iSTEP, an Integrated Self-Tuning Engine for Predictive Maintenance in Industry 4.0

The recent expansion of IoT-enabled (Internet of Things) devices in manufacturing contexts and their subsequent data-driven exploitation paved the way to the advent of the Industry 4.0, promoting a full integration of IT services, smart devices, and control systems with physical objects, their electronics and sensors. The real-time transmission and analysis of collected data from factories has the potential to create manufacturing intelligence, of which predictive maintenance is an expression. Hence the need to design new approaches able to manage not only the data volume, but also the variety and velocity, extracting actual value from the humongous amounts of collected data. To this aim, we present iSTEP, an integrated Self-Tuning Engine for Predictive maintenance, based on Big Data technologies and designed for Industry 4.0 applications. The proposed approach targets some of the most common needs of manufacturing enterprises: compatibility with both the on-premises and the in-the-cloud environments, exploitation of reliable and largely supported Big Data platforms, easy deployment through containerized software modules, virtually unlimited horizontal scalability, fault-tolerant self-reconfiguration, flexible yet friendly streaming-KPI computations, and above all, the integrated provisioning of self-tuning machine learning techniques for predictive maintenance. The current implementation of iSTEP exploits a distributed architecture based on Apache Kafka, Spark Streaming, MLlib, and Cassandra; iSTEP provides (i) a specific feature engineering block aimed at automatically extracting metrics from the production monitoring time series, which improves the predictive performance by 77% on average, and (ii) a self-tuning approach that dynamically selects the best prediction algorithm, which improves the predictive performance up to 60%. The iSTEP engine provides transparent predictive models, able to provide end users with insights into the knowledge learned, and it has been experimentally evaluated on a public unbalanced failure dataset, whose extensive results are discussed in the paper.