Development of soft sensors based on neural networks for detection of anomaly working condition in automated machinery

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.