The KBC Asset Monitoring Application is a low-cost, subscription-based, cloud service for anomaly event detection for class B industrial rotating equipment assets not already under continuous monitoring.
KBC cloud-based monitoring service works with the Yokogawa Sushi Sensor family of industrial IoT vibration and temperature sensors to monitor rotating equipment.
The Asset Monitoring Application assists operators and engineers in improving the monitoring of assets previously only managed by operator rounds, using only a web browser to securely access that information. Operators are able to detect both event anomalies, and yet-to-occur anomalies, to proactively limit unexpected downtime events.
The Asset Monitoring Application follows an ISA-106 model to store information about physical assets from the base deployment of sensors through to the customer meta data information which defines the highest level of context data. Get the advantage of a structured, predictable data model that can commonly be applied across all industrial facilities and associated assets.
The Asset Monitoring Application empowers registered users to subscribe to the assets of their choice for notifications using either email or SMS text messages. Whenever the asset-designated limits are triggered, subscribed users will be updated using SMS Text or email messaging.
Multiple methods are available for administrative users to set the operating envelope surrounding the measured vibration and temperature parameters reporting from the Sushi Sensors. Settings include industry standard ISO 10816-1 vibration limits, past history analytics-based dynamic R24 method and the ability for experienced operators to custom set their own values.
The Asset Monitoring Application is able to provide peer equipment groups for comparative analysis and personalized charting of similar variables across equipment and even across facilities.
Export any chart image in PNG or SVG format, or decide to export the data points included in your chart as a Microsoft Excel document.
Built-in artificial intelligence helps predict abnormal status using AI Health Scoring against an AI model built by the system across up to 10 days of historical data from each sensor.
Introductory articles, getting started instructions, and frequently asked questions are all accessible from the standard user interface which makes things easy for the first-time user.
