# A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Experiments

## 3. Location of the Fiber Breakage by the Triangulation Methodology

**Figure 3.**Peak detection of the acoustical emission collected by Sensor 1 (

**blue**) and Sensor 2 (

**green**) to obtain the experimental propagation velocity in the composite material.

**Figure 4.**Pre-processing of the signal. Wave front collected by Sensors C (

**blue**), B (

**green**) and A (

**red**).

_{EB}is the distance between E and B, v is the propagation velocity of the wave (obtained experimentally) and t

_{CB}is the time delay between the excitation of Sensors C and B.

_{FA}in Equation (2).

_{CA}is the time delay between the excitation of Sensor C and Sensor A. Figure 8 shows the scheme of the triangulation approach, the delay being represented by a circle.

## 4. Triangulation Equations System

- x
_{c}: x-coordinate at the top of the triangle. - y
_{c}: y-coordinate at the top of the triangle. - x
_{a}: x-coordinate at the left lower corner of the triangle. - y
_{a}: y-coordinate at the left lower corner of the triangle. - x
_{b}: x-coordinate at the right lower corner of the triangle. - y
_{b}: y-coordinate at the right lower corner of the triangle. - r
_{a}: radius of the circle originated from A (delay of Sensor A). - r
_{b}: radius of the circle originated from B (delay of Sensor B).

_{1}, x

_{2}, x

_{3}, x

_{4}, x

_{5}, x

_{6}and x

_{7}, being:

- x
_{1}and x_{2}the coordinates of the emission Source D. - x
_{3}and x_{4}the coordinates of the tangency of Point F. - x
_{5}and x_{6}the coordinates of the tangency of Point E. - x
_{7}is the radius of the circumference with the center D.

## 5. Experimental Procedure and Results

**Table 1.**First case study: detection time; delay with C; delay; theoretical distance; experimental distance.

Sensors | Detection Time (Samples) | Delay with C (Samples) | Delay (s) | Theoretical Distance (m) | Experimental Distance (m) |
---|---|---|---|---|---|

C | 1152 | - | - | - | - |

B | 1381 | 229 | 1.15 × 10^{−4} | 0.30 | 0.30 |

A | 1528 | 376 | 1.88 × 10^{−4} | 0.49 | 0.49 |

Locations | x-Coordinate (m) | y-Coordinate (m) | Radius (m) |
---|---|---|---|

A | 0 | 0 | 0.49 |

B | 0.8 | 0 | 0.30 |

C | 0.4 | 0.69 | - |

1 | 0.55 | 0.495 | - |

**Table 3.**Second case study: detection time; delay with C; delay; theoretical distance; experimental distance.

Sensors | Detection Time (Samples) | Delay with C (Samples) | Delay (s) | Theoretical Distance (m) | Experimental Distance (m) |
---|---|---|---|---|---|

C | 912 | - | - | - | - |

B | 1063 | 151 | 7.55 × 10^{−5} | 0.20 | 0.20 |

A | 1296 | 384 | 1.92 × 10^{−4} | 0.50 | 0.50 |

Locations | x-Coordinate (m) | y-Coordinate (m) | Radius (m) |
---|---|---|---|

A | 0 | 0 | 0.50 |

B | 0.8 | 0 | 0.20 |

C | 0.4 | 0.69 | - |

2 | 0.65 | 0.495 | - |

**Table 5.**Third case study: detection time; delay with C; delay; theoretical distance; experimental distance.

Sensors | Detection Time (Samples) | Delay with C (Samples) | Delay (s) | Theoretical Distance (m) | Experimental Distance (m) |
---|---|---|---|---|---|

C | 962 | - | - | - | - |

B | 1298 | 336 | 1.68 × 10^{−4} | 0.43 | 0.43 |

A | 1087 | 125 | 6.25 × 10^{−5} | 0.17 | 0.16 |

Locations | x-Coordinate (m) | y-Coordinate (m) | Radius (m) |
---|---|---|---|

A | 0 | 0 | 0.16 |

B | 0.8 | 0 | 0.43 |

C | 0.4 | 0.69 | - |

3 | 0.2 | 0.445 | - |

**Table 7.**Fourth case study: detection time; delay with C; delay; theoretical distance; experimental distance.

Sensors | Detection Time (Samples) | Delay with C (Samples) | Delay (s) | Theoretical Distance (m) | Experimental Distance (m) |
---|---|---|---|---|---|

C | 1155 | - | - | - | - |

B | 1650 | 495 | 2.48 × 10^{−4} | 0.64 | 0.64 |

A | 1385 | 230 | 1.15 × 10^{−4} | 0.29 | 0.30 |

Locations | x-Coordinate (m) | y-Coordinate (m) | Radius (m) |
---|---|---|---|

A | 0 | 0 | 0.30 |

B | 0.8 | 0 | 0.64 |

C | 0.4 | 0.69 | / |

4 | 0.05 | 0.645 | / |

#### 5.1. Case Study 1

- -
- Coordinate x: 0.55.
- -
- Coordinate y: 0.495.

