Predictive monitoring with AI anticipates critical failures in pump-motor assemblies

In industrial environments, asset reliability is a decisive factor for productivity. Motors, pumps, and hybrid systems like pump-motor assemblies operate continuously and are subject to natural wear, as well as mechanical and electrical failures. When maintenance teams fail to detect these issues in time, they can lead to unplanned downtime, high corrective maintenance costs, and even operational risks. 

However, predictive monitoring solutions powered by AI — such as DynaDetect — enable continuous machine health tracking through wireless sensors and advanced analytics platforms. With this technology, it’s possible to monitor asset behavior and receive automated diagnostics before a critical failure occurs. 

In addition to vibration and temperature monitoring, artificial intelligence systems analyze asset data and generate technical reports that highlight symptoms and potential causes of equipment failures. 

This solution enhances maintenance team productivity, showcases the advancements of Industry 4.0, and reflects the business maturity of companies investing in robust technological infrastructure for maintenance.   

To illustrate these advancements, here are two success stories featuring the AI-powered predictive maintenance solution, DynaDetect: 

Electrical failure detected in vertical Motor Pump assembly (1780 rpm)  

The motor pump assembly operated in a vertical configuration at 1780 rpm. Technicians applied predictive monitoring at various points of the equipment, including the bearing housings of both the electric motor and the pump.  

Additionally, the pump showed signs of lubrication deficiency and rotational looseness, with spectral data revealing blade-passing frequencies and characteristic rubbing noises.

Mechanical failure progression in Motor Pump assembly (3580 rpm) 

The second case involved another vertical motor pump assembly, this time operating at a higher speed: 3580 rpm.  

Predictive analysis was applied to the motor and pump bearing housings, focusing on temperature and vibration trends over 30, 7, and 3 days. Unlike the first case, the motor did not show significant failure signs, only random frequencies consistent with mild lubrication deficiency, below alert thresholds. 

The pump, however, exhibited a notable increase in vibration and temperature levels, with spectral indicators pointing to bearing defects such as 2x BSF (Ball Spin Frequency) and FTF (Fundamental Train Frequency – inner race fault).  

Rotational looseness was also observed, which can compromise shaft stability. These findings reinforce the importance of continuous monitoring and the correlation between spectral and thermal data. 

Technology that transforms industrial maintenance

These two cases demonstrate how Dynamox’s predictive monitoring can anticipate critical failures, prevent unexpected downtime, and guide maintenance actions with precision.

By turning data into strategic decisions, the solution directly contributes to:

• Cost reduction;
• Increased asset availability;
• Operational safety;
• Workforce optimization;
• 10x faster failure diagnostics compared to traditional analysis.  

In the analyzed cases, AI correlated specific frequencies with electrical and mechanical failures, such as rotor slotting, bearing defects, and lubrication issues — combining speed and reliability in data interpretation.

This continuous learning capability and generation of technical insights position DynaDetect as an essential tool for industrial operations aiming to:

• Minimize unplanned downtime;
• Maximize asset availability;
• Evolve toward smarter, scalable predictive management. 

Interested in bringing this maintenance strategy to your operation?   

Talk to a Dynamox specialist and discover how AI-powered predictive monitoring can help your plant build a high-performance predictive model.