Vibration data is, at its heart, complex. Analysts require many years of experience in both reviewing data and a high level of understanding of the machinery. The two skillsets are not typically closely aligned, one being a deep technical understanding of the physical and electrical characteristics of machine in a given process, the other a more mathematical approach to data analysis. Yet, there exists a community around the world of individuals who have grasped these skills and add real value to maintenance programmes as a result.

Part of the reason for this is, not only a personal desire from these individuals to upskill, but due to a large number of tools and established training programmes to help demystify the underlying data. With the introduction of wireless condition monitoring tools, a new generation of technicians, fitters and electricians are being exposed to the world of predictive maintenance. However, some of these systems can abstract away the underlying datasets for the purpose of presenting a simplified overview of the machinery health. Some systems take it a step further and suggest the appropriate fixes.
The ideal system allows for high-quality machine diagnosis completed by an expert in analysis with support from automated systems. Ensuring that a human remains in the loop will help maintain confidence in the conditioning monitoring programmes established within companies.

The balance between human expertise and technological advancement highlights the ongoing need for comprehensive training and continuous learning in the field of vibration analysis. As predictive maintenance technologies evolve, so too must the skill set of those who use them. The future of maintenance programs relies not only on the development of more sophisticated tools but also on the cultivation of a workforce capable of leveraging these tools to their fullest potential, thus allowing individuals and teams to ensure true operational excellence.

Encouraging a culture of lifelong learning and curiosity is essential for ensuring that maintenance professionals can adapt to new technologies and methodologies. This approach not only enhances the effectiveness of maintenance strategies but also fosters innovation within the field, pushing the boundaries of what is possible in predictive maintenance and machinery diagnostics. High-quality diagnostics are essential to drive further efficiencies and reduce costly downtime so it is in everyone’s interest to keep training, adopting new technologies, and sharing knowledge.