Enhanced Reliability Prediction Method Based on Merging Military Standards Approach with Manufacturer’s Warranty Data Andre Kleyner • Delphi Delco Electronics Systems • Kokomo. Mark Bender • Delphi Delco Electronics Systems • Kokomo. Key words: Reliability prediction, Warranty return, Failure rates, MIL-HDBK-217 SUMMARY & CONCLUSIONS This paper presents a practical method of enhancing the process of reliability prediction by merging empirical equations of MIL-HDBK-217 [1] with generic component failure rates, which are obtained from manufacturer’s warranty data. Each of those two reliability prediction methods have advantages and disadvantages; though the appropriate combination of both would help the user to overcome their shortcomings and make the most of their benefits. The merging of the two methods can be accomplished by adjusting the appropriate empirical equations and coefficients from MIL-HDBK-217 by multiplying them by WCF. Where WCF is a “warranty correction factor”, derived from the analysis of manufacturer’s warranty return data. The proposed procedure would greatly enhance the reliability prediction process by: a. improving the accuracy of MIL-HDBK-217 calculation technique b. providing a built-in consistency of the results with the product’s warranty data c. utilizing product/component history d. allowing to account for specific component usage conditions e. minimizing the adverse effect of less certain variables, such as quality factor, serial resistance, and others, on the accuracy of reliability prediction. f. providing a higher degree of credibility in the eyes of the customer accustomed to traditional reliability prediction methods. 1. INTRODUCTION Reliability prediction is one of the most common forms of reliability analysis, usually employed at the earlier design stages, in order to evaluate inherent reliability of product design as well as to identify potential reliability problems. The most common reliability prediction techniques of the past were based on empirically driven MIL-HDBK-217; the military standard widely used for three decades by many electronics companies. Reliability prediction models such as described by MIL-HDBK-217 are widely accepted in industry. However, they do not provide accurate values in a
number of situations. Therefore, during the last years this method’s popularity has been gradually declining, mostly due to proliferation of new electronic packaging technologies, continuous improvement in quality and reliability, and thus subsequent inability of MIL-HDBK-217 to make accurate failure rate predictions. However, most of the mathematical models in MIL-HDBK217 along with the relevant principles of physics remain largely valid. In the past years there have been multiple efforts to improve the accuracy of MIL-HDBK-217 predictions. The examples include PRISM®, developed by RAC [2], British Telecom HRD-5, European IEC 1709, Telcordia SR-332, French developed CNET 93, and others. This paper presents an attempt to further enhance the process of reliability prediction by linking the results to the product field data. 1.1 Nomenclature & Notation t R(t) T TMR TA
λ λb λPM λ0 λf πi
πi-typical WCF SMT OEM
Product operation time (hours) Reliability as a function of time Component temperature (° C) Component’s maximum rated temperature (° C) Ambient temperature (° C) Failure rate Base failure rate in MIL-HDBK-217 Predicted failure rate, calculated according to MIL-HDBK-217 Historical component’s failure rate, based on field data Final failure rate after being adjusted to account for warranty return data MIL-HDBK-217 multipliers accounting for component usage conditions MIL-HDBK-217 multipliers corresponding to typical component applications Warranty correction factor Surface mount technology Original Equipment Manufacturer
In this paper the terms component and part will be used interchangeably.
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2. MIL-HDBK-217 APPROACH Most of the reliability prediction approaches assume that the reliability function R(t) can be described by an exponential − λt
distribution R (t ) = e (see [3], [4], or any other reliability textbooks). These approaches also operate on the premise that the failure rate of a system is primarily determined by the components comprising that system. The traditional MIL-HDBK-217 approach bases the calculation of each individually predicted component’s failure rate λPM on the equation structured similar to (1) presented below.
