Deep digital twin-powered large vision-language model for multi-scenario industrial fault diagnosis

Data-driven deep learning techniques have been widely adopted in industrial health monitoring and maintenance. Nevertheless, conventional diagnostic approaches often suffer from scenario-specific limitations and inadequate intelligent capabilities. Recently developed large-scale vision-language models (LVLMs) provide an advanced human–machine interaction paradigm for intelligent fault diagnosis. This study extends the application of LVLMs to industrial fault diagnosis by proposing the fault diagnosis general language model (FDGLM) framework, which enables intelligent diagnostics across multiple operating conditions and datasets. First, leveraging advanced digital twin technology, high-quality vibration samples are generated to support model training. These samples are processed using the short-time fourier transform (STFT) to generate enhanced time–frequency spectrograms, which address the large-scale data demands of FDGLM training. Subsequently, the pre-trained visual model is fine-tuned using the low-rank adaptation strategy, incorporating both cross-entropy loss and circle loss to jointly improve discriminative capability and robustness in recognizing time–frequency characteristics of industrial faults. Next, the language model is fine-tuned using domain-specific textual instructions to achieve deep alignment between visual and textual modalities, enabling causal fault analysis and maintenance decision-making. Finally, cross-condition and cross-dataset experiments are conducted on four distinct datasets. The results show diagnostic accuracies of 0.995 under identical conditions, 0.890 across varying conditions, and 0.947 in cross-dataset scenarios, while also demonstrating professional-level conversational diagnostic capabilities. The experimental results confirm that FDGLM delivers reliable diagnostics suitable for industrial applications, with minimal fine-tuning overhead and superior performance compared to existing frameworks. The proposed framework represents a next-generation solution for industrial equipment health management.