12th IFToMM World Congress, Besanon (France), June18-21, 2007

Reliability Assessment for Components of Complex Mechanisms and Machines

M. Vintr* Brno University of Technology

Brno, Czech Republic

AbstractThe article deals with the problem of reliability assessment of complex mechanisms and machines, it is specifically focused on the analysis of the current situation in the field of item reliability prediction. The techniques of the item reliability prediction using the most credible methods (MIL-HDBK-217F, PRISM) and reliability databases (EPRD-97, NPRD-95) are presented in the article. Also the latest item reliability prediction method FIDES is presented.1

Keywords: Reliability, Prediction, Methods, Databases

I. Introduction Nowadays the requirements on reliability of the complex

mechanisms and machines are constantly rising. Suppliers, who are able to manage reliability of developed and manufactured mechanisms and machines, have significant competitive advantages. The basis of the mentioned managing is reliability assessment in the initial stages of product life.

The reliability of the whole mechanisms and machines can be assessed using the well-known methods (e.g. Reliability block diagrams, Fault tree analysis). The application of these methods requires to assess (or more precisely predict) the reliability of individual items. Reliability prediction can be carried out through various techniques based on the experience with similar items, experts estimates, etc. However the most credible approach to prediction of item reliability is utilizing of internationally accepted reliability databases and reliability prediction methods.

Therefore the article is focused on characterization of the most common tools in the field of item reliability prediction. The tools described in the article are usually used for prediction of the failure rate () and Mean Time Between Failures (MTBF).

II. EPRD-97 and NPRD-95 Databases The databases EPRD-97 Electronic Parts Reliability

Data [6] and NPRD-95 Nonelectronic Parts Reliability Data [7] were developed by Reliability Information Analysis Center (RIAC). The RIAC is the U. S. Department of Defense chartered Center of Excellence.

The databases complement one another and do not contain duplicated data. The databases enable reliability prediction of most types of components used in complex

*E-mail: vintr@fme.vutbr.cz

mechanisms and machines. The EPRD-97 database contains failure rate data on electronic components, namely capacitors, diodes, integrated circuits, optoelectronic devices, resistors, thyristors, transformers and transistors. The NPRD-95 database contains failure rate data on a wide variety of electrical, electromechanical and mechanical components.

Both databases contain data obtained by long-term monitoring of the components in the field. The collecting of the data was last from the early 1970s through 1994 (for NPRD-95) and through 1996 (for EPRD-97). The data collection was focused on obtaining data on relatively new component types, data on many different sources, application environments and quality levels.

The purposes of the both databases are to provide failure rate data on commercial quality components, provide failure rates on state-of-the-art components in cases where data or analyses are not feasible or required and complement MIL-HDBK-217F or other prediction methods by providing data on component types not addressed by it.

Both databases are sold in paper and electronic form and the prediction according to these databases is supported by most software products focused on reliability prediction.

III. MIL-HDBK-217F Standard The MIL-HDBK-217F Military Handbook: Reliability

Prediction of Electronic Equipment [5] was developed by the U. S. Department of Defense in 1961 and it was revised several times. Support by the Department of Defense was terminated in 1995.

This standard was primarily developed for reliability prediction of the military electronic components. Nowadays the usage of the standard is common in many non-military areas and it is the most used reliability prediction method of the electronic component. Values included in standard are based on statistical analysis of actual field failures and are used to calculate failure rates.

The standard contains prediction for generic types of electronic components, namely microcircuits, semiconductors, tubes, lasers, resistors, capacitors, inductive devices, rotating devices, relays, switches, connectors, interconnection assemblies, meters, quartz crystals, lamps, electronics filters and fuses.

The standard contains two prediction methods, the parts count method, and the parts stress method.

12th IFToMM World Congress, Besanon (France), June18-21, 2007

A. Parts Stress Method The parts stress prediction method requires a greater

amount of detailed information and is applicable during the later design phase when stresses and other environmental and quality factors are known for each component.

The basic procedure in calculating the failure rate is by multiplying a base failure rate by operational and environmental stress factors. An example of a semiconductors component part stress model is as follows:

EQCSRATbp pipipipipipipi = (1) where p is predicted failure rate, b is base failure rate, T is temperature factor, A is application factor, R is power rating factor, S is power stress factor, C is contact construction factor, Q is quality factor and E is environment factor.

The quality and environment factors are used in most models. The usage and meaning of other factors differ according to the type of component. Specific values of base failure rate and factors are included in the standard.

B. Parts Count Method The parts count prediction method is applicable in the

early stages of design and development when little information about the design is known. The parts count method is a relatively simple prediction method using default stress values. The information needed to apply the method is generic type of the component, component quality level and equipment environment.

The model for equipment failure rate with parts count method is as follows:

( )==

=

ni

iiQgiEQUIP N

1pi (2)

where EQUIP is total equipment failure rate, g is generic failure rate for the i-th generic part, Q is quality factor for the i-th generic part, Ni is quantity of the i-th generic part and n is number of different generic part categories in the equipment.

