The conversation begins with an introduction to predictive maintenance and the expert panel. Glenn Gardner discusses the evolution of predictive maintenance, highlighting the transformation of the industry over the past 20 years. He then delves into the concept of vibration as a predictive technology, providing a detailed explanation of its relevance and power. The discussion shifts to the role of CMMS in predictive maintenance, emphasizing the importance of both predictive technology and CMMS for an effective maintenance program. Mark Kingkade shares insights on vibration analysis in real systems, showcasing the practical application of vibration analysis in identifying machinery flaws. The conversation covered challenges in equipment repair, integration of machine data with maintenance workflows, data integration and predictive maintenance, and integrating advanced maintenance technologies. The challenges included difficulty in finding replacement parts, extended lead times for replacements, and the impact of supply chain challenges. The integration of machine data with maintenance workflows highlighted the direct integration of diagnostic insights with work orders, automated generation and assignment of work orders, and the importance of getting information to the right people. Data integration and predictive maintenance discussed the integration of machine data from various sources, usage-based predictive maintenance, and a phased approach to implementing predictive maintenance. Integrating advanced maintenance technologies addressed challenges in integrating different maintenance technologies, the importance of keeping analysis in purpose-built tools, and sending scalar data to centralized systems.

Takeaways

• Predictive maintenance has evolved significantly over the past 20 years, with a notable reduction in cost and time for implementing a world-class program.
• Vibration analysis is a powerful technology for identifying machinery flaws, and when combined with CMMS, it forms an effective predictive maintenance program. Challenges in equipment repair due to supply chain issues
• Importance of direct integration of diagnostic insights with work orders

Chapters

• 00:00 Introduction to Predictive Maintenance
• 11:46 CMMS and Predictive Maintenance
• 32:29 Challenges in Equipment Repair
• 47:24 Data Integration and Predictive Maintenance
• 58:02 Integrating Advanced Maintenance Technologies