Enhancing predictive maintenance architecture process by using ontology-enabled Case-Based Reasoning

Abstract

A common milestone in systems architecture development is the logical architecture. It provides a detailed overview of the system components and their interfaces but keeps the architecture as generic as possible, meaning that no component is bound to a specific technology. Subsequently, the architect searches for physical/informational components to fulfill the logical architecture and can apply structured creativity to look for innovative solutions. This search can turn out to be a difficult and long-lasting task depending on the system complexity. Too many options may be available to fulfill the logical system components and not always the most suitable ones are identified. This problem is for instance encountered in the design of new predictive maintenance systems, especially when selecting the components to carry out the diagnostics and prognostics. The current study proposes to support the choice of suitable components combining case-based reasoning and ontologies. A domain ontology has been developed as a terminology framework to support the case base, case structure and similarity measures for a casebased reasoning Decision Support System (DSS). The DSS uses attributes of the new problem to solve and suggests the most similar cases from past experiences. The retrieved solutions can be adapted to develop a new predictive maintenance architecture. The decision support system has been tested with data coming from proved predictive maintenance solutions documented in scientific publications.

Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud

“…Ontologies have long been used in various aspects of CBR such as the vocabulary for describing cases [1,12,16], case structure and indexing [14], semantic knowledge for similarity measurements [1,6,12,14,16], and domain knowledge for case adaptation [16]. Our focus is the use of ontologies to determine the semantic similarity during case retrieval.…”

Trends in Adopting Industry 4.0 for Asset Life Cycle Management for Sustainability: A Keyword Co-Occurrence Network Review and Analysis

“…IOT, Autonomous Robots, Cloud Computing [36][37][38] Predictive maintenance of assets using machine learning and chronicle mining. Big Data, Cloud Computing Despite the increasing attention on Industry 4.0-enabled ALCM for sustainability, research trends in applying Industry 4.0 for sustainability in ALCM are rarely explored.…”