The equation applies if the entire equipment is being used in one environment. If the equipment consists of several units operating in different environments, the equation should be applied to the individual units separately.

Microcircuits have an additional multiplying factor L, which accounts the maturity of the manufacturing process. For microcircuits in production for two years or more, no modification is needed. For those in production for less than two years, generic failure rate g should be multiplied by the appropriate L factor. Specific values of generic failure rate, quality factor and maturity of the manufacturing process factor are included in the standard.

In general, the parts count method will usually result in a more conservative estimation of failure rate than parts stress method.

The MIL-HDBK-217F is freely available on the Internet and prediction according to this standard is supported by most software products focused on reliability prediction.

IV. PRISM Method PRISM Reliability Prediction and Database for

Electronic and Non-electronic Parts is reliability prediction method developed by System Reliability Center (SRC).

The prediction method has two parts. The base failure rate of each component is predicted at first. This base failure rates are then modified with system-level process assessment factors.

The PRISM method failure rate model for a system is as follows:

() SWWNIGEIMM

GSGDEIMPIAS

+++++

+++= (3)

where S is predicted failure rate of the system, IA is initial assessment of the failure rate, P is parts process multiplier, IM is infant mortality factor, E is environmental factor, D is design process multiplier, G is reliability growth factor, M is manufacturing process multiplier, S is system management process multiplier, I is induced process multiplier, N is no-defect process multiplier, W is wear out process multiplier and SW is software failure rate prediction.

The initial assessment of the failure rate IA is the failure rate value which is obtained by using a combination of the RACRates model (is constituent of the PRISM method), the failure rate data contained in the reliability databases or userdefined failure data.

RACRates is component reliability prediction model that uses a separate failure rate for each generic class of failure mechanisms for a component. Each of these failure rate terms is then accelerated by the appropriate stress or component characteristic. This model form is as follows:

sjsjicceeooP pipipipi ++++= (4) where P is predicted failure rate, o is failure rate from operational stresses, o is product of failure rate multipliers for operational stresses, e is failure rate from environmental stresses, e is product of failure rate multipliers for environmental stresses, c is failure rate from power or temperature cycling stresses, c is product of failure rate multipliers for cycling stresses, i is failure rate from induced stresses, including electrical overstress, sj is failure rate from solder joints and sj is product of failure rate multipliers for solder joint stresses.

RACRates model is currently available for capacitors, resistors, diodes, transistors, thyristors, integrated circuits and software.

Specific values of failure rates, factors and multipliers are assessed according to the information on environment, operation, stresses, etc.

The PRISM method is not available in paper form, but only as software product developed by SRC.

12th IFToMM World Congress, Besanon (France), June18-21, 2007

V. FIDES Method The latest reliability prediction method is called FIDES.

This method is included in DGA-DM/STTC/CO/477-A FIDES Guide 2004 issue A: Reliability Methodology for Electronic Systems [4]. This guide was developed by consortium of French defense and commercial aeronautical companies and was published under the supervision of French Ministry of Defense in 2004.

The method was developed using experienced failure data from the aeronautical and military area and from manufacturers. The main aim of this method is to enable a realistic reliability prediction of electronic equipment, including systems operating in severe environments (defense systems, aeronautics, etc.).

The method is focused on electric, electronic and electro mechanic items, namely integrated circuits, discrete semiconductors, capacitors, thermistors, resistors, potentiometers, inductors, transformers, relays, printed circuit boards, connectors and piezoelectric parts.

The method covers intrinsic failures such as item technology and distribution quality. It also considers extrinsic failures resulting from equipment specification, design, production and integration, as well as selection of the procurement route. The method takes into account the failures resulting from development and manufacturing, and the overstresses linked to the application field such as electrical, mechanical and thermal.

The general failure rate model is as follows: ocessPringManufactur_PartPhysicalItem = (5)

where Item is predicted item failure rate, Physical is physical contribution, Part_Manufacturing is quality and technical control of the item's manufacture and Process is quality and technical control of the processes of development, manufacture and operation of the product containing the item.

The physical contribution can be expressed as follows:

( ) InducedonsContributi_Physical

onAcceleratiPhysical

= 0 (6)

where 0 is base failure rate, Acceleration is acceleration factor indicating sensitivity to the conditions of use and Induced is contribution of induced factors (overstress) inherent to the field of application.

The term between square brackets expresses the contribution of the rated constraints.

The Part_Manufacturing factor is a representative of component quality and can be expressed as follows:

( )[ ]11 1 = Grade_PartexpingManufactur_Part (7) where 1, 1 is correlating factors that determine the extent of the effects of Part_manufacturing on the item's reliability.

The Part_Grade can be expressed as follows:

( )

++= 36

compcompmanuf RAQAQAGrade_Part (8)

where QAmanuf is manufacturer quality assurance criteria, QAcomp is component quality assurance criteria, RAcomp is

component reliability assurance criteria and is criteria of the experience that the buyer of the item may have of his supplier.

