Conquering the Challenges of Data Preparation for Predictive Maintenance

Machine learning (ML) has made it possible for technologists to do amazing things with data. Its arrival coincides with the evolution of networked manufacturing systems driven by IoT, also known as industrial IoT (IIoT), which has led to an exponential growth in the data available for statistical modeling. Predictive maintenance (PdM) applications aim to apply ML on IIoT datasets in order to reduce occupational hazards, machine downtime, and other costs by detecting when machines exhibit characteristics associated with past failures. Implementing PdM involves a process that starts with data preparation and ends with applied ML. Practitioners know well that the bulk of the effort required for effective ML deals with data preparation, and yet, those challenges continue to be underrepresented in ML literature, where authors tend to demonstrate concepts with contrived datasets. Connect, manage, and observe microservices-based applications with security-focused Istio and Red Hat OpenShift.