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Harnessing the Power of Data Analysis, Intelligent Sensors for Predictive Maintenance

The expanding use of intelligent sensors and data analysis by companies and government entities is helping leaders predict when equipment needs maintenance before it actually breaks down.

Ludeca Sensalign 250 x 250 Harnessing the Power of Data Analysis, Intelligent Sensors for Predictive MaintenanceOil platforms, telecommunication network infrastructure, rail systems and even vending machines are producing a wealth of data that can be analyzed to enhance risk management or maintenance processes for virtually any type of equipment, Ronny Seehuus notes in a recent article for Capgemini.

Seehuus points out that intelligent sensors that are used to measure the physical condition of equipment – temperature, humidity, etc. – can help manufacturers and companies in other industries detect trends and patterns to predict a failure or breakdown of a mechanical part before it actually happens.

For instance, bearing failures in manufacturing equipment such as flaking and pitting or unusual wear patterns are sometimes caused by the high temperatures of the metal housing that surrounds the bearing. An unforeseen mechanical breakdown can cost thousands of dollars or more in lost productivity, especially if the repair is time-consuming and/or it’s difficult to obtain and quickly install a replacement part.

However, the use of data analysis and intelligent sensors can alert decision makers when they need to take action to help avert unnecessary downtime.

Blending data analysis with detector technologies can also yield other business benefits. Vibration analysis experts have historically been required to start and maintain vibration-based predictive maintenance programs, according to a white paper by ifm effector Inc., an automation technology manufacturer.

However, as the cost to produce and deploy intelligent sensors has continued to drop, using them is often a more cost-effective approach to predictive maintenance than sending engineers or consultants to evaluate equipment wear and tear.

Public sector agencies are increasingly using data analysis, predictive analytics and intelligent sensors to anticipate maintenance and operate more efficiently.

PEMEX Tula Refinery has been able to increase its efficiency while protecting its cooling towers, which are critical in the oil refining process, by installing a set of technologies to support wireless monitoring and analysis of process and vibration sensor data, according to an article in the Consulting-Specifying Engineer magazine.

The use of automated monitoring and data analysis techniques has provided the refinery with a continuous flow of sensor and equipment data, which has helped save personnel time while reducing risk. Now, PEMEX can monitor all general process variables such as water level, pressure, temperature, and water PH.

Vibration monitoring of the motors, reduction mechanisms, and pumps enables plant personnel to create predictive maintenance schedules for the machinery. A centralized human machine interface collates all the variables and graphics related to refinery operation, saving the company more than 20 hours per week in manual measurements or more than 960 man-hours that can be dedicated to other activities.

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