This is Part 3 of a 5 part series on Smart Reliability IIoT Condition Monitoring; don't miss Part 1, Part 2, Part 4 and Part 5!

Smart Reliability™ takes a failure mode approach to reliability and condition monitoring. Allied has­­­­­­­ databased over two decades of the Allied team’s failure modes analysis (over 3 million assets), condition monitoring services (assessing 10’s of thousands of assets each month) and reliability consulting practices (over 1000 sites served). Allied uses this database as the foundation of our Smart Reliability application for ThingWorx. This gives us a solid practical experience-based condition monitoring and predictive maintenance solution.

Other offerings take a preventative maintenance approach, recommending tasks on a per usage basis, i.e., change the oil every 500 hours of use, change the bearing every 2000 hours of use, etc. Even when other offerings take a condition based predictive maintenance approach, the depth of experience and success of those approaches does not compare to Allied’s depth of experience and success.

Predictive Maintenance Approach

A condition-based predictive maintenance approach is less expensive and more effective than a preventative maintenance approach. A condition based predictive maintenance approach built on decades of successful field practice is more effective than one built on less field domain experience.

Beyond Allied’s failure mode approach and its database of extensive field domain expertise, Smart Reliability uniquely builds on the extensive capabilities of the PTC ThingWorx® Industrial Innovation Platform. The ThingWorx application provides tag-based connectivity to process data, to enterprise data, to the client CMMS.

The ThingWorx platform goes further in offering augmented reality, allowing the overlay of tag data to live camera images from smart tablets, smart glasses, etc. This works by way of video how-to instruction that improves workers “first time fix” rate, to improve quality of maintenance, and to reduce time to repair. Allied leverages this augmented reality, working with our clients to implement digital work procedures that incorporate smart safety glasses and similar heads up displays.

Machine Learning

Lastly, the ThingWorx platform includes a machine learning engine to help our clients and Allied’s condition monitoring personnel find new relationships in the process and condition monitoring data. For example, Allied is already working to release a “virtual” sensor that predicts future oil sample analysis measures. The virtual sensor is based on Allied’s historical data of oil analysis, and the ThingWorx machine learning engine.

As practicing reliability engineers and condition monitoring subject matter experts, Allied coaches and mentors our clients along their digital transformation journey. This includes coaching on the people and process as well as the technology. Allied offers to procure and oversee the implementation of online inspection technologies as well. We become more of a one stop shop, where many of our competitors provide just some of the guidance needed for the full digital reliability journey.

Smart Reliability takes best practice condition monitoring and reliability engineering domain expertise, deploys it within an industry leading digital industrial innovation platform, and delivers an amazing Smart Reliability and Condition Monitoring application we can deploy today.

Call on Allied Reliability to begin or augment your Smart Manufacturing journey, with best in class predictive maintenance applications technology and services.

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