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Reliability Engineering Data Analysis and Modeling Training

Reliability Engineering Data Analysis and Modeling Training covers Weibull, life data analysis, accelerated life testing (ALT), Design of Experiments (DOE), Reliability Block Diagram (RNDs), and repairable systems modeling.

Reliability Engineering

Reliability Engineering Data Analysis and Modeling Training will coach you through principal methods of reliability analysis, including fault tree and reliability block diagrams, Failure Mode and Effects Analysis (FMEA), event tree construction and evaluation, reliability data collection and analysis, methods of modeling systems for reliability analysis.

Reliability Engineering Data Analysis and Modeling Training also will discuss the fundamental probability and statistics, application of selected mathematical techniques to the analysis and solution of reliability engineering problems, applications of matrices, vectors, tensors, differential equations, integral transforms, and probability methods to a wide range of reliability related problems.

In addition, Reliability Engineering Data Analysis and Modeling Training will teach you the basic life model concepts; probabilistic life models; data analysis; parametric and nonparametric estimation of basic time-to-failure distributions; data analysis for systems; accelerated life models; and repairable systems modeling.

TONEX Training Features

  • All the training courses are designed half theory, half practical exercises
  • Training sessions are fun, interactive, and dynamic
  • Each training contains labs, group activities, and hands-on workshop
  • Students are encouraged to bring in their own projects to work on the class, but they also will practice with the real-life case studies that our instructors will provide
  • TONEX offers several training courses in the field of reliability engineering. You can check them HERE to find what fits your needs.

Why Do You Need this Training?

  • You will learn to use life data analysis tools to understand how a product will perform over time
  • You will learn how to establish consequential reliability targets, presenting whether an item meets the standards and/or efficiently communicating performance approximation to management
  • You will learn to determine if an item will experience infant mortality and/or wear out and to predict performance during the useful life (or warranty) period
  • You will learn how to assess suppliers and/or comparing designs based on reliability
  • You will become familiar with the usage of other crucial reliability analysis methods, including ALT, DOE, RBDs and repairable systems modeling


Reliability Engineering Data Analysis and Modeling Training is suitable for:

  • All the engineers and managers involved in reliability investigations
  • Project managers
  • Project engineers
  • Safety engineers/managers
  • Quality engineers/managers
  • Reliability engineers/managers
  • Production engineers/managers
  • Design engineers/managers
  • Warranty analysts
  • Asset reliability managers
  • Capacity and predictive maintenance engineers and managers

What Will You Learn?

  • Reliability engineering principles and methods
  • The theory of reliability engineering and associated mathematics, metrics and applications
  • Life data analysis and applications
  • Relevant statistical concepts
  • Reliability data types and censoring schemes
  • Reliability metrics and their interpretation
  • Weibull distribution
  • Other distributions
  • Parameter estimation methods
  • Confidence bounds
  • Mixed Weibull distribution
  • software failure modes analysis
  • Degradation data analysis
  • Warranty data analysis
  • Model and data set comparisons
  • Reliability test design
  • Reliability demonstration
  • Stress-strength analysis
  • Life data modeling and applications
  • Quantitative accelerated life testing (ALT) data analysis with life-stress models for one or multiple stresses
  • Design of Experiments (DOE) focusing on reliability engineering
  • System analysis, modeling and applications
  • Reliability block diagram (RBD) analysis
  • Fault tree analysis
  • System reliability equation and metrics
  • Importance analysis and metrics
  • Reliability allocation
  • Determining optimum maintenance intervals
  • Reliability, availability and maintainability analysis for complex repairable systems
  • Statistical models for repairable systems and applications
  • Recurrent event data analysis
  • Fielded system analysis and modeling

Reliability Engineering Data Analysis and Modeling Training

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