Unveiling the Inefficiencies in Traditional Maintenance Methods

As we delve further into the complexities of machine failures and disruptions in manufacturing facilities, it’s evident that even the best maintenance strategies—such as RCM, FEMA, RBI, Failure Elimination, and Root Cause Analysis—fall short in preventing these issues. Despite rigorous implementation, these methods struggle to enhance reliability due to five critical factors:

1. Insufficient Data Collection

Many maintenance programs still rely on outdated practices, such as checking motor current readings sporadically with an ammeter. This approach is fundamentally flawed, as these readings often reveal nothing about impending failures. Induction motors, even with early-stage faults, may not draw more than their nameplate current. At minto.ai™, we capture thousands of current values per second to provide a comprehensive health assessment of the motor. Both the quantity and quality of data are paramount in predicting potential machine failures accurately.

2. Infrequent Data Collection

Advanced health monitoring tools like the CSI 2140 lack the flexibility for frequent data gathering. This infrequency allows failures to occur undetected between monthly or weekly inspection cycles. Even with a highly skilled workforce, the rapid development of faults can result in unplanned downtime, as the infrequent data collection fails to catch issues in time.

3. Limited Resources

Manufacturing facilities, especially those in sectors like steel/metals, pharmaceuticals, and power plants, often manage numerous assets, making manual data collection impractical. For instance, a mid-size manufacturing company might have 350 induction motors, while another section of a process plant might operate 179 pumps. Monitoring such a vast array of assets demands more personnel than available, leading to inconsistent data collection by different individuals.

4. Accessibility Concerns

Critical machines, including vane pumps, ID fans, AHUs, submersible pumps, and conveyors in steel manufacturing facilities, are often challenging to access and pose significant dangers to technicians. Maintenance tools requiring close proximity to these machines for data collection are inherently ineffective in such hazardous environments.

5. Variable Conditions and Principles

Operating conditions, such as load and environmental factors, can vary significantly. Identical induction motors may operate different machines with distinct operating principles. For example, a 10HP motor running a compressor will exhibit different characteristics compared to a 10HP motor running a pump. These variations can lead to inaccurate conclusions when existing maintenance tools and methods lack the intelligence to adapt to these differing conditions.

The Path Forward

These challenges highlight the inefficacy of traditional maintenance tools and methods in reducing machine downtime and enhancing availability. In our final article of this series, we will explore how an AI-driven IoT product can address these issues, significantly reducing machine downtime and achieving an impressive 99.999% availability. This forthcoming discussion will also guide asset owners in making informed investments in suitable IoT solutions.

By addressing these pain points with cutting-edge technology, minto.ai™ is poised to revolutionize industrial maintenance, making machine failures a thing of the past. Stay tuned for insights that could transform your approach to asset management and reliability.