CModel Class

Model allows you to choose or create a model to describe the behavior associated with the URD. This can be a reliability model, a probability of failure model or an event occurrence model.

Constructors

• New cModel Creates a new model.

Properties

• Name Sets or returns the name of the model (the one used in the repository)
• ID Returns the ID of the model (the one used in the repository).
• ProjectID Returns the ID of the project the resource belongs to.
• Type Returns the type of the model.
• Category Returns the category of the model.
• Designation Returns the designation of the model (for example Weibull-2).
• ErrorHappened Whether or not the last calculation produced an error.
• ConfLevel Returns the confidence level that is currently used for calculations.

Methods

• SetModel Sets a new model.
• Reliability Calculates the Reliability for a given time.
• Unreliability Calculates the Unreliability for a given time.
• Time Calculates the time value for a given Reliability.
• MeanTime Calculates the mean time value for a given Reliability.
• Pdf Calculates the PDF.
• FailureRate Fits a model from the raw data previously entered in the data set. Sets FittedModel property if successful. Clears it (sets to Nothing) in case of an error.
• Parameters Finds the distribution that fits the data best. Sets FittedModel property if successful. Clears it (sets to Nothing) in case of an error.
• Bounds_Reliability Returns the bounds on the reliability given time.
• Bounds_Unreliability Adds a free-form data point to the collection of data points in the data set.
• Bounds_Time Fits a model from the raw data previously entered in the data set. Sets FittedModel property if successful. Clears it (sets to Nothing) in case of an error.
• Bounds_MeanTime Finds the distribution that fits the data best. Sets FittedModel property if successful. Clears it (sets to Nothing) in case of an error.
• Bounds_FailureRate Adds a free-form data point to the collection of data points in the data set.
• Bounds_Parameters Fits a model from the raw data previously entered in the data set. Sets FittedModel property if successful. Clears it (sets to Nothing) in case of an error.
• SetConfindenceLevel Finds the distribution that fits the data best. Sets FittedModel property if successful. Clears it (sets to Nothing) in case of an error.