The use of time series data is pervasive across a range of industrial sectors. They have already become a fundamental tool in many fields, such as economics, for tasks like data cleaning and fraud detection, but they are now also crucial for detecting machine malfunctions, which is the main focus of this work. The understanding of the characteristics of time series data and the types of anomalies that can be identified is a crucial step in the generation of synthetic replications, essential for the evaluation of anomaly detection methodologies in the absence of annotated data. During my research, I collaborated with AROL, a leading company in the field of capping machines, on an ongoing project.