Wireless Mesh Network for Predictive Maintenance Reference Design
Created: Mar 14, 2018
This reference design can be used to quickly establish a wireless mesh network that connects various types of sensors directly to the Internet. Inputs like RS232, RS485 or RTD are on the board to collect data for predictive maintenance from different sensors and machines. For data processing a high performance ARM Cortex M4F MCU with Floating Point Unit and DSP acceleration is on the board.
Predictive maintenance allows for the repair of machines before they break down and enables the permanent monitoring of components inside the machine using sensors that measure vibration, pressure, temperature, or humidity. Specialized maintenance software collects this sensor data, evaluates the data, and recognizes in advance when a component might be in danger of failing. For the predictive maintenance data processing, this reference design has the MSP432P401 on board, which is a high- performance Arm Cortex-M4F MCU with floating-point unit and DSP acceleration. The sensors used for the data collection are often housed in hard-to-access locations, and the sensors may not be accessible through ordinary wired connections. As the sensors usually must communicate with upstream data- gathering nodes, wireless communication is required. This reference design uses the wireless communication 6LoWPAN, which enables IPv6 over the IEEE802.15.4 standard. This standard allows for low-power sensor nodes that work in mesh architectures. With the onboard, multistandard 2.4-GHz wireless CC2650 MCU, this reference design could also support Zigbee®, Bluetooth ® low energy, or proprietary solutions.
Permanent machine monitoring traditionally uses wired sensors; however, wireless sensors provide a cost-effective, simple, and reliable way to deploy new points of measurement and control without the wiring costs. With a predictive maintenance program, maintenance activities can be scheduled, and replacement parts can be ordered only as required, which minimizes onsite inventory. The operation of equipment can be optimized, and the factory reliability can be improved.