Telcordia Sr 332 Handbook 2

Software Used In today's competitive electronic products market, having higher reliability than competitors is one of the key factors for success. To obtain high product reliability, consideration of reliability issues should be integrated from the very beginning of the design phase. This leads to the concept of reliability prediction. Historically, this term has been used to denote the process of applying mathematical models and component data for the purpose of estimating the field reliability of a system before failure data are available for the system. However, the objective of reliability prediction is not limited to predicting whether reliability goals, such as MTBF, can be reached. It can also be used for: • Identifying potential design weaknesses • Evaluating the feasibility of a design • Comparing different designs and life-cycle costs • Providing models for system reliability/availability analysis • Establishing goals for reliability tests • Aiding in business decisions such as budget allocation and scheduling Once the prototype of a product is available, lab tests can be utilized to obtain more accurate reliability predictions. Accurate prediction of the reliability of electronic products requires knowledge of the components, the design, the manufacturing process and the expected operating conditions.

This article illustrates how reliability prediction methods for electronic products can improve the competitiveness of a product. The MIL-217, Bellcore/Telcordia and. What's in Telcordia SR-332? The Telcordia Standard allows reliability predictions to be performed using three methods: Method I provides predictions based on a Parts Count procedure. Method II provides predictions based on combining laboratory test data with Parts Count data. Method III provides predictions based on.

Several different approaches have been developed to achieve the reliability prediction of electronic systems and components. Each approach has its unique advantages and disadvantages. Among these approaches, three main categories are often used within government and industry: empirical (standards based), physics of failure and life testing. In this article, we will provide an overview of all three approaches. First, we will discuss empirical prediction methods, which are based on the experiences of engineers and on historical data. Standards, such as MIL-HDBK-217 and Bellcore/Telcordia, are widely used for reliability prediction of electronic products. Next, we will discuss physics of failure methods, which are based on root-cause analysis of failure mechanisms, failure modes and stresses.

This approach is based upon an understanding of the physical properties of the materials, operation processes and technologies used in the design. Finally, we will discuss life testing methods, which are used to determine reliability by testing a relatively large number of samples at their specified operation stresses or higher stresses and using statistical models to analyze the data.

Empirical (or Standards Based) Prediction Methods Empirical prediction methods are based on models developed from statistical curve fitting of historical failure data, which may have been collected in the field, in-house or from manufacturers. These methods tend to present good estimates of reliability for similar or slightly modified parts. Some parameters in the curve function can be modified by integrating engineering knowledge. The assumption is made that system or equipment failure causes are inherently linked to components whose failures are independent of each other. There are many different empirical methods that have been created for specific applications. Some have gained popularity within industry in the past three decades. The table below lists some of the available prediction standards and the following sections describe two of the most commonly used methods in a bit more detail.

Prediction Method Applied Industry Last Update MIL-HDBK-217F and Notice 1 and 2 Military 1995 Bellcore/Telcordia Telecom 2011 NSWC Mechanical 2011 FIDES Commercial/French Military 2009 MIL-HDBK-217 Predictive Method MIL-HDBK-217 is very well known in military and commercial industries. It is probably the most internationally recognized empirical prediction method, by far. The latest version is MIL-HDBK-217F, which was released in 1991 and had two revisions: Notice 1 in 1992 and Notice 2 in 1995.

The MIL-HDBK-217 predictive method consists of two parts; one is known as the parts count method and the other is called the part stress method [1]. The parts count method assumes typical operating conditions of part complexity, ambient temperature, various electrical stresses, operation mode and environment (called reference conditions). The failure rate for a part under the reference conditions is calculated as: where: • λ ref is the failure rate under the reference conditions • i is the number of parts Since the parts may not operate under the reference conditions, the real operating conditions will result in failure rates that are different from those given by the 'parts count' method. Therefore, the part stress method requires the specific part’s complexity, application stresses, environmental factors, etc. (called Pi factors).

For example, MIL-HDBK-217 provides many environmental conditions (expressed as π E) ranging from 'ground benign' to 'cannon launch.' The standard also provides multi-level quality specifications (expressed as π Q). The failure rate for parts under specific operating conditions can be calculated as: where: • π S is the stress factor • π T is the temperature factor • π E is the environment factor • π Q is the quality factor • π A is the adjustment factor Figure 1 shows an example using the MIL-HDBK-217 method (in ReliaSoft’s software) to predict the failure rate of a ceramic capacitor. According to the handbook, the failure rate of a commercial ceramic capacitor of 0.00068 μF capacitance with 80% operation voltage, working under 30 degrees ambient temperature and 'ground benign' environment is 0.0217 / 10 6 hours. The corresponding MTBF (mean time before failure) or MTTF (mean time to failure) is estimated to be 4.6140 / 10 7 hours.

