Books Trimmer Industrial Machine Knives Diagnosis: A Condition-Based Maintenance Strategy Through Vibration Monitoring via Novelty Detection

In recent years, Artificial Intelligence (AI) is ever more exploited in all the scientific and industrial fields and is allowing significant developments in mechanical engineering too. An emblematic contribution was given in terms of safety and reliability since Machine Learning (ML) techniques permitted the monitoring and the prediction of the state of health of machinery, allowing the adoption of predictive maintenance strategies. In fact, data-driven models — based on acquisitions — attract considerable interest both thanks to its theoretical and application development. The evolution of diagnostic techniques is oriented towards Condition-Based Maintenance (CBM) strategies, thus allowing improvements in terms of safety enhancement, cost reduction and increased performances. This paper proposes the development and implementation of a diagnostic/prognostic tool applied to an automated books trimmer industrial machine, implementing condition monitoring by means of accelerometers which can be integrated into a Supervisory Control And Data Acquisition (SCADA) system. Given its use, the core components of this production line are three knives, subjected to significant impulsive forces. Therefore, the target of the work is to infer the wear of these three knives, as they are critical elements of the machinery and have a high impact on the quality of the final product. The project was carried out in collaboration with Tecnau — an industry-leading company — which made it possible to conduct experimentation and data acquisition on their machinery. An appropriate Design Of Experiments (DOE) and the use of inferential statistical techniques — such as the ANalysis Of VAriance (ANOVA) and the identification of significant effects — applied to the multivariate dataset allowed recognizing the most relevant features for Novelty Detection (ND). Both the Linear Discriminant Analysis (LDA) and the k-Nearest Neighbors (kNN) method permitted to correctly distinguish the patterns representing the health conditions of the machinery, classifying the data in the reduced multidimensional space according to the final product quality. The results obtained in terms of accuracy are very positive and promising. This means that the developed method is able to successfully identify the state of health of the blade in spite of varying functioning parameters (book thickness and size, paper type and characteristics) and operating conditions. The algorithm speed and its integration into the industrial line make a real-time condition-based maintenance strategy possible. This diagnostic method is suitable for applications oriented to the paradigm of Industry 4.0 and the digitalization of the industrial sector, which can be integrated with the Internet of Things (IoT) and cloud systems.