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With the new generation Industry 4.0 coming, as well as globalization and outsourcing, products are fabricated by different parties in the distributed manufacturing network and enterprises face the challenge of consistent planning of semi-finished product in each manufacturing process in different geographical locations. The purpose of this paper is to propose a real-time operation planning system in the distributed manufacturing network to intelligently control/plan the manufacturing networks.

The feature of the proposed system is to model and simulate large distributed manufacturing networks to streamline the mechanical and production engineering processes with radio frequency identification (RFID) technology, which can keep track of process variants. To deal with concurrency and synchronization, the hierarchical timed colored Petri net (HTCPN) formalism for modeling is selected in this study. This method can help to model graphically and test the discrete events of concurrent operations. Fuzzy inference system can help for knowledge representation, so as to provide knowledge-based decision assistance in distributed manufacturing environment.

In this proposed system, there are two main sub-systems: one is the real-time modeling system, and the other one is intelligent operation planning system. These two systems are not parallel in the whole systems while the intelligent operation planning system should be embedded in any stage of the real-time modeling system as needed. That means real time modeling system provides the holistic structure of the studied distributed manufacturing system and realize real-time data transfer and information exchange. At the same time the embedded intelligent operation planning system fulfill operation plan function.

This new intelligent real-time operation system realizes real-time modeling with RFID-based HTCPN and smart fuzzy engine to fulfill intelligent operation planning which is highly desirable in the environment of Industry 4.0. The new intelligent manufacturing architecture will highly reduce the traditional planning workload and improve the planning results without manual error interference. The new system has been applied in a practical case to demonstrate its feasibility.

Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing.

The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index.

The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application.

Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available.

The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics.

The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.