Combining chronicle mining and semantics for predictive maintenance in manufacturing processes

Abstract

Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.

Semantic Asset Administration Shells in Industry 4.0: A Survey

“…A case study on a conditional maintenance task of bearings in rotating machinery was performed to evaluate the proposed ontology. This ontology was extended by the same authors in their recent work [27]. The extended new ontology was named Manufacturing Predictive Maintenance Ontology (MPMO).…”

“…[25] Process modelling PSL -Use PSL to enable the exchange of process information. [26], [27] Smart manufacturing Ontology, logic rules -Enable ontology-based predictive maintenance. [28] Smart manufacturing Ontology, JADE framework -Handle heterogeneous manufacturing data.…”

Semantic Data Mining in Ubiquitous Sensing: A Survey

“…Similar approaches for application in the Cyber–Physical system were also provided in [ 128 , 129 , 130 ]. In [ 131 , 132 ], the authors provide a method that aims to bring more semantics to clusters discovered by automated methods in an Industry 4.0 setting. Such semantic information can then be represented, for example in the form of rules, and can be used to extend the knowledge about the machinery states in the Cyber–Physical setting.…”

SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0

“…Their ontology contains three sub-modules: Manufacturing, Context, and Condition Monitoring, which are used within a cyber-physical system to enable a case study to model real-time predictive maintenance. The same authors developed a new ontology named the Manufacturing Predictive Maintenance Ontology (MPMO) in [19] which uses Semantic Web Rule Language (SWRL) rules to enable ontology reasoning. Using a real-world data set, this ontology is able to detect and predict possible anomalies within an Industry 4.0 manufacturing process.…”