Let’s say the motor driver board has a data sheet value for θ (commonly called MTBF) of 50,000 hours. This enables the Chinese reliability prediction standard to be used across a broad range of products that include components manufactured across the globe. Five of the most widely used Reliability Prediction standards for reliability analysis. An example equation from China’s GJB/z 299C, the latest version, for Bipolar Digital Circuits is: λp= πQ * [C1 * πT * πV + (C2 + C3) * πE] * πL, λp is the failure rate in failures/million hours (or failures/10e6 hours, or FPMH). Failure rate (FIT or λ-value) Each component has a failure rate curve in the shape of a bath tube, called Weibull distribution. A comparison between the approximation and the actual probability of failure is shown in Table 1, where the value of the failure rate is 0.001 failing/hour (which equates to a mean time to failure of 1000 hours). Unless you have a contractual requirement to use a specific standard, the selection of the reliability prediction standard should be based on your particular needs related to the design in question. However, this is variable and depends on the devices in your system. This becomes the instantaneous failure rate or we say instantaneous hazard rate as. The variables used in the reliability calculation formulas to calculate component failure rates vary, but include data such as device ratings, temperatures, operating parameters, and environmental conditions. The Part Stress section leads off the document and includes a number of equations that predict the failure rate for a wide variety of electrical components. The parameter defining a machine’s reliability is the failure rate (λ), and this value is the characteristic of breakdown occurrence frequency. This will allow us to obtain an expression for the CDF in terms of failure rate that we can use to illustrate the difference between the two functions. China’s GJB/z 299 Reliability Prediction standard has its roots in MIL-HDBK-217 and has been developed to align with the procedures and devices found in China. MIL-HDBK-217 is one of the most widely known Reliability Prediction standards. Although it was a useful approximation when it was first presented, it applies only for a constant failure rate model and only when the product λt is small. Failure probability can be computed from the PC expansion of the QoI. When evaluating the similarities and differences between the reliability prediction methods, it is most useful to delve into the calculations used to predict failure rate presented in the various standards. It was originally developed and published for use by the Department of Defense. Additionally, Telcordia includes the ability to calculate an infant mortality failure rates. For the equation above, the following list describes the variables: The equations, the variables, and the data parameters vary based on the specific device being modeled. For the following calculations 1 failure per million hours was assumed. In many cases, 217Plus failure rate predictions are not as pessimistic as MIL-HDBK-217. Correlate the test scores of the two tests. All reliability predictions standards mentioned here are widely known and accepted. There are two primary sections in the MIL-HDBK-217 standard: the Part Stress section and the Parts Count section. The failure rate of the unit is used to calculate the reliability of the unit at different time points. ... We developed a checklist to assess videos of team training and I want to calculate interrater-reliability. While most of the key device types are covered in all, there is variation. This article provides an overview of the most commonly used reliability prediction standards. An example equation for capacitors in 217Plus 2015 Notice 1 is: λP =πG* πC * (λOB * πDCO * πTO * πS + λEB * πDCN * πTE + λTCB * πCR * πDT ) + λSJB * πSJDT + λIND. The failure rate function, also called the instantaneous failure rate or the hazard rate, is denoted by λ(t). It can be calculated by deducting the start of Uptime after the last failure from the start of Downtime after the last failure. These equations were built by analyzing a huge amount of field data over a long period of time. How do I choose which Reliability Prediction method to use? Failure Rate is a simple calculation derived by taking the inverse of the mean time between failures: Failure Rate is a common tool to use when planning and designing systems, it allows you to predict a component or systems performance. In summation, some of the unique features of Telcordia include: The 217Plus™ reliability prediction standard was developed by Quanterion Solutions. Reliability function: R(t) = P(T > t) = ∫1 t f(x)dx = 1 F(t) = probability of surviving at least till age t where F(t) is the cumulative distribution function. This ability to perform design trade-off analysis with metric-based assessments empowers you to make the best decisions for your business. • To calculate: Administer the two tests to the same participants within a short period of time. The current release of MIL-HDBK-217 is MIL-HDBK-217F Notice 2. Bounds on Time and Reliability. MIL-HDBK-217 failure rate predictions are generally more pessimistic than Telcordia and 217Plus reliability predictions. In this case, you get coverage of almost all device types used in product design. By factoring in test data information, your predictions are weighted according to the amount of test data you have. An example of an increasing failure rate function is shown in Figure 3. The NPRD and EPRD databases include failure data on a wide range of electrical components and electromechanical parts and assemblies. Even though MTBF and reliability are different, you can very easily convert MTBF to reliability by using this equation for exponential distributions: In this equation: At the system level, 217Plus can incorporate environmental stresses, operating profile factors, and process grades. The Part Stress section of MIL-HDBK-217 includes complete details on all the equations and how to assess the variables used in the equations. Ready to take your reliability education further. Stay up-to-date by subscribing today. China’s GJB/z 299 is the most widely used Reliability Prediction standard in the extensive Chinese market. Reliability Prediction’s historical roots are in the military and defense sector, but over the years have been adapted and broadened for use in a wide range of industries. It is now used throughout a broad range of industries, including those related to military and defense applications. Calculating Failure Rates with Reliability Prediction Software, Relyence Reliability Prediction: Leading the Way in Reliability Prediction Analytics Part 1, How to Perform Reliability Predictions Easily and Efficiently, How Your Reliability Tools Can Work Together to Achieve Maximum Results, An Overview of Reliability Analysis Tools and Techniques, Compute the upper confidence level of steady state failure rates, Integrate laboratory data from devices, units, or systems with or without burn-in data, Integrate field data from devices, units, or systems with or without burn-in data, Determine early life factors based on no burn-in, limited burn-in, or extensive burn-in. In this article, we