如何在工业物联网中通过能量获取获取机器诊断

如何在工业物联网中通过能量获取获取机器诊断

 

　　来自工业设备的诊断数据是物联网的关键资源。远程监控设备的状态使操作员能够识别有问题的机器。这些数据甚至可以预测哪些系统是失败的，并取代它们作为日常维护的一部分，而不是灾难性的和昂贵的故障。

 

　　与物联网，所有这些数据可以在云中进行分析，提供警报和仪表板，让监测从世界任何地方。然而，有一个关键的挑战，这个模型，这实际上是获取数据。虽然现代系统可能有内置的诊断，这些可能无法连接到云，和旧设备可能是极其有限的监测电子产品。这可能是由于缺乏传感器和通信链路和可用的电源。

 

　　

 

　　Energy harvesting is providing a key capability for monitoring and diagnostics in the industrial Internet of Things. Being able to power a wireless sensor node from energy in the environment allows these nodes to be attached to equipment at the point at which the operator can get the best value from the data.

 

　　A variety of energy harvesting sources can be used to acquire this key diagnostic data. One particularly elegant approach is to use the vibration of industrial equipment to both power the wireless node and act as the sensor. A piezoelectric sensor can be tuned to the resonant frequency of the vibrations from the equipment and this can be used to power the wireless sensor node. A sophisticated power manager that is optimized for energy harvesting， such as Linear Technologies’ LTC3588， can then capture and modify the current from the piezoelectric crystal to power the wireless transceiver. Evaluation boards from SparkFun Electronics （shown in Figure 1） provide the links into the LTC3588 to connect to the power source. Four output voltages， 1.8 V， 2.5 V， 3.3 V and 3.6 V， are pin selectable from the device with up to 100 mA of continuous output current; however， the output capacitor may be sized to service a higher output current burst. An input protective shunt set at 20 V enables greater energy storage for a given amount of input capacitance.