#### 5.2. Case Study 2

- -
- Coordinate x: 0.65.
- -
- Coordinate y: 0.495.

#### 5.3. Case Study 3

- -
- Coordinate x: 0.20.
- -
- Coordinate y: 0.445.

#### 5.4. Case Study 4

- -
- Coordinate x: 0.05.
- -
- Coordinate y: 0.645.

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Dai, D.; He, Q. Structure damage localization with ultrasonic guided waves based on a time–frequency method. Signal Process.
**2014**, 96, 21–28. [Google Scholar] [CrossRef] - Spinato, F.; Tavner, P.J.; van Bussel, G.; Koutoulakos, E. Reliability of wind turbine subassemblies. IET Renew. Power Gen.
**2009**, 3, 387–401. [Google Scholar] [CrossRef] - Ajayi, O.O.; Fagbenle, R.O.; Katende, J.; Ndambuki, J.M.; Omole, D.O.; Badejo, A.A. Wind energy study and energy cost of wind electricity generation in nigeria: Past and recent results and a case study for south west nigeria. Energies
**2014**, 7, 8508–8534. [Google Scholar] [CrossRef] - Marugán, A.P.; Márquez, F.P.G. A novel approach to diagnostic and prognostic evaluations applied to railways: A real case study. Proc. Inst. Mech. Eng. F J. Rail Rapid Transit
**2015**. [Google Scholar] [CrossRef] - Chen, X.; Zhao, W.; Zhao, X.L.; Xu, J.Z. Failure test and finite element simulation of a large wind turbine composite blade under static loading. Energies
**2014**, 7, 2274–2297. [Google Scholar] [CrossRef] - Márquez, F.G.; Roberts, C.; Tobias, A.M. Railway point mechanisms: Condition monitoring and fault detection. Proc. Inst. Mech. Eng. F J. Rail Rapid Transit
**2010**, 224, 35–44. [Google Scholar] [CrossRef] - Pliego Marugán, A.; García Márquez, F.P.; Lorente, J. Decision making process via binary decision diagram. Int. J. Manag. Sci. Eng. Manag.
**2015**, 10, 3–8. [Google Scholar] [CrossRef] - Marquez, F.P.G. An approach to remote condition monitoring systems management. In Proceedings of the Institution of Engineering and Technology International Conference on Railway Condition Monitoring, Birmingham, UK, 29–30 Novemmber 2006; pp. 156–160.
- Light-Marquez, A.; Sobin, A.; Park, G.; Farinholt, K. Structural damage identification in wind turbine blades using piezoelectric active sensing. In Structural Dynamics and Renewable Energy; Springer: New York, NY, USA, 2011; pp. 55–65. [Google Scholar]
- García, F.P.; Pinar, J.M.; Papaelias, M.; Ruiz de la Hermosa, R. Wind turbines maintenance management based on FTA and BDD. Renew. Energy Power Qual. J.
**2012**. Available online: http://icrepq.com/icrepq'12/699-garcia.pdf (accessed on 7 January 2016). [Google Scholar] - Pedregal, D.J.; García, F.P.; Roberts, C. An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions. Ann. Oper. Res.
**2009**, 166, 109–124. [Google Scholar] [CrossRef] - Yang, H.-H.; Huang, M.-L.; Yang, S.-W. Integrating auto-associative neural networks with hotelling T
^{2}control charts for wind turbine fault detection. Energies**2015**, 8, 12100–12115. [Google Scholar] [CrossRef] - Chen, X.; Qin, Z.W.; Zhao, X.L.; Xu, J.Z. Structural performance of a glass/polyester composite wind turbine blade with flatback and thick airfoils. In Proceedings of the American Society of Mechanical Engineers (ASME) 2014 International Mechanical Engineering Congress and Exposition, Montreal, QC, Canada, 14–20 November 2014.
- De la Hermosa González, R.R.; Márquez, F.P.G.; Dimlaye, V. Maintenance management of wind turbines structures via mfcs and wavelet transforms. Renew. Sustain. Energy Rev.