The Process factor is representative of the quality and technical control of the reliability in the product lifecycle and can be expressed as follows:

( )[ ]Grade_ocessPrexpocessPr = 12 (9) where Process_Grade is grade indicating process control and 2 is correlation factor that determines the range of the Process factor.

Specific values of all factors, criteria and required inputs are assessed according to tables, equations and recommendations included in the guide.

The electronic form of the FIDES guide is freely available on the Internet and prediction according to this guide is supported by some software products focused on reliability prediction.

VI. Other Approaches A. RDF 2000 Method

The RDF 2000 reliability prediction method is included in technical report IEC/TR 62380 Reliability Data Handbook: A Universal Model for Reliability Prediction of Electronics Components, PCBs and Equipment published by International Electrotechnical Commission (IEC) in August 2004. The technical report is based on French Telecom standard UTE C 80-810 [8] published by Union Technique de l'Electricit (UTE) in July 2000.

This method provides elements to calculate failure rate of mounted electronic components. It makes reliability prediction easier to carry out, thanks to the introduction of influence factors.

B. Telcordia SR-332 Standard The standard Telcordia SR-332 Reliability Prediction

Procedures for Electronic Equipment was developed by Telcordia Technologies Inc. It originated from the Bellcore standard developed by AT&T Bell Laboratories and sometimes it is called Bellcore SR-332. The most version of this standard was released in May 2001. The prediction method is focused on equipment for the telecommunications industry and it is applicable to commercial electronic products. The method is based on MIL-HDBK-217F principles and better reflects Bellcore field experiences.

C. NSWC-98/LE1 Standard The NSWC-98/LE1 Handbook of Reliability

Prediction Procedures for Mechanical Equipment was developed by the U.S. NAVY Naval Surface Warfare Center. The recent version of this standard was released in September 1998. The standard contains models for various categories of mechanical components and enables to predict failure rates which are affected by temperature, stresses, flow rates and various other parameters.

12th IFToMM World Congress, Besanon (France), June18-21, 2007

D. GJB/z 299B Standard The GJB/z 299B Reliability Calculation Model for

Electronic Equipment is a Chinese standard translated into English in May 2001. This standard was developed for the Chinese army. The standard is very similar to MIL-HDBK-217 and includes both a parts count and parts stress prediction method. Sometimes the standard is called China 299B.

VII. Conclusion The article surely is not a complete overview of all

databases and methods that are used for reliability prediction of the items and systems. The article introduces the most frequently used databases and methods in the field of complex mechanisms and machines. The methods that are not introduced in the article are intended for specific field of application or specific company.

Utilization of the database NPRD-95 is the most frequently used reliability prediction method of non-electronic components and it has not a serious rival.

The situation in the field of reliability prediction of electronic components is rather complicated. The database EPRD-97 is also worldwide used and it contains a large amount of component reliability information but it does not allow to take all influence factors into consideration. Utilization of the MIL-HDBK-217F standard is the most frequently used reliability prediction method of electronic components but development of the standard was terminated and nowadays it is obsolete. For that reason the PRISM and FIDES methods offer different approaches to reliability prediction that eliminate deficiencies of the MIL-HDBK-217F. However, the PRISM software is relatively expensive and the FIDES method is relatively new and insufficiently verified in practice.

A final decision about the choice of proper reliability prediction method of the complex mechanisms and machines is notably dependent on the purpose of prediction and customers demands. According to authors personal experience it is possible to carry out the most of the predictions with utilization of databases NPRD-95, EPRD-97 and MIL-HDBK-217F standard.

References [1] Dylis, D. D. and Priore, M. G. A Comprehensive Reliability

Assessment Tool for Electronic Systems. In Proc. Ann. Reliability & Maintainability Symp. 2001. Institute of Electrical & Electronics Engineers, 2001.

[2] Marin, J. J. and Pollard, R. W. Experience Report on the FIDES Reliability Prediction Metod. In 2005 Proc. Ann. Reliability & Maintainability Symp. Institute of Electrical & Electronics Engineers, 2004.

[3] Smith, Ch. L. and Womack, J. B. Jr. Raytheon Assessment of PRISM As A Field Failure Prediction Tool. In Proc. Ann. Reliability & Maintainability Symp. 2004. Institute of Electrical & Electronics Engineers, 2004.

[4] DGA-DM/STTC/CO/477-A. FIDES Guide 2004 issue A Reliability Methodology for Electronic Systems. FIDES Group, 2004.

[5] MIL-HDBK-217F. Military Handbook Reliability Prediction of Electronic Equipment. Department of Defense, 1991.

[6] Electronic Parts Reliability Data (EPRD-97). Reliability Analysis Center (RAC), 1997.

[7] Nonelectronic Parts Reliability Data (NPRD-95). Reliability Analysis Center (RAC), 1995.

[8] UTE C 80-810. RDF 2000 Reliability Data Handbook A Universal Model for Reliability Prediction of Electronics Components, PCBs and Equipment. Union Technique de lElectricit, 2000.