Reliability failure analysis

Setty, Kaushik, Subbarayan, Ganesh, and Nguyen, Luu, Powercycling Reliability, Failure Analysis and Acceleration Factors of Pb-Free Solder Joints, Electronic Components and Technology Conference, 2005, pp. 907-915. 

The RMDB consists of two mayor sections computer data storage and retrieval system, and backup microfilm data bank file. The computer data storage and retrieval system is used for die standard reliability and maintainability data listing, special calculations, and searches. This data bank system has been established to facilitate remote terminal access compatible with the GIDEP remote terminal programs. The microfilm data bank file is used for storage and distribution of supplier s documents, failure analysis curves, description of methods used in the collection of the data, and additional background information too extensive to include in the computer data bank. 

The bulk of the information in the report is included in a 317-page appendix that contains systems descriptions, station blackout fault trees, diesel generator historical data, and diesel generator common cause failure analysis results for 18 different nuclear power plants. Tables and graphs are well organized and present data correlated to each plant studied. The study also contains conclusions and recommendations for improving reliability. 

Detailed fracture and metal failure analysis is usually a very reliable and extensive aspect of investigations of major loss incidents. For most small to medium investigations, macroscopic evaluation is typically sufficient. Macro evidence, such as indications of shear or brittle failure on fracture faces, lines showing detonation direction, and the chevron (herringbone) pattern all provide valuable clues to sequence, type, and cause of the failure.(See Figure 8-9.)... 

PMDA-ODA on Si02. It is clear from Fig. 3 that the adhesion of PMDA-ODA to SiO, surface is significantly improved by the application of APS. This is not only seen initially but also after exposure to extended times at T H conditions, i.e. the reliability of the interface has been improved. Notice the spontaneous delamination (zero peel strength) of the PMDA-ODA film from non-APS treated silica surface after only 100 h in T H. It should be pointed out here that the 100 h exposure was the first point at which the samples were removed from the T H test chamber. It is possible that the delamination may have occurred much earlier than the 100 h reported here. Table 2 shows the locus of failure analysis results for the interfaces after initial peel and after exposure to T H for 100 (no APS only) and 700 h. 

Web-based- easy access, collaboration and deployment to multiple sites. Reliability data/history repository Framework for FMEA and failure analysis. 

Excursions outside these process limits may produce a yield crash or, worse, a longer-term reliability problem. The well-characterized process is less prone to such problems and when they do occur the capability is at hand to support failure analysis. 

J.R. Beall and L. Hamiter, Jr., "EBIC-A Valuable Tool for Semiconductor Evaluation and Failure Analysis", IEEE, 15th Annual Proceedings Reliability Physics 1977, p. 61-69. 

The reliability parameters, such as the mean time to failure, have to be determined in experiments under well defined conditions. Failure rates of microsystems for automotive applications are typically in the range of a few ppm (parts per million). This may sound negligible, but due to the large number of sensors sold every year and their increasing numbers in each car, even this failure rate must be decreased further. However, the engineer who tries to investigate failure mechanisms is confronted with the problem of lack of failures in the sense that he finds too few defective samples for a thorough failure analysis. Thus, due to the lack of a statistical basis, the quality of lifetime predictions under normal in-use conditions would be poor. 

Lamon J. Ceramics reliability statistical analysis of multiaxial failure using the Weibull approach and the multiaxial elemental strength model. Journal of the TVmeiican Ceramic Society 1990 73(8) 2204-2212. 

D. Maintainability review This three pronged approach to improving equipment reliability is based on failure analysis to identify root causes, testing of equipment immediately after repair to ensure quality work was performed, and performance analysis of equipment to determine equipment efficiency rates and replacement intervals. This methodology uses predictive technologies. 

Acoustic waves are often used in microscopy techniques for failure analysis and reliability testing of modem devices. Although they have a quite large wavelength up to several centimeters, they can be well used for nanoscopic investigations by introduction of near-field conditions [1], e.g. with microprobes. These microprobes can be used either as an acoustic source [2,3] or as a detector [4,5] together with a comparably large acoustic transducer. 

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. 

In this part of the paper the evaluated application of reliability data analysis techniques - procedure for comparison of two constant failure rates is perceived of an item produced for systems specific use/ utilization. 

Kapur, K. C. Lamberson, L. R. 1977. Reliability in Engineering Design. N.Y. John Wiley Sons. lEC 61650 Reliability data analysis techniques - Procedures for comparison of two constant failure rates and two constant failure (event) intensities. 