**2015**, 48, 472–482. [Google Scholar] [CrossRef] - Márquez, F.P.G.; Pedregal, D.J.; Roberts, C. New methods for the condition monitoring of level crossings. Int. J. Syst. Sci.
**2015**, 46, 878–884. [Google Scholar] [CrossRef] - García, F.P.; Pedregal, D.J.; Roberts, C. Time series methods applied to failure prediction and detection. Reliab. Eng. Syst. Saf.
**2010**, 95, 698–703. [Google Scholar] [CrossRef] - García Márquez, F.P.; García-Pardo, I.P. Principal component analysis applied to filtered signals for maintenance management. Qual. Reliab. Eng. Int.
**2010**, 26, 523–527. [Google Scholar] [CrossRef] - Márquez, F.P.G.; Muñoz, J.M.C. A pattern recognition and data analysis method for maintenance management. Int. J. Syst. Sci.
**2012**, 43, 1014–1028. [Google Scholar] [CrossRef] - Michaels, J.E. Detection, localization and characterization of damage in plates with an in situ array of spatially distributed ultrasonic sensors. Smart Mater. Struct.
**2008**, 17, 035035. [Google Scholar] [CrossRef] - Chen, H.; Yan, Y.; Chen, W.; Jiang, J.; Yu, L.; Wu, Z. Early damage detection in composite wingbox structures using hilbert-huang transform and genetic algorithm. Struct. Health Monit.
**2007**, 6, 281–297. [Google Scholar] [CrossRef] - Eftekharnejad, B.; Carrasco, M.; Charnley, B.; Mba, D. The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing. Mech. Syst. Signal Process.
**2011**, 25, 266–284. [Google Scholar] [CrossRef][Green Version] - Gómez, C.Q.; Villegas, M.A.; García, F.P.; Pedregal, D.J. Big data and web intelligence for condition monitoring: A case study on wind turbines. In Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence; Information Science Reference, IGI Global: Hershey, PA, USA, 2015. [Google Scholar]
- Bohse, J. Acoustic emission characteristics of micro-failure processes in polymer blends and composites. Compos. Sci. Technol.
**2000**, 60, 1213–1226. [Google Scholar] [CrossRef] - Márquez, F.P.G.; Pérez, J.M.P.; Marugán, A.P.; Papaelias, M. Identification of critical components of wind turbines using FTA over the time. Renew. Energy
**2015**, 87, 869–883. [Google Scholar] [CrossRef] - Gorman, M.R. Plate wave acoustic emission. J. Acoust. Soc. Am.
**1991**, 90, 358–364. [Google Scholar] [CrossRef] - Ruiz de la Hermosa, R.; García Márquez, F.P.; Dimlaye, V.; Ruiz-Hernández, D. Pattern recognition by wavelet transforms using macro fibre composites transducers. Mechan. Syst. Signal Process.
**2014**, 48, 339–350. [Google Scholar] [CrossRef] - Betz, D.C.; Staszewski, W.J.; Thursby, G.; Culshaw, B. Structural damage identification using multifunctional bragg grating sensors: II. Damage detection results and analysis. Smart Mater. Struct.
**2006**, 15, 1313–1322. [Google Scholar] [CrossRef] - Coverley, P.; Staszewski, W. Impact damage location in composite structures using optimized sensor triangulation procedure. Smart Mater. Struct.
**2003**, 12, 795–803. [Google Scholar] [CrossRef] - Gómez, C.Q.; Ruiz de la Hermosa, R.; Trapero, J.R.; Garcia, F.P. A novel approach to fault detection and diagnosis on wind turbines. Glob. Nest J.
**2014**, 16, 1029–1037. [Google Scholar]

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**MDPI and ACS Style**

Gómez Muñoz, C.Q.; García Márquez, F.P. A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines. *Energies* **2016**, *9*, 40.
https://doi.org/10.3390/en9010040

**AMA Style**

Gómez Muñoz CQ, García Márquez FP. A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines. *Energies*. 2016; 9(1):40.
https://doi.org/10.3390/en9010040

**Chicago/Turabian Style**

Gómez Muñoz, Carlos Quiterio, and Fausto Pedro García Márquez. 2016. "A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines" *Energies* 9, no. 1: 40.
https://doi.org/10.3390/en9010040