FPGA implementation of a wireless sensor node with a built-in ADALINE neural network coprocessor for vibration analysis and fault diagnosis in machine condition monitoring

Industry is a very attractive research field for wireless sensor network (WSN) applications. This is demonstrated by the creation of a special category of these networks dedicated to industrial applications, called industrial wireless sensor networks (IWSN). The sensor node, the main component of the network, must have several characteristics, such as a very high processing speed, reliable results, communication capabilities, and reduced transmission time. In this article, we outline the results of replacing the fast Fourier transform (FFT) processing of vibrational signals with an artificial adaptive linear element (ADALINE) neural network in order to extract the harmonics of the signals and thus detect faults in rotating machines. In addition, a MicroBlaze soft-core processor and an nRF24L01+ transmitter was chosen to manage various tasks within the node and the data exchange between the nodes of the network. A Digilent Cmod A7 platform with an Artix-7 FPGA circuit from Xilinx was selected to implement the different blocks that constitute the wireless node. Practical tests showed that the choice of the ADALINE enabled us to achieve the desired results by reducing the processing time to 7.478 ms, which is a reduction of time of approximately 85% compared to results obtained in scientific research. In addition, we have reduced the number of transmitted packets to only two. These results will have a positive impact on the performance of the node, with measurements using a periodic measurement methodology showing that the lifetime of the node can reach up to 17 h. (C) 2020 Elsevier Ltd. All rights reserved.