Downloaded from https://www.everyspec.com ADS-79D-HDBK Warning limits should be set to alert an operator that a problem (e.g., bearing spall) has developed on the machine being monitored and that sensor parameters should now be monitored more closely as damage progresses towards the alarm limit. Its primary purpose is as an indicator of early damage to give an organization sufficient lead time to consider planning a scheduled maintenance at some future date. For instance, a warning limit set at a level that is 10% of the alarm limit is an indication that the bearing damage is in the early stages of damage progression.
9798 If monitoring several oil wetted components and only one sensor is in the oil stream, the most conservative limit is generally chosen. Appendix D, Minimal Guidance for Determining CIs/HIs for Propulsion Systems, provides guidance on prioritizing fault modes. MWARNING = 0.10 MALARM (for debris mass) (3) CWARNING = 0.10 CALARM (for debris counts) (4) M.6.6 Recommended Minimum Technical Requirements: To be considered a condition based maintenance tool, the ability to track performance metrics should be available. Any new monitoring tool introduced should at least provide the same fault detection or greater detection than the existing system.
All existing monitoring systems and any potentially new systems should strive to meet the SAE standard for a Condition Monitoring tool. In other words, detect 90% of faults in progress with no more than 10% false positive indications based on subsequent tear down analysis. As an example, the US Navy states that FDA meets all the criteria established by the SAE standard for a condition monitoring task. 99 M.6.7 Integration of Diagnostic Tools: Fault detection capabilities vary with the condition monitoring tool and the specific fault mechanism. Some tools detect a specific fault earlier than another tool, while for other faults the earliest detection is with the second tool.
Therefore, it is desirable to combine the strengths of each method to improve detection accuracy and robustness [Qui]. Fusing vibration and on-line oil debris sensing/oil debris monitoring (ODM) data has been demonstrated to provide more reliable indications of machine condition. The data from these two trending methods can augment, verify, and validate each other. Multisensor data fusion is a process to integrate oil debris and vibration based bearing damage detection techniques.
Information fusion is defined as ―the theory, techniques and tools conceived and employed for exploiting the synergy in the information acquired from multiple sources such that the resulting decision or action is in some sense better than that would be possible if any of these sources were used individually‖. 100 Data fusion methodology is the logical choice for integrating vibration and oil based measurement technologies for intelligent machine health monitoring. 97 ASTM D7685-11, Standard Practice for In-Line, Full Flow, Inductive Sensor for Ferromagnetic and Non- ferromagnetic Wear Debris Determination and Diagnostics for Aero Derivative and Aircraft Gas Turbine Engine Bearings.
98 SAE JA1012. A Guide to the Reliability-Centered Maintenance (RCM) Standard. 22 Aug 2011 99 Lastinger, W., R. Overman, and L. Yates, “Finding Bearing Failures through Filter Debris Analysis Reliability Information Analysis Center (RIAC) Journal , Vol . 14, No. 1, 1st qtr, p. 8-12, 2006. 100 Dempsey, Paula J. ―Integrating Oil Debris and Vibration Measurement for Intelligent Machine Health Monitoring‖, NASA/TM – 2003-211307, March 2003. 259