Non-rechargeable batteries remain as the main source of energy for small systems, owing to their unique advantages in energy density, safety, reliability and sustainability. Accurate prediction of the remaining useful life of the battery is not only beneficial to maintenance and production safety, but also can be regarded as a starting point for possible secondary life applications. In this study, an interactive attention sequence-to-sequence network is proposed for the remaining useful life prediction of the non-rechargeable batteries. The proposed approach can effectively extract the degenerate information of each variable-length sequence and dynamically weight the sequence features of different dimensions. For illustration, a case of primary battery dataset collected from the power supply system of 139 vibration sensors is utilized. The extensive experiments verify the effectiveness of the proposed approach.
|Title of host publication||2022 IEEE 20th International Conference on Industrial Informatics (INDIN)|
|Place of Publication||Piscataway|
|Publication status||Published - 25 Jul 2022|
|Event||2022 IEEE 20th International Conference on Industrial Informatics (INDIN) - Fully Online, Perth, Austria|
Duration: 25 Jul 2022 → 28 Jul 2022
|Conference||2022 IEEE 20th International Conference on Industrial Informatics (INDIN)|
|Period||25/07/22 → 28/07/22|