λ PM = λb ∏ π i i
(1) This equation includes the product of λb, component’s base failure rate and various multiplication factors πi, designed to account for component’s operation characteristics and its design. The πi factors (usually annotated with subscript capital letters: πCV, πE, etc.) vary for each component, accounting for parameters such as power dissipation, maximum rated temperature, resistance value, number of pins, environmental stresses, and others. The πi factors may also include junction temperature, quality factor, contact form factor, serial resistance, and other variables, which are more difficult to define and often requiring some guesswork in order to perform analysis. As mentioned before, MIL-HDBK-217 has not been updated for more than a decade and often does not provide satisfactory accuracy, though it still contains many valid mathematical assumptions and models. For example, the temperature effect on failure rates is calculated according to the Arrhenius model, which is still widely used by reliability engineers. It also takes into account component’s powertemperature characteristics, derating, electrical stress factors, and, in general, can adequately perform an A to B comparative analysis. Summary: MIL-HDBK-217 advantages include useful empirical equations allowing the user to account for component type and specific operating conditions. The disadvantages include low accuracy of predicting absolute values of component’s failure rates, which are further weakened by the effect of lesser-defined parameters. 3. WARRANTY ANALYSIS APPROACH In recent years many companies, in search for better tools, expanded utilization of other sources for reliability prediction, of which manufacturer’s warranty data remains one of the most popular and reliable. However, often being set up as a cost accounting system, most warranty databases lack specifics of electronic part’s failure conditions and often contain just generic failure rates, specific to a component family. Therefore, it often impedes the use of warranty data
for the analysis of components operating under conditions different from most typical applications. This also makes it difficult to account for the specific component parameters, like resistance, capacitance, number of pins, geometry, etc. In addition to that, a warranty database can be partially outdated and contain the data pertaining to an older technology. Needless to say that an accurate reliability estimate has to be derived from a good quality warranty data. That implies many important features of which the sufficient number of data points and robust data analysis process are very critical. The procedure of data collection and data processing should be capable of minimizing the effect of noise factors, such as: wrong failures, misclassified parts, human factor in data recording, etc. Components warranty return data are usually presented in a variety of forms. One of the commonly used formats is shown in the Table 1, which links the component family and its warranty-driven historical failure rate λ0. Table 1. Example of the Warranty Reporting Format Component Family Historical Failure Rate λ0 (failures per million hours) Film Resistor Leaded 0.00262 Film Resistor SMT 0.000723 Microprocessor Leaded 0.00322 Microprocessor SMT 0.00456 Bipolar Transistor SMT 0.000312 This data type, though well correlated to the field, does not allow any variability to account for more extreme or more benign conditions than the average. For example, some automotive customers are very specific about their operating temperature cycles and expect that predicted failure rates reflect the specified temperature variations. It is not uncommon to see the chart similar to the Table 2 in the Section 5 (EXAMPLE) of this paper, specifying operating temperatures in conjunction with percentage of total operating time. Warranty data similar to the one presented in Table 1 would not easily allow for this kind of flexibility. In some other cases the requirements can be specified for customer severity, e.g. 90th percentile or 95th percentile customer usage, which would also be difficult to address using only the set of λ0 from Table 1. Summary: The advantages of applying warranty or other field data to reliability prediction include relative accuracy of prediction and consistency of the results with product’s performance in the field. Moreover, reference to the field data provides more credibility with the customer examining the data. The disadvantages include inability to account for specific usage conditions and to distinguish between different parts within the same part family.
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4. MERGING TWO METHODS
According to equation (1), λPM is influenced by various multiplication factors πi, reflecting the component’s type, parameters, and operating conditions. Combining the two methods would require finding the appropriate πi factors, consistent with the warranty database conditions. In other words, the failure rates for each component family should be first calculated with πi-typical, the most commonly used parameters for each part family as presented in equation (2).
λ PM (typical conditions ) = λ b ∏ π i −typical
MIL -HDBK -217 Curve
W CF =
λ PM λ0
π i-typical
(2) followed by the multiplication by WCF to match λ0, as presented in equation (3).
λ PM (typical conditions ) × WCF = λ0 (3) Thus the warranty correction factor can be calculated by (4) as illustrated by the graph in Figure 1:
λ0 λb ∏ π i−typical i
(4) Figure 1 can help to understand the theory behind the process of determining the constant WCF. Now the obvious question is: how we define the “typical” operating conditions linked to the πi-typical multipliers? Considering the generic nature of each λ0 it is impossible to discern the precise conditions under which those parts failed. Clearly, the λb and π-multipliers will be specific to each part family; therefore one of the possible ways to obtain required information would be through conducting a designer survey on a per component basis.
