| dc.description.abstract | As production lines become more complicated and high production capacity is demanding, preventive even predictive maintenance strategies are becoming more and more mainstream in order to reduce maintenance costs and improve production efficiency. To achieve the prognostic needs of production equipment, we rely on the information exploration and modeling of big data. With the development trend of Industry 4.0, the integration of heterogeneous machine network and Internet of Things system architecture is gradually gaining importance. Based on the above, this study first constructs an integrated monitoring system and data streaming function. The system is equipped with multi-physics signal synchronized acquiring function to meet the needs of multi-mode information collection in the process. Incorporate Open Platform Communication Unified Architecture (OPC UA) and Firebase cloud development platform to strengthen heterogeneous networking capabilities. Develop data pre-processing module for real-time overview of data status, intelligence predict module can mount predict models from offline training to make real-time predictive warning. Afterward, data re-analysis by band saw cutting data in the steel machining center for system verification. The analysis consist of segment selection, data cleaning, screening redundant features by correlation coefficient and Variance Inflation Factor (VIF). Moreover, Apply the unsupervised learning algorithm—Self-Organizing Map (SOM) to train the prediction model, and use Minimum Quantization Error (MQE) to build up the aging and degradation assessment of saw bands. Finally, integrate the model into the integrated monitoring system to confirm system effectiveness. The result show that it is effective in providing early warning when the life of saw band #1 is 97%, the life of saw band #4 is 95.5%, the life of saw band #3 is 83.3%, and the life of saw band #5 is 97.6%. | en_US |