State 5 in this analysis is reserved for reliable (failure-free) state of sensor. 

This paper also outlines practical experience from failure analysis that may be important to share with SIS designers and system integrators. One example is that the use of too optimistic failure rates leads to unrealistic reliability targets in the operational phase. Another example is that many failures share failure causes, thus indicating that more awareness and control with common cause failures are required. 

The analysis of failure modes, their effects (FMEA) preceded by a functional analysis and a study of predictive Reliability (Quantitative Analysis), allows you to list and classify the predictable failures of a team. The FMEA intends to obtain an optimal system reliability drawing experience and expert opinion, using a simple and systematic analysis of possible failures (Figure 5). Tantamount to finding faults potential, to identify possible causes, to assess their effects to find the corrective action and implementation, to find a list of critical points. 

Rausand, M. Oien, K. 1996. The basic concepts of failure analysis. Reliability Eneineerinp and System Safety 7(53), 73-83. 

Common cause failure analysis (CCFA) or common cause analysis is used primarily to evaluate multiple failures that may be caused by a single event or causal factor common to or shared by multiple components. It is especially important in evaluating the true reliability pr uced by redundant systems or components. 

Lamon, (. (1990) Ceramics reliability statistical analysis of muhiaxial failure using the WeihuH approach and the miiltiaxial elemental strength. /. Am. Ceram. Soc., 73 (8), 2204-2212. 

Chapter 11 of this text discusses the use of fault tree analysis in determining system reliability, failure potential, and even accident cause factors through examination of specific or general fault paths. Additional information on the application and use of probability values in fault tree analysis is also provided in Chapter 11. 

The second and more common hardware FMEA examines actual system assemblies, subassemblies, individual components, and other related system hardware. This analysis should also be performed at the earliest possible phase in the product or system life cycle. Just as subsystems can fail with potentially disastrous effects, so can the individual hardware and components that make up those subsystems. As with the functional FMEA, the hardware FMEA evaluates the reliability of the system design. It attempts to identify single-point failures, as well as all other potential failures, within a system that could possibly result in failure of that system. Because the FMEA can accurately identify critical failure items within a system, it can also be useful in the development of the preliminary hazard analysis and the operating and support hazard analysis (Stephenson 1991). It should be noted that FMEA use in the development of the O SHA might be somewhat limited, depending on the system, because the FMEA does not typically consider the ergonomic element. Other possible disadvantages of the FMEA include its purposefiil omission of multiple-failure analysis within a system, as well as its failure to evaluate any operational interface. Also, in order to properly quantify the results, a FMEA requires consideration and evaluation of any known component failure rates and/or other similar data. These data often prove difficult to locate, obtain, and verify (Stephenson 1991). 

Bowers (at Life Cycle Engineering, cbowerse LCE.com) mentions the following risk assessment tools that can be employed to help preserve asset resources Simplified Failure Mode Effects Analysis, Root Cause Failure Analysis, and Reliability-Centered Maintenance. Should safety and health professionals step forward to train maintenance managers in risk assessment concepts ... 

The literature ([5-27], [5-28], [5-29]) provides failure rates for diesel standby plants which differ by several magnitudes. However, since the question posed can only be answered with the help of quantitative analysis, a statistical analysis of the operational behavior of diesel plants is indispensable for the determination of reliable failure rates. Figure 5.26 shows how many influence factors may lead to the failure of a diesel plant. This, in turn, requires that the failure rates for standby diesel power plants be determined as to type and mode of application. For this purpose, voluminous data on the operational behavior of standby diesel plants is required. This data can never be collected from the behavior of standby diesel plants under blackout conditions alone, since these blackouts, as mentioned above, are very rare. However, there is another avenue which leads to the acquisition of failure data, namely via monthly trial runs while the public power supply is in full and normal operation. 

A qualitative and quantitative failure analysis of the hardware—failure mode and effects analysis with documentation. It is necessary to carry out a probabilistic reliability calculation to determine the average probability of failure on demand of the safety function. 

The lying down of levels of quality and reliability necessary to ensure product success and acceptability in a particular market is a cause for increasing concern. They are the most difficult aspects to quantify in absolute terms, although statistical data fi-om company product precedents are helpful here. There are expressions used such as mean time before failure (MTBF) and mean time to repair (MTTR) that are used with mechanical, hydraulic, pneumatic, electrical, and other products. Nonetheless, some quantitative expression must be made in respect of quality and reliability at the initial design specification stage. A Company must ensure adequate feedback of any failure analysis to the design team. 

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