λ0 λ PM
Corrected for Warranty Curve
,
i
WCF =
For each category of components, designers would need to respond to the questions, such as “What is the most common capacitance of the electrolytic capacitors used in those products?” or “What is the power rating most commonly used for SMT thin film resistors in those applications?” and many others. The component families can be further refined on the basis of usage and technology. For example, similarly to Table 1, the resistor categories may include leaded film resistor, SMT film resistor, wire wound resistor, and other resistor types. Each part family will be characterized by its very own WCF. The procedure of specifying the “typical” conditions, though tedious, would have to be done only once, and then followed by some sort of maintenance or yearly upgrades.
Failure Rate
As mentioned before, each of the reliability prediction methods considered above have strengths and weaknesses. In this paper the authors propose combining the two methods with the idea of adding up the benefits and reducing disadvantages of both approaches. The easiest way to merge those techniques is to adjust the failure rates obtained from MIL-HDBK-217 by multiplying them by WCF, the “warranty correction factor” making them more in line with the failure rates obtained from the field. The whole process would be similar to calibration of predicted λPM, obtained from MILHDBK-217 to match the historical failure rate λ0. In the cases where no warranty data is available, the λ0 can be obtained from other applicable sources, such as lab data or reliability prediction software, though with the lesser degree of correlation to the field.
MIL -HDBK -217 Variables
Figure 1. Failure rate adjustment process. The equation for the adjusted for warranty final failure rate λf would be:
λ f = W CF × λ b ∏ π i i
(5) Here the parameters λb and π-variables are obtained directly from MIL-HDBK-217 for the current part family and appropriate operating conditions. This final equation (5) is flexible enough to address changes in technical requirements, like increased temperature, power, or voltage, as well as to differentiate between customer severity usages, while being consistent with field warranty data. In addition, the effect of unknown π−multipliers can be minimized if they are assigned the same values while calculating λPM and λf. This technique can be easily comprised into spreadsheet format with paste functions and lookup tables. The adjusted failure rates can be fed into PRISM®, BlockSim®, or any other commercially available reliability prediction software intended for system analysis. Practical applications of the method can be demonstrated with the following example.
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λcapacitor = λbπ CV π SRπ Qπ E
5. EXAMPLE All the actual numbers in this example were modified due to proprietary nature of this data. During the initial product development stage an automotive OEM customer requested a design review with reliability prediction for a new model year climate control unit. Among the multitude of various electronic components the unit also containes several 1500 µF electrolytic capacitors. A reliability prediction of the entire system would obviously include reliability calculations of each system component, however, for the reason of simplicity, we would like to demonstrate the proposed technique applied only to that particular part. The climate control unit is designed to be mounted and operated in the passenger compartment of a car or a light duty truck. Other specifications for the capacitor include operating voltage of 20V with a maximum rated voltage of 24V, maximum rated temperature TMR = 80° C and varying with usage ambient temperature presented in Table 2. Analysis of 5-year automotive warranty returns for various leaded electrolytic capacitors revealed the historical failure rate of λ0 = 0.000172 failures per million hours. Table 2. Ambient temperature TA usage requirements Portion of Average TA Ambient time spent Temperature Range, for this TA (° C) range (° C) SPEC 1: -40 to -10 -25 10% SPEC 2: -10 to 20 5 30% SPEC 3: 20 to 50 35 40% SPEC 4: 50 to 80 65 20% After collecting information from designers and component engineers the following parameters were identified as typical. The average capacitance for the parts C = 1000 µF, operating voltage 22V, and ambient temperature TA = 25° C. In MILHDBK-217 the automotive environment corresponds to “Ground Mobil” and the typical maximum rated temperature for this part family TMR = 85° C According to MIL-HDBK-217, the expected failure rate for a leaded electrolytic capacitor can be calculated by equation (6).