Figure 1: MIL-HDBK-217 capacitor failure rate example Bellcore/Telcordia Predictive Method Bellcore was a telecommunications research and development company that provided joint R&D and standards setting for AT&T and its co-owners. Because of dissatisfaction with military handbook methods for their commercial products, Bellcore designed its own reliability prediction standard for commercial telecommunication products. In 1997, the company was acquired by Science Applications International Corporation (SAIC) and the company's name was changed to Telcordia. Telcordia continues to revise and update the standard. The latest two updates are SR-332 Issue 2 (September 2006) and SR-332 Issue 3 (January 2011), both called 'Reliability Prediction Procedure for Electronic Equipment.' The Bellcore/Telcordia standard assumes a serial model for electronic parts and it addresses failure rates at the infant mortality stage and at the steady-state stage with Methods I, II and III [2-3].

Method I is similar to the MIL-HDBK-217F parts count and part stress methods. The standard provides the generic failure rates and three part stress factors: device quality factor ( π Q), electrical stress factor ( π S) and temperature stress factor ( T). Method II is based on combining Method I predictions with data from laboratory tests performed in accordance with specific SR-332 criteria. Method III is a statistical prediction of failure rate based on field tracking data collected in accordance with specific SR-332 criteria. In Method III, the predicted failure rate is a weighted average of the generic steady-state failure rate and the field failure rate.

Figure 2 shows an example in Lambda Predict using SR-332 Issue 3 to predict the failure rate of the same capacitor in the previous MIL-HDBK-217 example (shown in Figure 1). The failure rate is 9.655 Fits, which is 9.655 / 10 9 hours. In order to compare the predicted results from MIL-HBK-217 and Bellcore SR-332, we must convert the failure rate to the same units. 9.655 Fits is 0.0009655 / 10 6 hours. So the result of 0.0217 / 10 6 hours in MIL-HDBK-217 is much higher than the result in Bellcore/Telcordia SR-332. There are reasons for this variation. First, MIL-HDBK-217 is a standard used in the military so it is more conservative than the commercial standard.

Second, the underlying methods are different and more factors that may affect the failure rate are considered in MIL-HDBK-217. Figure 2: Bellcore capacitor failure rate example Discussion of Empirical Methods Although empirical prediction standards have been used for many years, it is always wise to use them with caution. The advantages and disadvantages of empirical methods have been discussed a lot in the past three decades. A brief summary from the publications in industry, military and academia is presented next [5-9].

• • • • Reliability Prediction for Mean Time Between Failures Reliability Prediction tools such as ITEM ToolKit are absolutely essential when the reliability of your electronic and mechanical components, systems and projects is critical for mission success. When you develop products and systems for commercial, military, or any other application, you need to ensure reliability and consistent performance. Electronics and Mechanical products, systems, and components are naturally prone to eventual breakdown owing to the number of environmental variables, heat, stress and moving parts.

The main question is 'When?' Reliability is a measure of the frequency of failures over time. System reliability has a major impact on maintenance and repair costs as well as the continuity of service and customer satisfaction. The Role of Reliability Prediction During the reliability analysis or process, reliability prediction or MTBF (Mean Time Between Failures) has many functions and is often the foundation for any analysis. Whether you're designing new or updating an existing system, ITEM ToolKit can assist in determining the impact of proposed design changes. It also provides a deeper understanding of acceptable reliability levels under environmental extremes.

You can evaluate acceptable limits of failure for your system, or meet overall design goals and the requirements of your clients as well. The five ITEM ToolKit reliability prediction modules provide powerful and competitive advantages, for example: • Combine prediction methods for complex analysis • Optimize designs to meet targeted goals • Select components with regard to reliability and cost savings • Be more accurate and efficient than with manual methods • Take advantage of powerful 'what if' analytical tools ITEM ToolKit offers the greatest flexibility and ease of use in 5 reliability prediction modules. The modules MIL-217, Telcordia (Bellcore), NSWC, IEC 62380 (RDF) and China 299B all share a powerful set of features and capabilities for inputting and utilizing data in multiple operations.

Now you can generate the most complete analysis for your purposes. Perfect for both military and commercial applications.

The ITEM ToolKit reliability prediction modules can aid in locating areas for potential reliability improvement. The software offers the most advance and diverse Multi-Document Interface (MDI) features allowing you to construct and analyze your system with accuracy and speed. The Reliability Software modules of ITEM ToolKit provide a user-friendly interface that allows you to construct, analyze, and display system models using the interactive facilities. Building a hierarchies and adding new components could not be easier. ToolKit calculates the failure rates, including mean time between failure (MTBF), associated with new components as they are added to the system, along with the overall system failure rate. Project data may be viewed both via grid view or dialog view simultaneously, allowing predictions to be performed with a minimum of effort. Each reliability prediction module is designed to analyze and calculate component, sub system and system failure rates in accordance with the appropriate standard.

