A dataset of excitation signals from crankshaft and camshaft sensors for engineering vehicle EECU

<p indent="0mm">The Engine Electronic Control Unit (EECU) serves as a vital component in engineering vehicles, with its performance directly determining the overall reliability of these vehicles. The recognition of excitation signals is a key technology for EECU testing and plays a significant role in ensuring the performance and reliability of the EECU. However, the lack of publicly available datasets in the field of EECU excitation signal detection has hindered the progress of research on EECU excitation signal detection in applying deep learning to engineering vehicles. To address this gap, we constructed a dataset of excitation signals from crankshaft and camshaft sensors in engineering vehicle EECUs. This dataset encompasses signal acquisition, data preprocessing, multi-label annotation, and experimental validation. With a size of approximately 1.41 GB, the dataset contains 9,600 data entries, covering nine types of EECUs and 16 excitation signal features. To ensure data quality, all entries were manually screened multi-label annotated, guaranteeing accuracy and consistency. In addition, through rigorous experimental evaluation, the reliability and validity of the dataset were verified, providing robust data support for deep learning-based multi-label EECU excitation signal detection.