(6)
Where base failure rate
273 + T 9 S 3 A λb = 0.00375 + 1 × exp 2.6 273 + TMR 0.4 (7) and S = Ratio of Operating to Rated Voltage. πCV = 1.0×C 0.12, Capacitance factor. C = Capacitance (µF). πSR = Series resistance factor, obtained from the lookup table based on the value of CR, the circuit resistance. CR = Ratio of effective resistance between capacitor and power supply to voltage applied to capacitor. πE = Environmental factor, obtained from the corresponding lookup table πQ = Quality factor, obtained from the quality lookup table based on the class category of the component. Though seemingly cumbersome, the equations (6) and (7), with all the accompanying tables, can be easily programmed into Excel spreadsheet or other type of computational tool. This example’s data is summarized in the spreadsheet Table 3 below Based on the temperature usage requirements (TA) listed in Table 2, the average failure rate for this component can be obtained by multiplying each λf associated with SPEC 1, SPEC 2, SPEC 3, and SPEC4, from the last column of Table 3, by the respective percent usage from the last column of Table 2. Thus, the solution for the average component’s failure rate would be: λf = (2.306E-5)×10%+(2.801E-5)×30%+(4.433E-5)×40% + 0.0001201×20%=0.0000525 failures per million hours. The obtained failure rate accounts for the variations in ambient temperature, according to Table 2, while being consistent with field warranty data for the electrolytic capacitor family.
Table 3. Calculation of warranty adjusted λf. Ratio of
Capacitance Operating to Rated Voltage (µF) Condition Typical 1000.00 0.917 SPEC 1 1500.00 0.833 SPEC 2 1500.00 0.833 SPEC 3 1500.00 0.833 SPEC 4 1500.00 0.833
TA TMR (°C) (°C) πE 25 -25 5 35 65
85 80 80 80 80
1.0 1.0 1.0 1.0 1.0
πQ
πcv
πSR
0.10 0.03 0.03 0.03 0.03
2.29087 2.40509 2.40509 2.40509 2.40509
0.33 0.27 0.27 0.27 0.27
λ0 for this λPM, predicted with Component MIL-HDBK-217
0.0060919 0.0008168 0.0009922 0.0015702 0.0042551
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Family
0.000172 0.000172 0.000172 0.000172 0.000172
WCF λf Adjusted Constant for Warranty 0.02823 0.02823 0.02823 0.02823 0.02823
0.000172 2.306E-05 2.801E-05 4.433E-05 0.0001201
REFERENCES: 1. 2. 3. 4.
Department of Defense, “Reliability Prediction Of Electronic Equipment”, MIL-HDBK-217F, Washington, DC., Notice 1, December 1991. D. Dylis, “PRISM: A New Approach to Reliability Prediction,” Reliability Review Vol. 21, March 2001, p.p. 5-14. P. O’Connor, “Practical Reliability Engineering” Third Edition, John Wiley & Sons, October 1992. D. Kececioglu, “Reliability Engineering Handbook,” PTR Prentice Hall, Englewood Cliffs, NJ, 1991
BIOGRAPHIES Andre V. Kleyner Delphi Delco Electronics Systems One Corporate Center, M.S. R103 Kokomo, IN 46904-9005 USA Phone: (765) 451-8070 Fax: (765) 451-9874 E-mail:
[email protected] Andre Kleyner has 17 years of experience as a mechanical engineer specializing in reliability of mechanical and electronic systems designed to operate in severe environments. He received his BS degree in Applied Mathematics and MS degree in Mechanical Engineering from St. Petersburg Polytechnic Institute in Russia, and MBA from Ball State University. Andre Kleyner is currently employed by Delphi Delco Electronics Systems as a Product Validation Architect. He is a member of ASQ and a Certified Reliability Engineer. Mark A. Bender Delphi Delco Electronics Systems One Corporate Center, M.S. 1500 Kokomo, IN 46904-9005 USA Phone: (765) 451-5666 Fax: (765) 451-5005 E-mail:
[email protected] Mark Bender started his career in 1984 at Buick Motor Company and in 1988 joined Delco Electronics, which later became part of Delphi Corporation. He received his BS degree in Electrical Engineering from Wayne State University and his MS degree in Computer Science from Southern Methodist University in Dallas. Mark Bender has 17 years of experience in the field of automotive electronics, product validation, and reliability analysis.
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