After the analysis is complete, ITEM ToolKit's integrated environment comes into its own with powerful conversion facilities to transfer data to other reliability software modules of the program. For example, transfer your MIL-217 project data to FMECA or your Bellcore project to RBD. These powerful facilities transfer as much of the available information as possible, saving you valuable time and effort.

Hierarchy Diagrams Users can interactively construct hierarchy diagrams that represent the structure of a system at various hierarchical levels. As new components are added to the system, each module automatically calculates and updates all dependent and overall failure rates. Parts Count & Parts Stress Analysis When adding components to your system, ITEM ToolKit automatically employs the applicable default values (Parts Count). The Parts Count generally requires less information such as part quantities, quality levels and the application environment. It is most applicable early in the design phase and proposal formulation. You have the option of modifying these values to meet specific system or project requirements (Parts Stress). Cnc Software.

The Part Stress Analysis requires more detailed information and is usually applicable later in the design phase. MTBF & Failure Rate Calculations MTBF and Failure rates are automatically computed and displayed for all levels of systems and projects. Redundancy and Repairable Calculations Each reliability software module of ITEM ToolKit includes redundancy and repairable options for calculations of availability and failure rates at block and system levels. User Defined Linked Blocks A Linked Block is a graphical representation of an existing block that assumes the exact characteristics of another block in your System. Linked Blocks enable you to reduce repetitive data entry.

Changes made to the source block will automatically update in the Linked Block. Pi Factors Each module calculates and makes visible the various Pi Factors used to calculate the Failure Rates for the Component categories per the applicable standard being used. 'What If' Study 'What-if' studies allow you to preview and evaluate the feasibility and quality of your design and the selection of your components.

This allows you model the system, change components, and see the effects without having to construct an actual system. External Arrhenius Temperature Model for User Defined Failure Rates For some designs you use a component which cannot be modeled using a Component Category known to the standard, or you have a failure rate from a manufacturer of a subassembly. By using the External component and the Arrhenius temperature model, you can introduce a non-standard component into you analysis, and vary the failure rate with temperature via the Arrhenius temperature formula. Reliability Allocation Allocation models logically apportion the product design reliability into lower level design criteria, such that the cumulative reliability still meets the requirements. ITEM ToolKit performs allocation analysis at two levels, project and system level.

ITEM ToolKit contains the following five allocation models: • Equal Allocation • AGREE Allocation • Feasibility of Objective Allocation • ARINC Apportionment Technique • Repairable Systems Allocation Derating Derating is the selection and application of parts and materials so that the applied stress is less than rated for a specific application. For example, derating is the negative slope of a power-versus-temperature graph. It shows that as the operating ambient temperature increases, the output power of a particular component drops to ensure reliable system operation. Derating curves provide a quick way to estimate the maximum output power of a device at a given temperature. Following are the commonly used derating standards that are included within ITEM ToolKit: • NAVSEA TE000-AB-GTP-010 • MIL-HDBK-1547 • MIL-STD-975M (NASA) • NAVAIR-AS-4613 Class A • NAVAIR-AS-4613 Class B • NAVAIR-AS-4613 Class C • User Defined Derating Files. The MIL-HDBK-217 Module of ITEM ToolKit is a powerful reliability prediction program based on the internationally recognized method of calculating electronic equipment reliability defined in MIL- HDBK-217 (published by the US Department of Defense). This standard uses a series of models for various categories of electronic, electrical and electro-mechanical components to predict failure rates that are affected by environmental conditions, quality levels, stress conditions and various other parameters.

These models are fully detailed within MIL-HDBK-217. . The IEC 62380 module supports reliability prediction methods based on the latest European Reliability Prediction Standard. Originally, a French Standard published by the Union Technique de L'Electricite (UTE, July 2000 - RDF). The standard has evolved and become the European Standard for Reliability Prediction (IEC 62380). Its unique approach and methodology has gained worldwide recognition. Download Font Untuk Android Apk here. IEC 62380 is a significant step forward in reliability prediction when compared to older reliability standards. .

The Telcordia Software Module of ITEM ToolKit calculates the reliability prediction of electronic equipment based on the Telcordia (Bellcore) TR-332 and SR-332 standards. These standards use a series of models for various categories of electronic, electrical and electro-mechanical components to predict steady-state failure rates which environmental conditions, quality levels, electrical stress conditions and various other parameters affect. It provides predictions at the component level, system level or project level for COTS (Commercial Off-The-Shelf Parts). .