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Mean Time Between Failures
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Product reliability performance is a major consideration for technology firms. It affects the entire company's bottom line. Poor product reliability may raise the following questions: Will your warranty costs exceed the total predicted costs? Will the firm lose valuable reputation?
Reliability is defined as the probability that an item, product or a system will perform a required function under stated conditions for a stated period of time. A reliability prediction can be stated as the average time (usually expressed in hours) that a part, a component or a system works without failure.
A reliability prediction is usually based on an established model described in MIL-HDBK-217, Bellcore, or some other model before the product is manufactured or marketed. The model can predict MTBF using as little data as the part type and count information. As the design progresses, the MTBF model can be updated to include thermal and electrical stress analysis information.
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Key benefits of MTBF prediction:
- Meets reliability objectives
- Saves money on all life cycle costs
- Optimizes maintenance
- Maximizes availability
- Decreases time to market
- Improves product design
- Reduces company warranty and repair costs
- Improves product safety
- Ensures return on investment
| MTBF Prediction:
Failure rate and MTBF calculation for components and blocks using prediction methods and conditions
- Predicts Failure Rates according to:
- MIL-HDBK-217F Notice 2
- 217Plus
- British-Telecom HRD5
- Siemens SN-29500
- IEC62380 - RDF2000 / UTEC80810
- NSWC98 mechanical
- Non-Operating (RAC Tool kit)
- Bellcore Issue 6
- CNET 2000
- Chinese GJB299
- FIDES, available soon
- Component libraries (only for M217, HRD5 and Bellcore)
- Interface for direct import from CAD/CAE interfaces (Importing your part list into CARE-MTBF)
- Mentor Graphics
- Cadence
- Or-Cad
- Excel CSV
- ERP – SAP
- ERP – MFG-Pro
- Global change, optimization and curve sensitivity for: Ambient/Case temperature, Quality-Levels, Environments and Prediction-Methods
- Handles Project Trees, CORE Database & the HTML report generator
- User defined prediction models for components and assemblies, based on field data
- 3 Pareto tables by: Reference-Designator, Part-Numbers & Part-Categories
MTBF Allocation
Top-Down Allocation Algorithm for RAMS requirements-Serial Model.
For redundant models the RBD module is used.
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