In the context of Industry 4.0, Predictive Maintenance becomes one of the main challenges for manufacturers. The monitoring of machinery health status results in huge cost savings. For this reason, the industry of automated machinery is moving in this direction, by acquiring large volumes of data, even if without full awareness of which physical variables are important to predict the status of a machine nor, consequently, which sensors to use and where to place them. This paper presents a general approach for the selection of sensors arrangement for the development of a condition monitoring system. The algorithm is based on multibody simulation tool and gives guidelines about the physical quantities to monitor and the parameters to extract. A machine learning model is then trained to demonstrate the ability of the obtained setup in identifying possible faults. The main benefit of this work regards the generality of the approach: it can be applied to different application cases (not only automated machineries), with the only constraint of developing a validated multibody model of the system.
In modern manufacturing industries, quality control systems are crucial components that are rising attention in production environments; companies are looking for new and innovative ways to identify and minimize the quantity of non-compliant products. Intelligent quality control is particularly important when evaluating the outcome of a production line is a complex task (for example when a visual inspection is not sufficient). The first step for building a smart process control system is the identification of all the process variables that are related to the final condition of a product. If key-variables are not directly accessible in real-time, their effect can be derived by means of sensor measurements, but, in this case, a learning model able to put in relation the available information to the inaccessible variables is needed. For all these reasons, in the last couples of decades the building of reliable and robust soft sensors gained a certain relevance in the academic world. In this research an automated rotating machinery is considered. The misalignment condition between two functional parts is the inaccessible process variable, whereas the signal of an accelerometer mounted on the machinery is available for a real time measurement. Changings in rotational speed, according to the production rate required, generate variations in acceleration’s amplitude and cycles’ length. A model based on neural networks is built to detect non-compliant products, while handling different operative conditions.
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