# State Evaluation of Self-Powered Wireless Sensors Based on a Fuzzy Comprehensive Evaluation Model

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## Abstract

**:**

## 1. Introduction

## 2. FE-SPS Evaluation Method

## 3. Construction of the Failure Mechanism and Index System of the Self-Powered Wireless Sensor

#### 3.1. Design of Self-Powered Wireless Sensors

#### 3.1.1. Hardware Design of Self-Powered Wireless Sensors

#### 3.1.2. Energy Management Circuit, Wireless Communication Unit, Sensor

#### 3.1.3. Construction of the Testing Platform

#### 3.2. Failure Mechanism and Key Indexes of the Vibration Energy Harvester

- 1.
- Open-circuit voltage deviation rate

- 2.
- Short-circuit current deviation rate

- 3.
- Equivalent impedance deviation rate

_{C}, and L

_{C}can be ignored. Here, the equivalent impedance deviation rate is introduced, which can be expressed by:

- 4.
- Output power stability

#### 3.2.1. Ability to Manage Circuit Failure Mechanisms and Key Indexes

- 1.
- Output voltage stability

- 2.
- Circuit output power stability

- 3.
- Energy conversion efficiency

#### 3.2.2. Key Indexes of the Wireless Communication Unit Failure Mechanism

- 1.
- Communication distance attenuation rate

- 2.
- Packet loss rate

- 3.
- Efficiency of data packets

- 4.
- Communication timeliness

#### 3.2.3. Selection of Sensor Indexes

- 1.
- Measurement accuracy

- 2.
- Measurement range

## 4. Index Weighting

#### 4.1. Index Weighting Based on AHP

#### 4.2. Index Weighting Based on the CRITIC Weight Method

- 1.
- Normalize and standardize each indicator as shown in Equations (19) and (20):

- 2.
- Calculate the comparative strength between various indicators, with the calculation process described as Equation (21):

- 3.
- Calculate the conflict of the indicators, with the calculation process described as Equation (22):

- 4.
- Calculate the total amount of information contained in a single indicator, with the calculation process described as Equation (23):

- 5.
- Normalize the total amount of information ${C}_{j}$ and obtain the objective weight values of each factor, with the calculation process described as Equation (24):$${w}_{j}=\frac{{C}_{j}}{{\displaystyle {\sum}_{j=1}^{p}{C}_{j}}}$$

#### 4.3. Index Weighting for Subjective and Objective Integration

## 5. Fuzzy Comprehensive Evaluation

- 1.
- Collection of raw parameters for evaluation indexes

- 2.
- Determine the set of factors

- 3.
- Determine the comment set

- 4.
- Determine the standard values

- 5.
- Construction of the membership matrix

- 6.
- Calculate the evaluation results

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Weight Assignment Method | Subjective/ Objective | Advantage | Disadvantage |
---|---|---|---|

Expert scoring method | Subjective | Simple and fast | Highly influenced by expert experience |

AHP | Subjective | Simple and practical, systematic, less quantitative data information required | Highly influenced by expert experience, more complex eigenvalue method |

Entropy weight method | Objective | Ability to consider uncertainty and information, does not rely on expert judgment | High vulnerability to data errors, correlations between indicators cannot be considered |

CRITIC method | Objective | It can simultaneously take into account the variability of indicators and the correlation between indicators, multiple attributes and decision scenarios can be handled | Need a lot of comparative data, professional software support may be required |

Variation coefficient method | Objective | Simple and easy to implement, each indicator can be effectively distinguished | There are certain restrictions on the selection of indicators |

Scale | Meaning |
---|---|

1 | It means that two elements are of equal importance compared to each other |

3 | It means that the former is slightly more important than the latter |

5 | It means that the former is significantly more important than the latter |

7 | It means that the former is more important than the latter |

9 | It means that the former is more important than the latter |

2, 4, 6, 8 | It means the middle value of the above neighboring judgments |

The reciprocal of 1 to 9 | It means the importance of the exchange order of two factors |

Order (n) | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|

RI | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 |

Item | Index Variability | Index Conflict | Amount of Information | Weight (%) |
---|---|---|---|---|

