# Research on Information Fusion for Machine Potential Fault Operation and Maintenance

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Information Fusion Based on D–S Evidence

#### 3.1. D–S Evidence Theory

#### 3.2. Lack of D–S Evidence Theory

#### 3.3. Improved Evidence Theory

#### 3.3.1. Weight of Evidence Reliability Based on Entropy

#### 3.3.2. Weight of Evidence Consistency Based on Evidence Correlation Matrix

#### 3.3.3. Reference Evidence Generation

#### 3.3.4. Comprehensive Evidence Fusion Rule

## 4. Experiments

#### 4.1. Reliability Weight Calculation

#### 4.2. Consistency Weight Calculation

#### 4.3. Reference Evidence Calculation

#### 4.4. Comprehensive Evidence Fusion Generation

#### 4.5. Results Analysis

## 5. Application and Verification

#### 5.1. Problem Description

#### 5.2. Establish Problem Identification Framework

#### 5.3. Suspected Failure Probability Of Machines

#### 5.4. Comparative Analysis

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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Fusion Methods | Advantage | Disadvantage | Main Application Scenarios |
---|---|---|---|

Bayesian inferences | Simple calculation rules and fast calculation speed. | This method should be based on the accurate prior probability. | |

fuzzy reasoning | Information processing is closer to people’s thinking with strong explanatory ability. | There are a lot of subjective factors in design of reasoning rule and the standards are not unified. | |

Neural network | High data utilization and high accuracy. | Poor interpretability and high computational complexity. | |

D–S evidence | Suitable for the fusion of multi-source information with strong explanatory ability and flexible fusion mode. | The serious conflict between evidences is hard to resolve. |

Witness 1 | Witness 2 | Fusion Result | |
---|---|---|---|

A | 0.99 | 0.00 | 0.00 |

B | 0.01 | 0.01 | 1.00 |

C | 0.00 | 0.99 | 0.00 |

Witness 1 | Witness 2 | |
---|---|---|

A | 0.60 | 0.60 |

B | 0.40 | 0.20 |

C | 0.00 | 0.20 |

Witness 1 | Witness 2 | Witness 3 | Witness 4 | D–S Fusion Result | |
---|---|---|---|---|---|

A | 0.90 | 0.95 | 0.95 | 0.00 | 0.00 |

B | 0.09 | 0.04 | 0.03 | 0.03 | 0.63 |

C | 0.01 | 0.01 | 0.02 | 0.97 | 0.37 |

${\mathit{E}}_{1}$ | ${\mathit{E}}_{2}$ | ${\mathit{E}}_{3}$ | ${\mathit{E}}_{4}$ |
---|---|---|---|

0.1553 | 0.0971 | 0.1008 | 0.0585 |

${\mathit{\omega}}_{1}$ | ${\mathit{\omega}}_{2}$ | ${\mathit{\omega}}_{3}$ | ${\mathit{\omega}}_{4}$ |
---|---|---|---|

0.2076 | 0.2547 | 0.2517 | 0.2860 |

${\mathit{\omega}}_{1}$ | ${\mathit{\omega}}_{2}$ | ${\mathit{\omega}}_{3}$ | ${\mathit{\omega}}_{4}$ |
---|---|---|---|

0.2734 | 0.2538 | 0.4723 | 0.005 |

Reference Evidence | $\mathit{A}$ | $\mathit{B}$ | $\mathit{C}$ |
---|---|---|---|

${r}_{E}$ | 0.6680 | 0.0450 | 0.2870 |

${r}_{C}$ | 0.9362 | 0.0490 | 0.0148 |

$r$ | 0.9381 | 0.0469 | 0.0150 |

Evidence Fusion Result | $\mathit{A}$ | $\mathit{B}$ | $\mathit{C}$ |
---|---|---|---|

