Detecting faults in real-time is an important aspect of supervision systems in industrial environments. Being able to detect, isolate and diagnose a fault, enables advance asset management, in particular predictive maintenance, which greatly increases efficiency and productivity. In this work a real-time fault detection platform is designed and implemented, following an Industrial Internet reference model. Fault detection is based on statistical data-driven methods. A preliminary application to an industrial motor is presented as case study.