Seyed Hesam Hosseinizadeh Mazloumi: Edge and cloud Computing for Robust Asset Monitoring

<p dir="ltr">Maintaining industrial machinery is critical to keeping factories, power plants, and transportation systems running smoothly. When equipment unexpectedly breaks down, the financial impact is staggering. In 2020, unplanned downtime cost industries $1.4 trillion globally, representing 11% of annual turnover. Rotating machinery, such as bearings and gearboxes, forms the heart of these industries yet remains highly vulnerable to failure. While vibration monitoring systems can detect failures early, they remain out of reach for many companies due to high costs, complexity, and overwhelming data requirements.</p><p dir="ltr">This research transforms asset monitoring into an affordable and scalable solution. By introducing a self-tuning algorithm and a low-cost edge-cloud platform, this study enables remote and real-time monitoring without traditional complexity. Validated across 32 open-source failure scenarios, the system achieves 99.97% data reduction and enables monitoring from anywhere via internet. By reducing costs by up to 90%, this production-ready platform makes predictive maintenance accessible to industries worldwide.</p>