如何在工業物聯網中通過能量獲取獲取機器診斷

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　　來自工業設備的診斷數據是物聯網的關鍵資源。遠程監控設備的狀態使操作員能夠識別有問題的機器。這些數據甚至可以預測哪些系統是失敗的，并取代它們作為日常維護的一部分，而不是災難性的和昂貴的故障。

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　　與物聯網，所有這些數據可以在云中進行分析，提供警報和儀表板，讓監測從世界任何地方。然而，有一個關鍵的挑戰，這個模型，這實際上是獲取數據。雖然現代系統可能有內置的診斷，這些可能無法連接到云，和舊設備可能是極其有限的監測電子產品。這可能是由于缺乏傳感器和通信鏈路和可用的電源。

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　　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.

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　　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.

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