Abstract
The exploitation of big volumes of data in Industry 4.0 and the increasing development of cognitive systems strongly facilitate the realm of predictive maintenance for real-time decisions and early fault detection in manufacturing and production. Cognitive factories of Industry 4.0 aim to be flexible, adaptive, and reliable, in order to derive an efficient production scheme, handle unforeseen conditions, predict failures, and aid the decision makers. The nature of the data streams available in industrial sites and the lack of annotated reference data or expert labels create the challenge to design augmented and combined data analytics solutions. This paper introduces a cognitive analytics, self- and autonomous-learned system bearing predictive maintenance solutions for Industry 4.0. A complete methodology for real-time anomaly detection on industrial data and its application on injection molding machines are presented in this study. Ensemble prediction models are implemented on the top of supervised and unsupervised learners and build a compound prediction model of historical data utilizing different algorithms’ outputs to a common consensus. The generated models are deployed on a real-time monitoring system, detecting faults in real-time incoming data streams. The key strength of the proposed system is the cognitive mechanism which encompasses a real-time self-retraining functionality based on a novel double-oriented evaluation objective, a data-driven and a model-based one. The presented application aims to support maintenance activities from injection molding machines’ operators and demonstrate the advances that can be offered by exploiting artificial intelligence capabilities in Industry 4.0.
Highlights
Nowadays, the continuous accelerating pace of data creation and gathering from a wide range of sources such as sensors, posts to social media sites, transaction records, traffic data, pictures and videos, health data, mobile devices, and users’ activities led to significant changes in data analytics solutions by boosting machine learning (ML) and artificial intelligence (AI) methodologies to a wide range of domains (Salamanis et al, 2016; Vatrapu et al, 2016; Galetsi et al, 2020)
The adoption of state-of-the-art algorithms and cutting-edge technologies in the years of Industry 4.0 enables the automation of processes and the creation of novel predictive maintenance solutions based on predictive and prescriptive analytics (Rojko, 2017)
The current study introduces a predictive solution based on the application of cognitive analytics in feature parameters coming in real time from injection molding machines by using IDS connectors (Otto et al, 2019; Otto and Jarke, 2019)
Summary
The continuous accelerating pace of data creation and gathering from a wide range of sources such as sensors, posts to social media sites, transaction records, traffic data, pictures and videos, health data, mobile devices, and users’ activities led to significant changes in data analytics solutions by boosting machine learning (ML) and artificial intelligence (AI) methodologies to a wide range of domains (Salamanis et al, 2016; Vatrapu et al, 2016; Galetsi et al, 2020). The application of human-like intelligence in the form of cognitive analytics in manufacturing domain is still in initial stages. Cognitive applications that can get smarter and more effective over time by learning from their interaction with data and by evaluating their own performance indicators in terms of precision, is still an ongoing activity. To this aim, the work presented in this study introduces a cognitive framework that exploits the capabilities of retraining mechanisms by continuous learning. Its application for predictive maintenance services in injection molding machines of a large electronics manufacturer’s shop floor demonstrates the advantages of this cognitive solution in terms of predictions’ accuracy
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