The imbalanced data limit the effectiveness of mobile robot fault diagnosis, while generating pseudo multi-sensor signals of mobile robot is an effective solution. However, existing generative methods often fail to balance the differences and correlations among channels across multi-sensor signals. To address these issues, a novel multimodal knowledge and multi-channel correlation generative adversarial network (MKMCGAN) is proposed to generate high-quality fault signals. Specifically, wavelet packet decomposition (WPD) are used to extract time-frequency features for each channel, then multi generator-discriminator pair strategy (MGDS) and a time-frequency analysis knowledge module (TFKM) are designed to bring higher similarity between the generated signal and the real signal. Subsequently, we construct a sensor data association graph and design a prior knowledge correlation module (PKM), which effectively consider the impact of inter-channel correlations on generated signals. Eventually, a novel multi-channel correlation generative adversarial network is proposed to extract time-frequency features and consider inter-channel correlations, which can generate high-quality fault signals. The effectiveness of MKMCGAN is thoroughly validated on datasets collected from a real robot fault diagnosis test bench. Experimental results indicate that MKMCGAN generates higher-quality signals compared to state-of-the-art methods.

Multilingual language models (MLLMs) have demonstrated remarkable success in various cross-lingual downstream tasks, facilitating the transfer of knowledge across numerous languages, whereas this transfer is not universally effective. Our study reveals that while existing MLLMs like mBERT can capturephrase-level alignments across the language families, they struggle to effectively capturesentence-level andparagraph-level alignments. To address this limitation, we propose GranCATs, Granularity-specific Contrastive AdapTers. We collect a new dataset that observes each sample at three distinct levels of granularity and employ contrastive learning as a pre-training task to train GranCATs on this dataset. Our objective is to enhance MLLMs' adaptation to a broader range of cross-lingual tasks by equipping them with improved capabilities to capture global information at different levels of granularity. Extensive experiments show that MLLMs with GranCATs yield significant performance advancements across various language tasks with different text granularities, including entity alignment, relation extraction, sentence classification and retrieval, and question-answering. These results validate the effectiveness of our proposed GranCATs in enhancing cross-lingual alignments across various text granularities and effectively transferring this knowledge to downstream tasks.