Applying AI-Accelerated Condition Monitoring in Hazardous Environments
AI-accelerated condition monitoring follows the data. Where sensors can extract machine-health data and transmit it through a digital data stream, Viking Analytics’ MultiViz platform can deliver insights. Thanks to Wilcoxon’s IEPE-to-digital converters, this is now possible with analog vibration sensors that are already certified and installed in hazardous locations.
Conversion is only the first step. The advantages of remote monitoring are multiplied ten-fold by automated behavior-based analytics.
This article is the second part in a series on taking AI-enabled vibration monitoring into hazardous areas. It examines how Viking Analytics’ platform applies behavior-based AI, automatic prioritization, smart alarms, and automatic mode identification once this data becomes available.
If you missed the first part on why AI-enhanced vibration monitoring rarely reaches hazardous areas, you can read it here.
There is a common problem reliability teams face when scaling remote condition monitoring: traditional, threshold-based workflows break when faced with the complexity and volume of data coming from sensors spread throughout a plant, monitoring a variety of equipment in variable conditions. Hundreds of sensors produce hundreds of alarms. The overwhelming majority of these alarms are false, but the only way to confirm that is to manually check.
This is inefficient in any scenario, and especially so in hazardous environments where safety and access concerns impede manual checks. It’s easiest to see the problem in cases in the oil and gas industry, where remote monitoring means monitoring remote locations. Scrambling technicians to far-flung sites to confirm alarms is not realistic.
Viking Analytics cuts alerts by 95%, meaning analysts can focus on the sensor data that really matters. In practice, this has turned 500 monthly alarms into 20 focused alerts, saving each reliability team member more than two hours a week.
Here’s how it works.
Viking Analytics’ AI identifies and categorizes operational changes, or modes, across time, providing insights beyond data spikes. Grounded in multi-modal signal processing including FFT, harmonics, and envelope analysis, it incorporates specific machine-learning models trained on each measuring point and its output is checked by a proprietary validation engine developed with vibration analysts to cut false alarms.
By learning what normal looks like for each individual sensor, regardless of varying operating conditions, MultiViz can detect abnormal machine behavior early and accurately, supporting timely maintenance planning.
The MultiViz platform prioritizes insights based on urgency. If there’s anything you need to know, you’ll know it the moment you open the dashboard.
AI-powered triage across factories, assets, and sensors reveals the most important information first, automatically generating a priority list focused on high-risk and deviating machines. This supports smarter monitoring with less cognitive load, faster symptom detection, better planning, and improved uptime.
MultiViz supports acting on insights rather than reacting to alerts.
Smart alarms minimize false alarms, providing the benefits of continuous monitoring without the unnecessary work of continuously checking alerts. Meanwhile, with filters that highlight critical changes, visual state-change tracking, and insights on symptoms and changes in machine behavior that can be used for root-cause analysis, each alarm maximizes actionability.
We want your team to evaluate AI-accelerated vibration monitoring using existing sensor infrastructure. That’s why Viking Analytics is offering a free one-month MultiViz trial to users of Wilcoxon vibration monitoring hardware.
MultiViz - AI powered vibration analysis-conditioning monitoring for smarter predictive maintenance.