AssetWatch at SMRP 2025

Visit us in Booth #501

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If There's Something Strange In Your Equipment, Who Ya Gonna Call?

If there’s something strange in your equipment, and your day is filled with unplanned downtime, who ya gonna call?

AssetWatch is your all-in-one “fault-fighting” team, ready to tackle stressed equipment, unplanned downtime, and overworked crews. We bundle wireless sensors, a secure communication network, powerful software, and the expertise of CAT III+ condition monitoring pros—all for one low monthly subscription.

Onboarding? We make it supernatural-ly easy. Our team installs your hardware and network in just 1–2 days. No capex investment, no IT headaches, no need to tap into your Wi-Fi.

Our platform blends vibration, temperature, and oil analysis into a single crystal-clear view of asset health. With advanced AI and seasoned reliability experts, we zero in on the biggest risks and give you prescriptive steps to bust problems before they break you.

The payoff: downtime gets busted, maintenance costs evaporate, and your operation runs safer, smoother, and more sustainably.

When unexpected equipment issues strike, we’re the ones to call. Our all-in-one predictive maintenance solution helps you stay ahead of failures and turn costly downtime into reclaimed time.

Visit booth #401 to see the tech in action, get a 1:1 demo, and enter to win a prize you choose. Then pick up the Costbusters Hotline for your shot at winning the key to uninterrupted uptime—which unlocks a special bonus prize!

Predict Equipment Failures to Reduce Unplanned Downtime

Teaching Machines to Listen: AI-Driven Anomaly Detection for Vibration Monitoring

Traditional vibration condition monitoring relies heavily on manual analysis and fixed thresholds, which can lead to missed early warning signs or an overload of false alarms. AI-powered anomaly detection offers a new approach that continuously learns from vibration data, adapts to changes in machine behavior, and surfaces only the most meaningful deviations.

This session will explore how machine learning algorithms are being applied to vibration monitoring to enhance early fault detection and streamline maintenance workflows. We’ll examine how these models ingest vibration data from sensors, build baselines for normal machine behavior, and detect deviations that indicate emerging mechanical issues such as bearing wear, looseness, or imbalance. As more data is collected across different operating conditions such as, load changes, speed variation, environmental shifts, the algorithms evolve to become more accurate and resilient over time.

Speaking Engagement