

## 5. Conclusions

## Funding

## Conflicts of Interest

## References

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Parameter | Value | Description |
---|---|---|

Maximum Iterations | 10000 | Maximum iterations in neural network algorithm |

Neural network scale | 10000 | The size of neural network and the number of HSPICE simulations |

dt | 1μs | The length of each edge in the neural network and the duration of each SPICE simulation. |

Total time | 3 h | The total running time of the neural network algorithm tested each time |

α | 0.5 | Target weight of the state sequencing |

**Table 2.**The fault identification results of single fault identification of two kinds of sensors and fusion fault identification of multi-sensor.

Fault Components | Sensor and Fusion | Signal Value of the Fault | Fault Diagnosis | ||
---|---|---|---|---|---|

1 | 2 | 3 | |||

1 | Temperature | 0.5436 | 0.0782 | 0.0000 | Not sure |

Pressure | 0.4092 | 0.0743 | 0.2731 | Not sure | |

Fusion | 0.8906 | 0.1072 | 0.0048 | Component 1 fault | |

2 | Temperature | 0.0748 | 0.6161 | 0.0000 | Component 2 fault |

Pressure | 0.0022 | 0.2935 | 0.1763 | Not sure | |

Fusion | 0.0527 | 0.9462 | 0.0076 | Component 2 fault | |

3 | Temperature | 0.2435 | 0.2424 | 0.3217 | Not sure |

Pressure | 0.0038 | 0.0036 | 0.1956 | Not sure | |

Fusion | 0.0098 | 0.0441 | 0.9842 | Component 3 fault |

Parallel Times | Index | Literature [14] | Literature [15] | Parallel Compression |
---|---|---|---|---|

1 | Convergence precision | 1.265 × 10 ^{−5} | 4.286 × 10 ^{−5} | 4.149 × 10 ^{−5} |

Convergence time/μs | 5.368 | 2.418 | 0.156 | |

5 | Convergence precision | 3.359 × 10 ^{−5} | 4.173 × 10 ^{−5} | 3.942 × 10 ^{−5} |

Convergence time/μs | 26.416 | 11.598 | 0.249 | |

10 | Convergence precision | 1.287 × 10 ^{−5} | 3.946 × 10 ^{−5} | 2.928 × 10 ^{−5} |

Convergence time/μs | 55.943 | 28.418 | 0.317 | |

15 | Convergence precision | 2.649 × 10 ^{−5} | 2.851 × 10 ^{−5} | 2.516 × 10 ^{−5} |

Convergence time/μs | 78.634 | 35.76 | 0.729 |

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**MDPI and ACS Style**

Wang, N.
The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm. *Symmetry* **2020**, *12*, 458.
https://doi.org/10.3390/sym12030458

**AMA Style**

Wang N.
The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm. *Symmetry*. 2020; 12(3):458.
https://doi.org/10.3390/sym12030458

**Chicago/Turabian Style**

Wang, Nana.
2020. "The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm" *Symmetry* 12, no. 3: 458.
https://doi.org/10.3390/sym12030458