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o Improved Understanding of problems, others roles and other functions o Improved Communication by Reducing Story Telling o Reactive to Proactive transition Becoming Proactive While RCA is viewed by many as primarily a reactive tool, it becomes a proactive tool when applied pro p e r l y. For example, consider a premature gearbox failure due to poor lubricant because no oil analysis was in place to monitor oil quality. Subsequently, foreign particles contaminated the oil and resulted in premature wear. If it is further discovered that there was no oil analysis program to detect contamination, one of the corrective actions from this RCA should be to implement an oil analysis program for all critical equipment at the site. In this situation, many other pieces of equipment will be proactively addressed as a result of the findings from just one RCA. Close The key to an effective RCA is to assure that: 1) The problem is defined, 2) The analysis breaks down a problem into its individual causes, both actions and conditions, and documents them on a cause and effect chart, and 3) Solutions attack and eliminate the causes. The key is for people to think about the causes and resist the temptation to blindly accept that “stuff happens”. Once the RCA method is understood within an organization, the RCA program will serve as the foundation to support the RCA pro c e s s . Defining the RCA program goals, roles and responsibilities, training strategy, threshold criteria, action tracking system and effectiveness metrics will be the first step towards assuring the success of the program. APPENDIX 1 - APOLLO RCA PROCESS SUMMARY Step I. Define the Problem • What: Primary Effect - What you want to prevent from occurring. • When: Date? Time? Status? • Where: Physical Location? Process Location? • Significance Safety: Injuries? Potential? Environmental: Regulations? Spills? Potential? Revenue Pounds? Rates? Delay? Hours? Quality? Dollars? Customer Service Delay? Customer Satisfaction? Cost: Materials? Labor? Dollars? Frequency: Occurrences? Timeframe? Step II. Create an Apollo Cause & Effect Chart Primary Effect EVIDENCE • For Every Effect Ask “Why?” • Look for Causes in Actions & Conditions • Connect Causes with “Caused By” (CB) • Support Causes With Evidence or Use a “?” Step III. Identify Effective Solutions Challenge Each Cause, Offer Possible Solutions & Be Creative The Best Solutions Must: • Prevent Recurrence • Be Within Your Control • Meet Your Goals and Objectives Step IV. Implement the Best Solutions Table & Track the Corrective Actions CB CB Cause/Effect EVIDENCE CB Cause/Effect Cause/Effect EVIDENCE EVIDENCE CB Cause/Effect Cause/Effect EVIDENCE EVIDENCE Causes Solution / Corrective Actions Name Date Cause/Effect EVIDENCE 49
50 A Automated Bearing Wear Detection Alan Friedman DLI Engineering bstract: The following paper will describe a methodology for automatically detecting and diagnosing rolling element bearing wear. The techniques involved have been proven in over 15 years of use in a huge variety of environments, machine types and applications. The techniques involved in diagnosing bearing wear will be described in detail and supported by a set of example graphs and an annotated diagnostic report. Introduction: Rolling element bearing wear will create a variety of different patterns in spectral data depending on the geometry of the bearing, the type and degree of wear, and the operating condition of the machine. Therefore, an automated diagnostic system approach to bearing wear detection must be capable of interpreting a huge array of spectral patterns and differentiating bearing wear from other machine faults or external causes. Although knowledge of the bearing geometry may be useful in calculating expected fault frequencies, a versatile diagnostic system should be able to detect bearing wear without requiring this information. Both of these requirements have been met in this system. Summary of Techniques: The automated bearing wear detection algorithms combine a number of well proven techniques to detect and confirm the presence of bearing defects. These include: • Data normalization • Automated spectral peak extraction • Cepstrum analysis • Noise floor calculation • Bearing wear logical rules • Demodulation. Each of these concepts will be reviewed and explained in the following document with respect to its contribution to automating the detection of rolling element bearing wear. Data Normalization: Because bearing tones in a vibration spectrum are non-synchronous, in other words, they are not integer multiples of shaft speed, the first order of business is to determine shaft speed and exclude peaks that are synchronous. This is accomplished by an automated data normalization routine. If actual shaft speed is known via a tachometer, manual input or other mechanism, the algorithm will simply use this value. If the shaft speed is not known, which is typical in variable frequency drives, the algorithm will search for a peak at the nominal machine speed plus or minus a percentage window set by the user. Up to ten user-defined forcing frequencies are also used by the normalization routine. The forcing frequencies help determine the pattern of the expected spectrum and the algorithm can successfully match the pattern even if some peaks are missing, including a strong shaft rate peak. Automated Spectral Peak Extraction: Once the data have been normalized, 10 user defined forcing frequencies are extracted from each of the spectra*. Next, the two highest undefined peaks are extracted from each spectrum. These are likely candidates for bearing tones. Data may be collected in all three axes and two frequency ranges for each test location, bringing the total number of potential bearing tone candidates to up to 12 per test location. When the human analyst attempts to find bearing tones, the same process is used. Known peaks are defined and excluded then un-defined, non synchronous peaks are considered as bearing tone candidates. Beyond the 10 user defined forcing frequencies, an array of related peaks will also be extracted and disre g a rded as possible bearing tones as described below. This is to say that there is a logical order of determining the likely source of a peak and the conclusion that it is a bearing tone only comes after all other logical explanations have been discredited. As an example of this, if the user defines the motor bar pass frequency, the system will automatically extract 2x line frequency sidebands around this peak and will disqualify them as bearing tones. (Even though they will be non synchronous and may not have been specifically defined by the user). These criteria would apply to a 2x line frequency peak in an AC motor as well, even if it was not defined. If the machine was defined as a diesel engine however, a peak at 120 Hz might be considered a bearing tone as 2x line frequency should not appear in this type of machine.
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August 2005 - Library

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