# A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data

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## Abstract

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## 1. Introduction

- A method for building a digital twin model (of a motor and gearbox circuit specifically in this study) and generating synthetic healthy and faulty data using this model is proposed.
- A predictive maintenance algorithm to estimate the current state of the machine/digital twin is presented for all types of periodic signal (vibration data specifically in this study), and steps of this algorithm are described in detail to ensure reproducibility. The proposed predictive maintenance algorithm can work with both synthetic data and suitable real-life data.
- A publicly accessible vibration dataset and digital twin model-generated synthetic data were used to test the classification accuracy of the proposed predictive maintenance algorithm, in terms of correctly classifying healthy and faulty data.

## 2. Methodology

#### 2.1. Creating the Digital Twin Model

#### 2.2. Predictive Maintenance Workflow

Algorithm 1 Synthetic Data Generation Algorithm |

Input: Description of the List of Failures |

(1) Develop Detailed Physics-Based Model of the Process |

(2) Develop and Implement Modelling Strategies of the Failures |

(3) Input Realistic Range of Variables Responsible of the Failures |

Randomly vary the Variables Responsible of the Failures |

Run Until Enough Data |

Output: Supervised Dataset for Predictive Maintenance |

#### 2.3. Testing the Predictive Maintenance Algorithm

## 3. Results and Discussion

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**A visualization for gearbox tooth faults [18].

**Figure 7.**Effect of the preprocessing step proposed above, on an example vibration signal: (

**a**) Vibration data before the proposed preprocessing step; (

**b**) Vibration data after the proposed preprocessing step.

**Figure 11.**Features vs. classes graphs for the synthetic data with ensemble bagged tree algorithm: (

**a**) Feature vs. classes graphs for mean, median, variance, peak value, skewness and kurtosis; (

**b**) Feature vs. classes graphs for RMS value, MAD, peak-to-peak value, approximate entropy, crest factor and high-frequency power; (

**c**) Feature vs. classes graphs for range of cumulative sum, correlation dimension, Lyapunov exponent, peak frequency, envelope power and spectral kurtosis.

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**MDPI and ACS Style**

Selçuk, Ş.Y.; Ünal, P.; Albayrak, Ö.; Jomâa, M. A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data. *Information* **2021**, *12*, 386.
https://doi.org/10.3390/info12100386

**AMA Style**

Selçuk ŞY, Ünal P, Albayrak Ö, Jomâa M. A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data. *Information*. 2021; 12(10):386.
https://doi.org/10.3390/info12100386

**Chicago/Turabian Style**

Selçuk, Şahan Yoruç, Perin Ünal, Özlem Albayrak, and Moez Jomâa. 2021. "A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data" *Information* 12, no. 10: 386.
https://doi.org/10.3390/info12100386