Kube-IPM: A Kubernetes-Native Platform for Industrial Process Monitoring With Heterogeneous Sensor Data and Delay-Sensitive Prediction

Large-scale monitoring represents a critical frontier in industrial IoT research, yet real-time tracking of heterogeneous machinery with prognostic capabilities remains a persistent challenge. Unresolved complexities include divergent feature dimensions across machinery, nonlinear interdependencies in multivariate data streams, and dynamic temporal behavior. To overcome these limitations, we introduce Kube-IPM, a Kubernetes-native platform designed for large-scale industrial process monitoring. First, we propose the attention-based padding (ABP) method that generates meaningful feature extensions instead of dummy values, preserving data integrity. Then we apply a temporal attention layer within an encoder–decoder architecture addressing time delays and variable coupling. Finally, we establish a Kubernetes-orchestrated cloud-edge deployment enabling scalable real-time monitoring. Taking the injection molding barrel heating process as an example, we validate our approach using a comprehensive dataset collected from two injection molding machines, one experimental platform, and two simulation models, featuring variable sensor configurations (4–6). We conduct comparisons with state-of-the-art predictive and padding methods. Experiments validate model’s performance, yielding MAE of $0.13{\pm }0.003$ (single-step) and $0.26{\pm }0.009$ (multistep). It exceeds benchmarks by 38.1% and 38.0% against top predictive models, with 18.8% and 7.1% improvements over advanced padding techniques. Attention weight analysis validates that the temporal attention module effectively identifies temporal dependencies, enhancing interpretability in forecasting. Furthermore, Kube-IPM maintains 12 concurrent replicas within sampling intervals. Operational costs average 65 per machine, with marginal scaling expenses of 55 per unit, ensuring cost-efficiency for scalability. By integrating cloud-edge and attention-driven prediction technologies, this study constructs an intelligent manufacturing framework with both academic frontier and engineering implementation. Code availability: https://github.com/MangoLab-ZJU/ABP-TA-LSTM.git.