100 episodes

Reliability Engineering Basics, Statistics, Accelerated Testing, Program Assessment and Improvement.

Listen in on in depth discussions held during the live monthly Accendo Reliability webinar series. We explore topics ranging from reliability engineering basics, statistics, accelerated life testing, program assessment and improvement. Catch up or review past events below.

Accendo Reliability Webinar Series Fred Schenkelberg

    • Business

Reliability Engineering Basics, Statistics, Accelerated Testing, Program Assessment and Improvement.

Listen in on in depth discussions held during the live monthly Accendo Reliability webinar series. We explore topics ranging from reliability engineering basics, statistics, accelerated life testing, program assessment and improvement. Catch up or review past events below.

    Fundamentals of Hypothesis Testing

    Fundamentals of Hypothesis Testing

    Let's examine a handful of parametric and non-parametric comparison tools, including various hypothesis tests.

    Using FMEA Block Diagrams to Understand Your Item

    Using FMEA Block Diagrams to Understand Your Item

    This is where taking a breath and understanding our product before we build the wrong thing fast can really helpful.

    Fundamentals of Process Capability

    Fundamentals of Process Capability

    Let's explore the steps necessary to obtain valid and valuable process capability ratios. Then, let's examine how to use this information.

    Using Monte Carlo Simulation

    Using Monte Carlo Simulation

    Sometimes the equations we need to model reliability are just so complicated that we simply avoid them. Let's use Monte Carlo instead.

    Fundamentals of Control Charts

    Fundamentals of Control Charts

    Statistical control charting (SPC) is a method for monitoring and determining whether a process is in control or stable.

    AI Product Development and Deployment

    AI Product Development and Deployment

    Greg will discuss the critical architecture and design practices that need to be considered in AI product development and deployment.

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