How do you compare and contrast Weibull analysis with other reliability methods?

People usually call it Weibull analysis, but many other types of statistical distributions can be used as well (log-normal, logistic, log-logistic, etc.). They can be used to derive the same types of information (failure rates, mean life, etc.) as the Weibull. The Weibull is most frequently cited because of its flexibility in modeling different statistical behaviors. However, there are circumstances where those other distribution types are more appropriate.

It is worth mentioning or discussing 3-parameter weibull since the article gives an impression that it is always 2-p weibull. Also it would be insightful to explain what the parameters mean.

Mixtures of distributions can also be modeled. For example, on a car tire, there are chance failures of punctures (Weibull slope around 1), followed by a steeper slope where the tires wear out. Models can fit data using rank regression or maximum likelihood estimation techniques. Other mixture examples include 1 tray of bearing races that misses heat treat.

Benefits of getting the shape parameter beta is it can help reduce test times based on test time and sample size for components that follow weibull distribution. Otherwise typically exponential model is followed for testing which can extend test time duration. Knowing shape parameter beta helps to reduce or accurately determine test time.

It is possible to make Weibull analysis more sophisticated. One way of doing it is to make the two Weibull parameters functions of the operational parameters. Function coefficient can be estimated by using operational history.

Fifth, it can be made adaptive to incorporate new data as it is generated (I.e. Bayesian analysis). It has capabilities to incorporate modern AI techniques or hybrid field physics models or use it in traditional way.

While some user's experiences suggest that Weibull analysis is best used with small sample sizes, it has been used successfully for very large data sets as well (thousands of failures and many times more suspensions).

SAE paper a few years ago showed the best way to compare 2 distributions is with the Likelihood Ratio test, or Likelihood contour test. This is also in SuperSMITH(R) Software and "The New Weibull Handbook" There is also a Monte Carlo based "pve" test to determine if a model is a reasonable fit. This can also be used to compare Weibull to Log-Normal to see if one or the other should be used.

The parameters derived from the Weibull analysis represent the underlying physics of the part, the operating environment, and the degradation process that leads to the failures. When a part experiences more than one failure mechanism, it's very important to partition the failure data into separate "buckets" so they can be analyzed individually. Failing to do so can create confusing or even misleading results.

O ponto crucial é seguir um processo estruturado, come?ando pela coleta de dados confiáveis sobre o tempo até a falha ou a vida útil de componentes ou sistemas. Esses dados podem ser obtidos através de testes de vida acelerada, histórico de manuten??o ou dados operacionais. A amostragem deve ser suficientemente representativa para garantir que os resultados da análise sejam robustos. Após a coleta dos dados, o próximo passo é realizar a estimativa dos parametros de forma e escala da distribui??o de Weibull. A determina??o precisa desses parametros é fundamental, pois influencia diretamente as previs?es de confiabilidade e manuten??o. Uma prática recomendada é fazer uso de software especializado, como Minitab ou ReliaSoft.

One customer looked at Joint Angulating Torque on a haftshaft (front drive shaft on a car). When the Torques were fit to a Normal model, the process was not capable, and 100% inspection was suggested. A review showed that the Weibull model was a better fit, and the process was capable. This was a new concept to the customer, so they agreed to a sampling of so many pieces/hour, rather than 100%. There were no parts outside of specifications found.

Performing root cause analysis and developing knowledge about various failure modes can enhance the effectiveness of the analysis. Effectiveness of Weibull analysis can be increased multifold by incorporating modern techniques.

When performed on an entire system, the distribution parameters for each component and failure mechanisms can be utilized in a Reliability Block Diagram (RBD) model to simulate the aging of one or more systems. The RBD model results can then be used to forecast maintenance workload, logistics demands, and equipment availability.

Weibull analysis provides insights into failure rates over time, whereas other methods like FMEA focus on identifying potential failure modes. In renewable energy, Weibull analysis predicts wind turbine failures, while FMEA assesses failure modes in manufacturing processes. Grid code compliance studies may use both Weibull analysis and FMEA to ensure reliability. Battery storage systems utilize Weibull analysis to predict cell degradation, while reliability block diagrams analyze system reliability. Each method offers unique perspectives on reliability, allowing for comprehensive risk management in various industries.

Great text is "The New Weibull Handbook" by Dr. Robert Abernethy, available on Amazon. Demo software available at www.BathtubSoftware.com, along with a tutorial guide. Classes available from SAE, Quanterion, University of Tennessee.