Machinery failure

Block, H. P. and Geitner, F. K., Machinery Failure Analysis and Troubleshooting, Gulf Publishing Company Book Division, Flou-ston, (1986). 

Usually most machinery failure analysis is performed at component level, therefore, a definition is required to state what constitutes life attainment in connection with component failure. Defect limit is a term that is used in this context and needs to be expanded upon to understand it fully. 

Experience shows that some machines have more frequent failures than do others. Obviously, different failure modes have different frequencies of occurrence. This is usually described as mean-time-between-failure (MTBF) and expresses the probability of machinery failure and breakdown events as a function of time. This is of particular interest to the maintenance failure analyst and troubleshooter who have to grapple with the realization that some machinery failure modes appear slowly and predictably whilst others occur randomly and unpredictably. In most cases, both types of failures have been encountered. 

The third step is to proceed to the component level because more than likely, the cause will be uncovered here. Table 62.2 shows machinery component failure modes commonly encountered in machinery failure analysis together with suggested standard life values, Weibull indices (/J), and responsiveness to preventable or predictable maintenance strategies. Referring to this table will help decision-making. To assist the analyst in documenting bad actor failure modes on the component level. 

Even though bad actor identification and tracking can be a complicated process, it is the first step in an effort to eliminate recurring and costly machinery failures. There are many other steps which are necessary in order to identify bad actors, and we will discuss them later. 

Routine work typically includes monitoring and reporting load changes, unusual occurrences, machinery failure, steam leaks, and the like. In addition, boiler plant personnel carry out a number of other specific functions, including those tasks described in the following sections. 

Nonconformances may fall into the category of manpower, machinery methods, materials, etc. Employees not following procedures or unable to execute required steps of the process indicate a poorly designed process requiring modification and/or improved training. Machinery failures often indicate poor qualification, validation, calibration, or maintenance programs. Unexpected results or outcomes are indicative of poor process design, characterization, or a break down between processes. 

Ineffective lubrication remains one of the leading causes for machinery failure. Routine inspection of the machine s lubrication system is among the easiest and most productive ways to avoid equipment failure and should play a pivotal role in the pursuit of precision lubrication. This program allows the operators and the mechanics who perform the routine inspections to have a better awareness of the importance of effective lubrication, resulting in the improvement of the quality of lubricant application. ... 

Lubricant condition monitoring is best accomplished by the analysis of numerical data that are associated with the various fluid failure modes [2]. Numerical data can be analysed by statistical methods to determine the relationship between the various test parameters and their respective fluid and machinery failure modes. In addition, the statistical analysis can be used to determine potential data interference sources, the various alarm limits for each parameter and other criteria to be used in the daily evaluation of used oil. Note that it is important to determine all of the causes for variability in parametric data, just as it is necessary to separate changes due to interfering causes from changes with its associated relevant failure modes. 

Rakowsky, U. K. Soffker, D. 1997. Real-Time Reliability Evaluation of Vibrating Mechanical Structures. Pusey, H.C. Pusey, S. (edts.) A Critical Link - Diagnosis to Prognosis. Proceedings of the 12th ASME Conference on Reliability, Stress Analysis, and Failure Prevention, Virginia Beach, USA. Society for Machinery Failure Prevention Technology, pp. 625-636. 

The depth of the failure analysis into the roots of a failure is the key to accurately unearthing all the failure sources. Looking at machinery failures one finds that there are [5]... 

Sachs NW. Understanding the multiple roots of machinery failures. Reliability Magazine 2002 8 18-21. 

The role of a reliability prognosis cannot be overestimated. Reliability prognosis plays a serious role in maintenance management of a machine. The ability to forecast machinery failure is vital... 

Bloch, H.P. Geitner, F.K. 1994. Machinery failure analysis and troubleshooting. 3rd ed. Houston, Texas Gulf Publishing Company. 

Helps to prevent the occurrence of serious casualty conditions due to undetected machinery failures... 

Machinery failure (including electronic devices, navigation equipment and safety systems) The containership s compartments include ... 

In this Chapter, only human errors are considered in the analysis. However, this can be extended to include failures induced by other causes, such as machinery failure. Hence, it can be easily integrated into the Formal Safety Assessment (FSA) framework as discussed in Cht ter 5. Step 4 of the FSA framework requires the evaluation of different risk control options. The AHP method presented here can be used for this purpose, and the results obtained from the analysis can be applied to Step 5 of an FSA. 

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