$D-S$ | 0.0000 | 0.6255 | 0.3745 |

$D-S-{r}_{E}$ | 0.6680 | 0.0450 | 0.2870 |

$D-S-{r}_{C}$ | 0.9362 | 0.0490 | 0.0148 |

$D-S-r$ | 0.9381 | 0.0469 | 0.0150 |

**Table 10.**The suspected failure probability of 10 machines derived from machine importance [49].

$\mathbf{Machine}\_$Order | Probability | $\mathbf{Machine}\_$Order | Probability |
---|---|---|---|

$1$ | 0.3309 | $6$ | 0.0376 |

$2$ | 0.3176 | $7$ | 0.0092 |

$3$ | 0.1713 | $8$ | 0.0083 |

$4$ | 0.0793 | $9$ | 0.0054 |

$5$ | 0.0376 | $10$ | 0.0027 |

**Table 11.**The suspected failure probability of machines derived from the machine alarm [49].

$\mathbf{Machine}\_$Order | Probability | $\mathbf{Machine}\_$Order | Probability |
---|---|---|---|

$1$ | 0.2547 | $6$ | 0.0217 |

$2$ | 0.1456 | $7$ | 0.0235 |

$3$ | 0.1315 | $8$ | 0.1543 |

$4$ | 0.0021 | $9$ | 0.0122 |

$5$ | 0.2111 | $10$ | 0.0433 |

**Table 12.**The suspected failure probability of machines derived from the comprehensive evidence fusion method.

$\mathbf{Machine}\_$Order | Probability | $\mathbf{Machine}\_$Order | Probability |
---|---|---|---|

$1$ | 0.2958 | $6$ | 0.0303 |

$2$ | 0.2385 | $7$ | 0.0158 |

$3$ | 0.1530 | $8$ | 0.0755 |

$4$ | 0.0438 | $9$ | 0.0085 |

$5$ | 0.1175 | $10$ | 0.0214 |

$\mathbf{Machine}\_$Order | Probability | $\mathbf{Machine}\_$Order | Probability |
---|---|---|---|

$1$ | 0.5149 | $6$ | 0.005 |

$2$ | 0.2826 | $7$ | 0.001 |

$3$ | 0.1377 | $8$ | 0.008 |

$4$ | 0.0010 | $9$ | 0.000 |

$5$ | 0.0486 | $10$ | 0.000 |

**Table 14.**Fusion result comparison with [49].

$\mathbf{Machine}\_$Order | Comprehensive Evidence Fusion | Result From [49] | Normalization Result Form [49] |
---|---|---|---|

$1$ | 0.3349 | 0.2890 | 0.3349 |

$2$ | 0.2413 | 0.2083 | 0.2413 |

$3$ | 0.1488 | 0.1285 | 0.1488 |

$4$ | 0.0332 | 0.0286 | 0.0332 |

$5$ | 0.1100 | 0.0950 | 0.1100 |

$6$ | 0.0250 | 0.0216 | 0.0250 |

$7$ | 0.0135 | 0.0116 | 0.0135 |

$8$ | 0.0674 | 0.0582 | 0.0674 |

$9$ | 0.0072 | 0.0062 | 0.0072 |

$10$ | 0.0187 | 0.0162 | 0.0187 |

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## Share and Cite

**MDPI and ACS Style**

Xu, W.; Wan, Y.; Zuo, T.-Y.; Sha, X.-M.
Research on Information Fusion for Machine Potential Fault Operation and Maintenance. *Symmetry* **2020**, *12*, 375.
https://doi.org/10.3390/sym12030375

**AMA Style**

Xu W, Wan Y, Zuo T-Y, Sha X-M.
Research on Information Fusion for Machine Potential Fault Operation and Maintenance. *Symmetry*. 2020; 12(3):375.
https://doi.org/10.3390/sym12030375

**Chicago/Turabian Style**

Xu, Wei, Yi Wan, Tian-Yu Zuo, and Xin-Mei Sha.
2020. "Research on Information Fusion for Machine Potential Fault Operation and Maintenance" *Symmetry* 12, no. 3: 375.
https://doi.org/10.3390/sym12030375