An engineering device or system is designed to retain its functioning conditions for the time it is needed. Unfortunately, failures happen and mark the state in which a device stops functioning to a satisfactory level and, thus, is considered malfunctioning. The study of malfunction and its prevention is a central topic in engineering and its analysis poses challenges relevant to applied ontology as well. In this work, we focus on the intersection between the conceptual and ontological understanding of causation and malfunction by studying existing works in these areas. Aiming to compare these approaches from a unified viewpoint, we introduce a causation-based analysis method to take on terminological and conceptual challenges. The goal is to develop a formal taxonomy of malfunctions which can also be used to compare the other approaches in the literature. Our work pays attention to real case scenarios paving the way to the ontological understanding and modeling of failures in practice. Furthermore, it helps exploiting the potentialities of failure analysis and, in particular, of root cause analysis.

The representation of manufacturing resources plays a fundamental role in engineering modeling scenarios. These are characterized in a number of ways by taking into account their physical properties, capabilities, roles, etc. In this context, notions like capability, process, and functionality are used in different manners, hence it is not clear how different approaches can interoperate. The aim of this work is to propose an ontology for manufacturing that represents assets involved in manufacturing operations, their characteristics and relations, as defined in a list of requirements. This contribution stems from the existing literature and, in particular, integrates recent works related to the modeling of manufacturing resources, engineering functions, and capabilities. The ontology takes advantage of DOLCE as foundational ontological framework. The relevant axioms are presented and commented on with respect to the identified requirements.