This paper presents the implementation of online condition monitoring using the Petasense ARO Industrial Internet of Things (IIoT) platform for predictive maintenance of industrial assets. We outline strategies employing IIoT analytics to predict failures, optimize maintenance schedules, and enhance operational reliability. Case studies demonstrate monitoring techniques for diverse equipment (fans, motors, pumps), including specific methodologies for challenging VFD-operated, variable-speed machinery. The study highlights the benefits of multi-parameter monitoring—integrating vibration, temperature, pressure, and magnetic flux—for a comprehensive asset health assessment superior to single-parameter methods. Presented case studies confirm reduced downtime, demonstrating the practical advantages of adopting IIoT-based, multi-parameter condition monitoring to improve industrial asset reliability and performance. This paper provides valuable insights for practitioners seeking to leverage IIoT solutions.