Integrating semantics for diagnosis of manufacturing systems

Abstract

Trends in novel manufacturing systems lead to an increased level of data availability and smart usage of these data. Nowadays, many approaches are available to use the data, but because of an increased flexibility of the systems the interaction between machines and humans has become a challenge. Humans have to browse through a huge amount of data, need knowledge about the machine and underlying algorithms to interpret the results; they cannot use their known terms for communication, we call it the conceptual gap. The user should be enabled to communicate with the machine on a more abstract level and in a more natural way. Therefore, a natural language layer is introduced to provide users with a familiar interaction interface. Underlying layers contain knowledge about the domain, the machines and how data can be accessed and processed. This enables users' questions such as “Are there any anomalies in the system?” to be answered. Answers are provided in natural language and evaluated with a test set of 204 questions

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

“…Much research effort is put into semantic description of data to make it machinereadable and especially -understandable. Efforts are going in different directions using linked data approaches [38,39], description of devices like the Industry 4.0 reference architecture (RAMI 4.0) and similar device description approaches [40,41], and various other ways to describe sensors and connected machines [42][43][44]. The most promising seems to use semantics already designed in OPC UA.…”

XAI-KG: knowledge graph to support XAI and decision-making in manufacturing

“…Though many sources of data exist, conversational interfaces provide a flexible medium to gather locally observed collective knowledge [13,2]. Though approaches were developed to query existing knowledge in manufacturing systems [4], we found no literature using such interfaces to gather data in a manufacturing setting. Such an interface can be useful to assess the quality of forecast explanations [6], extended to decision-making options, and help enhance their quality over time.…”

“…We implemented the Knowledge Graph using Neo4j 3 . We developed an ontology with general concepts that can be reused in any use case, and an extended version with concepts related to the demand forecasting use case 4 .…”

Ontologies for Industry 4.0

“…Ontologies for Industry 4.0 5 (Bunte et al, 2016), product control (Bunte et al, 2016), safety control (Akbari et al, 2010), and security inspection (Mozzaquatro et al, 2016).…”

“…On the other hand, production services (Wally et al, 2017) involve abstractions of manufacturing processes (Brodsky et al, 2016;Tang et al, 2018), such as production management (Yusupova et al), product compliance (Disi & Zualkernan, 2009), resource reconfiguration (Wan et al, 2018b), decision support (Arena et al, 2017), and intelligence-based automatization of chain processes (Muller et al, 2018), such as assembly (Merdan et al, 2008;Cecil et al, 2018) and/or diassembly (Koppensteiner et al, 2011), packaging (Wan et al, 2019), shipping (Phutthisathian et al, 2013) as well as system diagnosis Ontologies for Industry 4.0 5 (Bunte et al, 2016), product control (Bunte et al, 2016), safety control (Akbari et al, 2010), and security inspection (Mozzaquatro et al, 2016).…”