To complete the FMS plant management solution, MCM recently developed a new machine data monitoring and predictive maintenance system, composed of different sensors and the Edge jNODE FR device on which MCE, the group’s IT division, has implemented the jFRX software. for high frequency collection and aggregation of machine data.
The product is the concrete result of a series of projects in which MCM has exploited a series of factors, such as the availability of a large amount of information present in its flexible production systems (FMS), the birth of low-cost and high-level devices services for data recording and processing (edge computing), the constant attitude of research and application of news in the world of technology and IT.
The new jNODE FR monitoring system, equipped with jFRX software, integrates the MCM offer at the machine level which has long used the consolidated jFMX supervision software for cell (plant) and workshop (shopfloor) levels.
The basic concept is to exploit the information already present in the machine and normally used for process control to enable new support services for the customer and the plant manufacturer.
The key points of the solution are:
The system collects a considerable amount of data using different components and the information that is acquired is extremely heterogeneous among them, for example data relating to positions and absorptions of the machine axes, energy consumption, vibratory phenomena both in the time and frequency domain and other sizes specific to the machine and customer.
In addition to being used to perform local control loops, this information now travels from the machines to a cloud server, acquiring additional context data at the cell and workshop level, for example, the code of the piece being worked, the tool on the spindle, the name of the machine and plant, thus creating a solid basis for the creation of new services designed to improve the management of the process and the availability of the means of production.
Specifically, a predictive maintenance system has been developed, with which each machine is systematically monitored. A first service collects the value of the signals relating to the normal operating actions of the main machine groups, such as the tool change, the pallet change, the block and outlet of the table while another service provides for the execution of test programs in the machine at intervals of preset time (fingerprint), recording the power absorbed when empty. Algorithms for comparing the data of the current operation with respect to the episodes acquired over time, provide indicators to highlight any anomalies of a component or of a machine group.
Another service under development is that relating to the monitoring of the manufacturing process. Also in this case, the collection of data concerning the use of tools in various contexts, represents a very interesting knowledge base for the user and will also serve for the development of even more advanced services for the optimization of tool management.