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Weibull Analysis and Zero-time Failures with ReliaSoft Weibull++

Introduction

Explore the application of Weibull analysis in identifying and addressing zero-time failures. By examining real-world scenarios, it demonstrates how this statistical method helps predict failure patterns, enhance reliability, and optimize maintenance strategies. Discover how Weibull analysis can be a game-changer in managing asset performance and preventing unexpected downtime.

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  • Processing zero-time failures in life data analysis can be a challenging task with much uncertainty.
  • There is no established method of how and when to include time-zero data for life analysis.
  • Predict reliability by understanding the failure modes, their root causes, relevance to the field and other accompanying factors, such as manufacturing. Tests, stress screening, and accumulated time of the degradation, among others.
  • Improve reliability predictions by incorporating zero-time-to-failure data.
  • Quickly assess different approaches for handling zero-time data.
  • Easily manipulate data and display multiple scenarios for results comparison.

Further information

Life data analysts often face the challenge of zero-time failures, which are defined as failures occurring before a product is in the hands of the end customer or out-of-box failures. Processing zero-time failure data can be a complex process since most statistical distributions and analysis software packages do not handle such data well. The analyst must thoroughly understand the appropriateness and implications of including zero-time failures when analyzing life data. Incorrect usage can result in inaccurate forecast results and potentially negative engineering and financial consequences. Simply plugging numbers into a software program will not always result in satisfactory results if the background engineering analysis is not performed as well. The analyst must determine if the zero-time failure data should be used as presented, transformed according to known field exposure correlations or ignored all together.

This presentation explores several types of zero-time failures, how to determine whether to include zero-time failures in analysis, how to transform zero-time failures into life data, and the effects on the analysis results. Examples are provided, using both simulated and real-life data, illustrating the effect of zero-time failures along with discussion on when and how to include in a life data analysis for the best possible results.

Technology used

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