Introduction
Identifying circuit modules remotely through the network and replacing invalid ones in time is a crucial issue in logistics. To maintain a high-level of logistics support, large-scale network systems, such as rapid transit systems, telecommunication networks or power systems, should be properly maintained. Specifically, some sub-systems or modules are often very far away from the repair station and the system maintenance is usually very costly. In view of this, the development of remote association repair technology (RART) is in high demand for logistics to reduce repair costs and time. The concept of remote maintenance has been developed for decades, however, in the early period remote maintenance technology was centered on internet technology. In recent years, the applications of remote maintenance technology have drawn much attention with the development of wireless transmission technology. Specifically, in manufacturing [1], control and robot systems [2–4], weapon systems [5] and logistics applications [6], remote maintenance technology plays an important role. Nevertheless, most research seems to focus on the diagnosis of the equipment, but it is generally not an easy task to automatically perform fault detection of invalid modules. In this paper, we have developed a Logistical Remote Association Repair Framework (LRARF) to aid repairmen in efficiently and effectively maintaining the operation of systems. LRARF, as shown in Figure 1, was established for aiding in faulty module detection and maintenance by the integration of QR-code technology, image identification, wireless transmission and intelligent data mining technologies. The architecture of LRARF includes four parts: smart mobile phones, DBMS, a MSC and wireless networks. The transmission of LRARF is performed through High-Speed Downlink Packet Access (HSDPA) or WiFi networks. In this framework, invalid modules’ images can be sent back to the DBMS and the MSC through the APP. The experimental results reveal that the images of invalid modules can be sent back to the DBMS and the MSC through the APP and the image recognition algorithm is capable of identifying the invalid module. The corresponding maintenance manual for an invalid module is then sent via e-mail to a smart phone by maintenance personnel. In addition, voice and the live video can be recorded synchronously on the MSC for later use.
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