Machine Fault Diagnosis Method Using Lightweight 1-D Separable Convolution and WSNs With Sensor Computing
Liqun Hou, Lan Liu, Guopeng Mao
Abstract
Compared with a wired machine fault diagnosis system, a wireless one based on WSNs has many inherent merits, like low cost and ease of installation. However, the limited bandwidth and battery energy of WSNs will impede the high-speed data collection and transmission required in the machine fault diagnosis system. To address this challenge, this paper proposes a novel machine fault diagnosis method based on separable convolution and WSNs with sensor computing. Firstly, a lightweight one-dimensional separable convolution network fault diagnosis model is designed and embedded on the WSNs sensor node. Machine fault diagnosis is then completed on the sensor node and only the diagnosis result is transmitted in the WSNs to reduce transmission data. A set of experiments have been conducted on the experimental setup to evaluate the proposed method. The results show that the accuracy of the proposed method can reach 98.3%, the payload transmission data of the WSN decrease from 2048 bytes to 2 bytes, while the node’s energy consumption saves about 15%. Compared with traditional 1D CNN and 1D ResNet, the proposed method is more suitable for the application on resource-constrained WSNs nodes.