Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1463313
6 | March - April 2022 | www . machinerylubrication.com in order of their ranking from left to right (clockwise). Again, this can be customized to the application. e single detection method is represented in each chart. e CAM graph shows vibration analysis as an example. As can be quickly seen from the spikes, vibration is really good at some failure modes (misalign- ment, unbalance, resonance) and far less effective at others (particle contamination, degraded oil, viscosity starvation). Viewed in this way, it is apparent that a single condition monitoring method (vibra- tion) is inadequate for protecting the bearings of the machine from all the highly ranked failure modes. By combining the best detection methods with the Pareto ranked failure modes, the most comprehensive level of coverage and protection is achieved, as illustrated in the CAM graph in Figure 5. e spikes are color-coded to represent the different detection methods, as shown in the legend (upper left corner). Unlike Figure 4, each spike reaches down at or near the proactive domain (central region of the CAM graph). Figure 4 and Figure 5 represent the same machine or component (rolling element bearing). Figure 4 applies only a single detec- tion method (vibration), whereas Figure 5 applies five methods. e final optimized selec- tion of the detection methods and frequency of use can now be easily rationalized to achieve the best results. In Sum Condition Alarm Mapping is a graphical representation of condition monitoring across multiple machine types, applications and detection methods. It emphasizes the impor- tance of showing the time domain from failure inception and the ability of the detection methods to deliver good results. Using this method, the user can better achieve: • Early fault or problem detection • Prolonged P-F interval • Optimized choice or selection of condi- tion monitoring methods (including inspection) • Prioritization of resources, based on ranked failure modes for individual machines and applications ML About the Author Jim Fitch has a wealth of "in the trenches" experience in lubrication, oil analysis, tribology and machinery failure investiga- tions. Over the past two decades, he has presented hundreds of courses on these subjects. Jim has also published more than 200 technical articles, papers and publica- tions. He serves as a U.S. delegate to the ISO tribology and oil analysis working group. Since 2002, he has been the director and a board member of the International Council for Machinery Lubrication. He is the CEO and a co-founder of Noria Corporation. Contact Jim at jfitch@noria.com. Figure 5. Rolling Element Bearing – optimized detection across multiple failure modes and multiple condition monitoring detection methods AS I SEE IT START YOUR FREE SUBSCRIPTION www.machinerylubrication.com