Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913562282164224 |
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| author | de Souza, Douglas Baptista Leao, Bruno Paes |
| author_facet | de Souza, Douglas Baptista Leao, Bruno Paes |
| contents | This work presents a novel data augmentation solution for non-stationary multivariate time series and its application to failure prognostics. The method extends previous work from the authors which is based on time-varying autoregressive processes. It can be employed to extract key information from a limited number of samples and generate new synthetic samples in a way that potentially improves the performance of PHM solutions. This is especially valuable in situations of data scarcity which are very usual in PHM, especially for failure prognostics. The proposed approach is tested based on the CMAPSS dataset, commonly employed for prognostics experiments and benchmarks. An AutoML approach from PHM literature is employed for automating the design of the prognostics solution. The empirical evaluation provides evidence that the proposed method can substantially improve the performance of PHM solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16419 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics de Souza, Douglas Baptista Leao, Bruno Paes Machine Learning Statistics Theory Methodology This work presents a novel data augmentation solution for non-stationary multivariate time series and its application to failure prognostics. The method extends previous work from the authors which is based on time-varying autoregressive processes. It can be employed to extract key information from a limited number of samples and generate new synthetic samples in a way that potentially improves the performance of PHM solutions. This is especially valuable in situations of data scarcity which are very usual in PHM, especially for failure prognostics. The proposed approach is tested based on the CMAPSS dataset, commonly employed for prognostics experiments and benchmarks. An AutoML approach from PHM literature is employed for automating the design of the prognostics solution. The empirical evaluation provides evidence that the proposed method can substantially improve the performance of PHM solutions. |
| title | Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics |
| topic | Machine Learning Statistics Theory Methodology |
| url | https://arxiv.org/abs/2410.16419 |