Abstract
Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many industries including manufacturing, oil and gas, chemical and process industry. In the age of Industry 4.0, where the aim is a deep degree of production automation, unprecedented amounts of data are generated by equipment and processes, and this enables adoption of Machine Learning (ML) approaches for condition monitoring. Development of such ML models is challenging. On the one hand, it requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers with asymmetric backgrounds. On the other hand, there is high variety and diversity of data relevant for condition monitoring. Both factors hampers ML modelling for condition monitoring. In this work, we address these challenges by empowering ML-based condition monitoring with semantic technologies. To this end we propose a software system SemML that allows to reuse and generalise ML pipelines for conditions monitoring by relying on semantics. In particular, SemML has several novel components and relies on ontologies and ontology templates for ML task negotiation and for data and ML feature annotation. SemML also allows to instantiate parametrised ML pipelines by semantic annotation of industrial data. With SemML, users do not need to dive into data and ML scripts when new datasets of a studied application scenario arrive. They only need to annotate data and then ML models will be constructed through the combination of semantic reasoning and ML modules. We demonstrate the benefits of SemML on a Bosch use-case of electric resistance welding with very promising results.
Highlights
Industry 4.0 [1] and technologies of the Internet of Things (IoT) [2] behind it lead to unprecedented growth of data generated in many industrial processes, such as manufacturing, oil and gas, chemical and process industries [3,4]
We developed the module of semantic Ontology Extender, which can be combined with Machine Learning (ML) module of Exploratory Data Analysis (EDA) for process and data understanding
Another reason could be that the users moved to a new template group, which increased the cognitive complexity of the task and the time spent on the task
Summary
Industry 4.0 [1] and technologies of the Internet of Things (IoT) [2] behind it lead to unprecedented growth of data generated in many industrial processes, such as manufacturing, oil and gas, chemical and process industries [3,4]. The workflow is iterative and includes several steps: data collection (Step 1), task negotiation, to define feasible and economic tasks (Step 2), data preparation, to integrate data from different conditions and production environments (Step 3), ML analysis (Step 4), result interpretation and model selection (Step 5), and model deployment in production (Step 6) Development of such ML approaches is a complex and costly process where the following three challenges are of high importance for many companies, including Bosch, since they consume more than 80% of the overall time of development [11]. Our ontology-based ML system SemML allows users to do ML based condition monitoring on a specific domain without an extensive knowledge of ML thanks to the semantic artefacts and ML pipelines offered by the system. A comparison of ML methods with extensive evaluation is presented in [34]
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