Abstract

Newly developed techniques for intelligent sensor systems make it possible to register the mechanical wear-out of parts, such as band saws, ball screws and gearbox reducers, by collecting working signals from them, such as vibrations and preload pressure and temperature changes. To build an accurate wear model, we need to log as many real signals as possible from numerous parts in machine tools. This raises a substantial problem: How can we collect a large number of real signals from the parts installed in many machine tools — which could be located anywhere in the world — and aggregate data to use in constructing a wearing model, as well as enabling remote systems analysis and send warnings if the parts are worn? In this study, based on our previous work, we design a special embedded system to realize a cloud-based service that logs mechanical wear-out of parts. Both short and long range wireless communications are tested to evaluate its performance. The proposed system can be used to collect operating signals regarding mechanical wear-out of parts and can allow manufacturers to track state of wear and send warnings to tool owners before wear-out.

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