Open-circuit voltage deviation rate | 1.703 | 12.093 | 20.594 | 10.43 |

Short-circuit current deviation rate | 2.535 | 12.890 | 32.681 | 16.56 |

Equivalent impedance deviation rate | 0.614 | 12.257 | 7.522 | 3.81 |

Output power stability | 1.491 | 12.444 | 18.559 | 9.40 |

Output voltage stability | 1.684 | 12.083 | 20.346 | 10.31 |

Circuit output power stability | 0.701 | 11.946 | 8.370 | 4.24 |

Energy conversion efficiency | 1.432 | 12.302 | 17.621 | 8.93 |

Communication distance attenuation rate | 0.175 | 12.162 | 2.129 | 1.08 |

Packet loss rate | 0.602 | 11.657 | 7.018 | 3.56 |

Data validation | 0.694 | 12.104 | 8.396 | 4.25 |

Timeliness | 2.851 | 12.706 | 36.221 | 18.35 |

Measurement accuracy | 0.499 | 12.439 | 6.206 | 3.14 |

Measurement specifications | 0.965 | 12.147 | 11.722 | 5.94 |

Parameter Type | Parameters | Weight (%) |
---|---|---|

Energy harvester index X1 | Open-circuit voltage deviation rate X11 | 5.68 |

Short-circuit current deviation rate X12 | 8.81 | |

Equivalent resistance deviation rate X13 | 2.17 | |

Energy management circuit index X2 | Output power stabilityX14 | 6.69 |

Output voltage stability X21 | 7.52 | |

Output power stability X22 | 21.39 | |

Wireless communication index X3 | Energy conversion efficiency X23 | 9.70 |

Communication distance attenuation rate X31 | 0.86 | |

Packet loss rate X32 | 2.44 | |

Data packet efficiencyX33 | 4.12 | |

Sensor unit index X4 | Timeliness X34 | 11.09 |

Measurement accuracy X41 | 14.09 |

Evaluation Index | Parameter |
---|---|

Open-circuit voltage deviation rate | 13.82% |

Short-circuit current deviation rate | 10.40% |

Equivalent impedance deviation rate | 3.13% |

Output power stability | 8.17% |

Output voltage stability | 5.57% |

Circuit output power stability | 1.59% |

Energy conversion efficiency | 77.75% |

Communication distance attenuation rate | 0.60% |

Packet loss rate | 1.7% |

Data packet efficiency | 98% |

Timeliness | 85.26% |

Measurement accuracy | 2 °C |

Measurement accuracy | 159 °C |

Evaluation Index | V1 | V2 | V3 | V4 |
---|---|---|---|---|

X11 (%) | 11.41 | 22.83 | 34.24 | 45.65 |

X12 (%) | 17.76 | 35.52 | 53.28 | 71.04 |

X13 (%) | 10.71 | 21.43 | 32.14 | 42.85 |

X14 (%) | 18.25 | 36.5 | 54.75 | 73 |

X21 (%) | 11.36 | 22.73 | 34.09 | 45.45 |

X22 (%) | 5 | 10 | 15 | 20 |

X23 (%) | 80.35 | 67.34 | 54.85 | 42.25 |

X31 (%) | 10 | 30 | 40 | 60 |

X32 (%) | 2.5 | 5 | 7.5 | 10 |

X33 (%) | 98 | 96 | 94 | 92 |

X34 (%) | 95 | 85.55 | 76.11 | 66.66 |

X41 (°C) | 1 | 3 | 6.5 | 9 |

X42 (°C) | 160 | 150 | 140 | 130 |

Type Layer | Factor Layer | Selected Membership Model |
---|---|---|

Energy harvester | Open-circuit voltage deviation rate | Model 1 |

Short-circuit current deviation rate | Model 1 | |

Equivalent resistance deviation rate | Model 2 | |

Output power stability rate | Model 2 | |

Energy management circuit | Output voltage stability | Model 2 |

Output power stability | Model 2 | |

Energy conversion efficiency | Model 2 | |

Wireless communication unit | Communication distance attenuation rate | Model 1 |

Packet loss rate | Model 1 | |

Data packet efficiency | Model 2 | |

Timeliness | Model 2 | |

Sensing unit | Measurement accuracy | Model 2 |

Measurement range | Model 2 |

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## Share and Cite

**MDPI and ACS Style**

Xiong, S.; Li, Q.; Yang, A.; Zhu, L.; Li, P.; Xue, K.; Yang, J.
State Evaluation of Self-Powered Wireless Sensors Based on a Fuzzy Comprehensive Evaluation Model. *Sensors* **2023**, *23*, 9267.
https://doi.org/10.3390/s23229267

**AMA Style**

Xiong S, Li Q, Yang A, Zhu L, Li P, Xue K, Yang J.
State Evaluation of Self-Powered Wireless Sensors Based on a Fuzzy Comprehensive Evaluation Model. *Sensors*. 2023; 23(22):9267.
https://doi.org/10.3390/s23229267

**Chicago/Turabian Style**

Xiong, Suqin, Qiuyang Li, Aichao Yang, Liang Zhu, Peng Li, Kaiwen Xue, and Jin Yang.
2023. "State Evaluation of Self-Powered Wireless Sensors Based on a Fuzzy Comprehensive Evaluation Model" *Sensors* 23, no. 22: 9267.
https://doi.org/10.3390/s23229267