Rolling bearings are widely used in modern production equipment. Effective bearing fault diagnosis method will improve the reliability of the machinery and increase its operating efficiency. In this paper, a novel fault diagnosis method based on WSN and CNN has been proposed to fully utilize the strong fault classification capability of CNN and the inherent merits of WSNs, such as relatively low cost, convenience of installation, and ease of relocation. The feasibility and effectiveness of proposed system are evaluated using the vibration data sets of seven motor operating conditions released by the Case Western Reserve University Bearing Data Center. The experimental results show the fault diagnosis accuracy of the proposed approach can reach 97.6%.
Even though smart instruments are popular in modern industrial processes, pointer meters without computer interfaces are still widely employed in many application areas. Automatic reading recognition of pointer meters based on computer vision provides a promising solution to input the meter readings to the existing monitoring system or IoT. However, most of the current reading recognition approaches for pointer meters are based on inconvenient wired systems or expensive inspection robots. Compared with a wired system, wireless sensor networks (WSNs) are low-cost, easy to install and relocate. To leverage these advantages of WSNs and address the limitation of its radio bandwidth, this paper proposes a novel automated meter reading system for pointer meter using WSNs with on-sensor image processing, in which meter image capturing, image preprocessing, and reading recognition are completed on WSNs end node, and then only the recognition result is transmitted through WSNs. The feasibility of the proposed method is evaluated by a set of experiments on the prototype. Experimental results indicate that the presented approach can recognize the meter reading with an error of 0.3% and significantly reduce the payload transmission data in the WSNs.
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