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How SmartOil™ Is Protecting The National Electric Grid and Natural Gas Pipeline Network

Mission critical infrastructure such as the national electric grid and natural gas pipeline network consists of thousands of engines, turbines, compressors and generators that depend on lubrication systems in order to operate reliably on a 24/7/365 basis. Maintaining these capital intensive fleets of machine systems falls on the backs of reliability engineers and capital asset managers to employ best-practices in preventative maintenance, as well as adopt the latest advancements in BIG data and predictive analytics. Intelligent machine health monitoring systems utilizing M2M and industrial internet-of-things (Industrial IoT) sensors can provide a fleet-wide perspective with huge benefits in optimizing network operations.

Two common problems associated with on-site machine health monitoring are: 1) operator error and 2) equipment calibration. On-site lubricant monitoring solutions generally incorporate either in situ (in line) sensors or bench-top analytical equipment. In situ sensors are difficult to calibrate, install, and maintain in a harsh industrial environment; they rarely receive the attention necessary to ensure consistently reliable results. Best practice methods are often ignored during the oil sample extraction, handling, and calibration of sensors and/or analytical equipment, leading to less than ideal (e.g. unreliable) oil analysis data.

LogiLube has developed new condition-based oil monitoring technology to simplify, integrate, and improve upon current methods for machine health management. This novel SmartOil™ Industrial IoT technology integrates: a) real-time electronic oil quality and machine parameter measurements with, b) automated, contamination-free oil sample collection, and c) accredited laboratory analysis, making the combination of the individual attributes greater than if taken separately.

LogiLube SmartOil™ P-F Interval

SmartOil™ is an advanced on-machine monitoring system that utilizes edge processing to collect and compute real-time oil quality data, identify machine-related events, and record a chain-of-custody incorporating all aspects of the oil collection, analysis and reporting process. Electronic sensor calibration is maintained by comparing 30-second sensor data to oil analysis lab results when synched up to a common time-stamp. Both real-time and lab analysis oil condition data are viewable via a secure web-based dashboard, correlating both datasets and delivering a ‘big picture’ machine health overview. Hands-free automated oil collection ensures contamination-free oil samples with an added bonus of reducing associated labor costs.

Incorporating this novel oil quality monitoring process, plant personnel are equipped with real-time ‘actionable machine health information’ that has been verified by lab-based oil analysis. Predictive analytic algorithms computed from the stream of BIG data provides need-to-know staff with advanced knowledge of machine health, and facilitates the necessary resources to focus on condition-based maintenance. Visit and watch the video to learn more.

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