LAUSR

Despite the fact that the revolution of Industry 4.0 has started almost a decade ago, there are still many yesteryear’s manufacturing machines that are still currently in operation in many small and medium enterprises (SME) factories. These legacy manufacturing machines are built without computing power and Internet connectivity. Therefore, the process of gathering operational information of such systems is often done manually. This article aims to automatically track these machines’ operation status via the vibration produced by these machines, by using a retrofit Internet-of-Things (IoT) approach that attaches wireless vibration sensors onto legacy manufacturing machines to capture the vibration of the machines. One of the challenges of the proposed retrofit approach is to interpret the meaning of the vibration without any prior knowledge of the machine’s vibration and also without the privilege to interrupt the manufacturing process to produce data sets with labels. Although there are many existing works that capture and analyze vibration, they very often only focus on fault diagnosis and prognosis. Also, many of these vibration analysis techniques are not parameter free; i.e., parameters need to be fine-tuned according to the data. The contribution of this article is the proposal of a parameter-free vibration analysis technique to cluster and classify the type of vibrations produced by a machine. Experiments, which were carried out in a limestone processing factory on real industrial machineries, show that the proposed technique is able to track the operation status of a 3-speed industrial exhaust fan with an average accuracy of 98.6% (worst case 95.5%) and standard uncertainty of 1.06%.