<p>Available deep learning models have shortcomings in processing some industrial time datasets, as these models often ignore the importance of various stages in industrial time-series data and fail to capture the complex relations between multiple series from different sensors. This paper introduces MMITNet, a novel Multi-Stage Multi-Sensor Industrial Time-series Network, designed to emphasize the importance of various stages within industrial time-series data and the fusion of multi-sensor data. In our study, we utilize time-series embedding techniques, incorporating stages-based segmentation and BiLinear Self-attention Embedding, to effectively highlight pivotal stages within time-series data. This embedding methodology employs a parallel structure, which accounts for both inter-stage relationships within a time-series and the dynamics across various stages. Subsequently, the Multi-Sensor Fusion Network leverages a graph attention mechanism to integrate data from multiple sensors, capturing non-linear correlations among features. Our experiments demonstrate that our model attains state-of-the-art performance on an authentic industrial spot-welding time-series dataset and exhibits a good performance across several baseline datasets. Further investigation reveals that MMIPNet consistently achieves promising results when handling time series characterized by distinct stage changes and fluctuations.</p>