Predictive Maintenance and IoT 

According to Statista, the global predictive maintenance market is expected to reach a size of 64.3 billion U.S. dollars by 2030. The growth of the market value as well as the number of predictive maintenance devices is explained by their ability to predict when industrial equipment or machinery may face failures and prevent them.  

The rapid adoption of predictive maintenance, especially in terms of manufacturing, energy, and transportation projects, is also proved by 1NCE customers. Over 1,800 of them operate in the field of Industrial Automation, where predictive maintenance is an essential prerequisite. Additionally, an overwhelming 90% of customers from various sectors, including Utilities, Automotive, Logistics, Infrastructure, and Agriculture, are actively using IoT-enabled predictive maintenance for their equipment and systems. 

Top Vendors of Predictive Maintenance Equipment

Predictive maintenance vendors are developing globally, with the top players mentioned in the table:

Vendor Location Website 
IBM United States www.ibm.com  
Schneider Electric France www.se.com  
General Electric United States www.ge.com 
Honeywell United States www.honeywell.com 
ABB Switzerland www.abb.com 
Rockwell Automation United States www.rockwellautomation.com 
PTC United States www.ptc.com 
Bosch Germany www.bosch.com 
Fluke Corporation United States www.fluke.com 

Note: The list makes no claim to completeness.

IoT Applications in Detail

The role of IoT in predictive monitoring implies the following applications:

Application 

Description 

Condition Monitoring Continuous monitoring of equipment to detect deviations from normal conditions, helping to identify potential issues before they become critical. 
Machine Learning Utilizing machine learning algorithms to analyze data and predict equipment failures based on historical patterns and real-time sensor data. 
Vibration Analysis Monitoring equipment vibrations to detect abnormal patterns that may indicate impending failures in rotating machinery. 
Thermal Imaging Using thermal cameras to detect overheating or temperature anomalies in equipment, which can be an early sign of problems. 
Oil Analysis Regular analysis of lubricating oil to identify contaminants or wear particles, providing insights into the condition of machinery. 
Ultrasound Testing Detecting high-frequency sounds emitted by equipment to identify mechanical issues or leaks that may not be visible. 
Remote Diagnostics Enabling remote experts to diagnose and troubleshoot equipment issues without being physically present at the site. 
Predictive Analytics Applying data analytics to predict when maintenance is needed, optimizing schedules, and reducing downtime. 
Failure Mode and Effects Analysis (FMEA) Systematically evaluating potential failure modes and their consequences to prioritize maintenance efforts. 
Asset Health Score Assigning a health score to each piece of equipment based on various data inputs, allowing for easy prioritization of maintenance tasks. 

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