Overview

The rise of IIoT-based condition monitoring systems has enabled more facilities than ever before to reap the benefits of predictive maintenance.

Wireless sensing has unlocked access to more data and insights than was previously possible. Leading organizations are leveraging advanced system features that collect, organize, and categorize fault types to support more informed decisions.

When paired with Root Cause Failure Analysis, this information helps reduce systemic failures and increase Mean Time Between Failures (MTBF).

 

Proactive Reliability Maintenance

Proactive Reliability Maintenance is a strategy that works to prevent systemic failures from happening. Unlike Predictive Maintenance strategies, Proactive Maintenance uncovers the root cause of failures and identifies ways to improve MTBF. Taking action on these additional insights brings about superior results.

While predictive maintenance methods do support reduced costs through earlier identification and remediation of failures, by itself it does nothing to prevent or reduce the frequency of occurrences due to systemic issues. Benefits of proactive maintenance include increased uptime, reduced maintenance costs, improved safety, spare part optimization, and better maintenance workforce efficiency.

This can be simply illustrated in Figure 1 below. This is a modified version of the classic D-I-P-F curve that is often used to explain the impact of specific actions on the operational condition of equipment from the onset of “P” (Potential Failure) to “F” (Functional Failure). All actions taken to the right of the shaded green area can only detect potential failures. They do nothing whatsoever to extend the MTBF. However, actions taken during the shaded area of the curve do so through improvements in design, installation and operation of the asset.

 

Figure 1. Modified version of the classic D-I-P-F curve.
 

The area shaded in green is where Proactive Reliability (sometimes referred to as RCM, or Reliability Centered Maintenance, or Prescriptive Maintenance) methods applied can improve the reliability of the asset and either prevent or greatly reduce the occurrence of failure mechanism. An example of a Proactive Maintenance action might be to improve the bearing design. Whereas an example of Predictive maintenance methods would be condition based technologies like vibration analysis, or oil analysis.

 
Data Mining for Reliability “Gold”

A recent research conducted by the firm Research and Markets indicates that companies continue to aggressively adopt wireless sensors to facilitate operations and maintenance. This has brought unprecedented amounts of data, which was never possible with handheld systems. (Figure 2).

 

Figure 2. Research and Markets Survey. “Industrial Automation and Wireless IoT Connectivity Report 2024.”
 

The ability to capture in near real time online information helps enable a proactive approach to improving machinery reliability. In the past one had to gather, analyze and categorize data manually in order to see patterns in the failure modes. Features in more advanced IIoT systems include the ability to automatically capture, detect, define, and categorize faults (See Figure 3 below). When used properly, the user can see patterns in the occurrence and types of faults. This can then be used with RCFA (Root Cause Failure Analysis) to eliminate or minimize failures due to systemic faults.

 

Figure 3. Dashboard analytics captured from the ARO system (courtesy of Petasense Inc.)
 

Getting Started: Real-World Examples

In the author’s experience, several common issues can be effectively addressed through proactive reliability efforts. These include:

• • Misalignment (Installation-related)
• • Lubrication (Operational practices)
• • Cavitation (Operational conditions)
• • Bearing failures (Design or installation issues)

For example, Figure 3 (above) highlights a site where 40 faults were identified as Misalignment (M) and 26 as Structural Looseness (SL). A root cause failure analysis (RCFA) revealed that the site lacked proper precision maintenance skills, and the necessary tools, to correctly install and align both new and rebuilt equipment. This led to recurring failures and reduced MTBF (Mean Time Between Failures).

The solution involved targeted alignment training and the acquisition of shaft alignment tools.

In many cases, expert guidance is readily available. Local application engineers, such as those affiliated with bearing distributors or lubrication providers, can be excellent resources. They often bring deep domain expertise and are willing to collaborate on identifying root causes and implementing corrective actions.

These are just a few examples from the field. It’s important not to accept the status quo or fall into the mindset of “it’s always been done this way.” By leveraging the analytical power of modern software and analytics, organizations can shine a light on chronic equipment issues and uncover systemic faults that may have gone unnoticed.

 
Summary

The growth of IIoT systems and analytics presents a valuable opportunity to strengthen maintenance programs across all industries. These systems deliver far more data than ever before, which can be transformed into actionable insights.

Proactive Reliability Maintenance goes beyond traditional PdM strategies, offering broader benefits. Best-in-class organizations leverage this approach to gain a competitive advantage.

To fully realize these benefits, organizations should regularly review faults, fault types, and root causes—quarterly, semi-annually, and annually—and implement corrective actions based on these insights. Doing so helps extend Mean Time Between Failures (MTBF), improve overall productivity, and reduce maintenance costs.

 
About the author: Dan Bradley is a Mechanical Engineer with 40+ years of experience in the Reliability and Condition Monitoring industry. This includes consulting, instrumentation & software design along with implementation of programs around the world in a variety of industries. He has held previous positions that include CEO of Petasense, Inc., Global Director of SKF AB Reliability Systems, and he started his career with IRD Mechanalysis, Inc. Email: Dan.Bradley@petasense.com