discussed the probability density function, unreliability function, reliability function, failure rate function and the relationships between them. For example, many military and defense-based contracts will require you to use MIL-HDBK-217. The MIL-HDBK-217 Reliability Prediction Standard, China’s GJB/z 299 Reliability Prediction Standard. If using failure rate, la… The latest versions of these databases, NPRD-2016 and EPRD-2014, can be used alongside the prediction standards and work well together. Essentially, the advantages afforded by reliability prediction analyses make it an important part of managing and maintaining reliability and quality objectives. The failure rate estimates from China’s GJB/z 299 tend to be very divergent from the other standards, especially for microelectronic devices. Example Telcordia formulas to compute the black-box steady state failure rate of a device are: λ BB = λ G * π Q * π S * π T. where λ BB is the failure rate in failures per billion hours (failures/10e 9 hours, or FITs) and. At the early design stage, Reliability Predictions enable you to perform an assessment of likely failure rate characteristics. The component or assembly failure rate obtained on field-based failures can then be used in your reliability prediction. Assume that the objective of an analysis is to determine the unreliability at the end of a 300-hour product warranty. You can also mix and match standards. Failure data spans a variety of environments and quality levels, allowing you to select components that most accurately reflect your usage. Oftentimes, NPRD-2016 and/or EPRD-2014 can be used to include failure rate estimates for devices not modeled in the prediction standards. Or, one of the reliability standards may be more commonly used in your industry. Its revisions and updates are designated with suffix notations similar to MIL-HDBK-217. How To Use Failure Rate Predictions to Improve Reliability. 217Plus can adjust failure rate estimates based on process grades. ReliaSoft products and services empower reliability professionals worldwide by promoting efficiency and innovation. The conditional probability of failure is more popular with reliability practitioners and is used in RCM books such as those of N&H and Moubray. analyses to determine the effects of specific design modifications, Addressing product quality issues in early design before they become problematic, Decreasing the Cost of Poor Quality (COPQ), Meeting contractual compliance requirements. A closer look at the failure rate function was presented to illustrate why the unreliability function is preferred over a common approximation using the failure rate function for calculation of reliability metrics. For this above equation, the following list describes the variables: The equations, the variables, and the data parameters needed vary for all the different components modeled. For example, the equation for Microcircuits, Gate/Logic Arrays and Microprocessors is: where λp is the failure rate in failures/million hours (or failures/10e6 hours, or FPMH). It was one of the first models developed, and many other reliability standards available today have their roots in MIL-HDBK-217. Early on, Telcordia was referred to as the Bellcore standard. The choice is based on particular requirements. What are the Primary Reliability Prediction Standards? Pr ⁡ ( T ≤ t ) = F ( t ) = 1 − R ( t ) , t ≥ 0. The failure rate of nonlife test units represented by a visual Type 5 operator is set to 0. This period is usually given the most consideration during design stage and is the most significant period for reliability prediction and evaluation activities. There are several widely accepted Reliability Prediction standards including: Additionally, component databases NPRD (Non-electronic Parts Reliability Data)  and EPRD (Electronic Parts Reliability Data) are often used in conjunction with the Reliability Prediction standards to augment prediction analyses. The CDF can be computed by finding the area under the pdf to the left of a specified time, or: Conversely, if the unreliability function is known, the pdf can be obtained as: The reliability function, also called the survivor function or the probability of success, is denoted by R(t). The best tools will even allow you to mix and match standards, provide built-in component libraries, and enable you to view how design changes impact reliability. A mistake that is often made when calculating reliability metrics is trying to use the failure rate function instead of the probability of failure function (CDF). Failure Probability. These two functions, along with the probability density function (pdf) and the reliability function, make up the four functions that are commonly used to describe reliability data. The most recent China GJB/z standard is China’s GJB/z 299C. These types of failures are typically caused by mechanisms like design errors, poor quality control or material defects. The pdf is the curve that results as the bin size approaches zero, as shown in Figure 1(c). Oftentimes you may not have a choice: there may be contractual requirements, or the model choice may be selected by a systems integrator, or it may be set by a reliability group. Enter the number of hours and iterate the failure rate until the Reliability equals 99.9%. Statistical analysis was then used to determine the equations which best modeled the failure characteristics of the accumulated data. 10,250 x 0.41 = 4202.5 or just over 4,000 fan failures. By adjusting your failure rates based on this real-world information, your predictions will more accurately reflect your product performance. For example, consider a data set of 100 failure times. Additionally, the πE, which factors in the environmental condition, is factored into the overall failure rate calculation. Therefore, it is recommended that the CDF should be used for calculations of unreliability at a given time and the time at which a given unreliability occurs, and the failure rate function should be used only as an aid to understand if the model used to fit the data is consistent with the types of failure modes observed or expected for the component. It represents the probability of failure per unit time, t, given that the component has already survived to time t. Mathematically, the failure rate function is a conditional form of … Then, as the product design becomes more solidified and data parameters are established, the Parts Count prediction is moved over to Part Stress, maintaining all the data already entered during the Parts Count assessment. The amount of data parameters for devices does vary by device type, but overall, Telcordia generally asks for less data and MIL-HDBK-217 and 217Plus require more. It should be noted that any of this additional data is not required to perform a reliability prediction based on the Telcordia standard. Thus, the total reliability for 2 pumps in paralel, R T =1-(0.05*0.05)=0.9975. If any one of the four functions presented above is known, the remaining three can be obtained. The Parts Count reliability prediction is useful in early design stages when the design is still in progress and not all operating parameters are known. 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