Reliability Life Data Engineering

Seminar Overview

Quantitative reliability engineering is not a software exercise — it is the discipline of extracting a defensible failure model from messy, censored, real-world data and defending that model in front of program managers, warranty-reserve accountants, and customer quality organizations. This advanced seminar is built directly from the two references that define the field — the **ReliaSoft Life Data Analysis Reference** and the **ReliaSoft Accelerated Life Testing Reference** — the primary-source material underlying the Weibull++ and ALTA analysis platforms used across the automotive, aerospace, medical, and electronics industries. The course knowledge base carries roughly 94,000 indexed passages from 450 source documents; every module in the outline below traces to specific chapters of those references, their worked datasets, and their case records.

Across three days you will move from raw failure/suspension data to fitted, bounded, and defensible life models: median-rank probability plotting and Weibull parameter estimation by rank regression and maximum likelihood; the an affiliated engineering firmial, lognormal, and normal distributions and when each is — and is not — appropriate; confidence bounds (Fisher matrix, likelihood ratio, beta-binomial); goodness-of-fit and model selection; mixed and multi-modal populations and competing failure modes; warranty and field data analysis; accelerated life testing with the Arrhenius, Eyring, inverse power law, and temperature-humidity life-stress relationships; and system-level reliability block diagrams.

The differentiator: this seminar is taught from PRIMARY SOURCE material — the actual reliability references, their derivations, worked examples, and failure records — not vendor slide decks or marketing summaries. You leave able to open any reliability dataset at your company and produce an analysis a customer's reliability group will accept.

Ideal Learner

• Reliability engineers and reliability program owners (DFMEA/DVP&R support, warranty analysis)
• Quality and supplier-quality engineers who receive field return, claim, and test-to-failure data
• Design, test, and validation engineers who must size and interpret life tests and accelerated test programs
• Program and warranty managers who must translate failure statistics into reserve and service-part decisions
• Industry segments: automotive OEM/Tier 1, heavy truck, medical devices, electronics, aerospace, industrial equipment

Learning Objectives

• Fit Weibull, exponential, lognormal, and normal distributions to complete and censored failure data using probability plotting, rank regression, and maximum likelihood — and interpret shape (β) and scale (η) parameters against failure physics
• Construct and correctly interpret two-sided and one-sided confidence bounds (Fisher matrix, likelihood ratio, beta-binomial) and explain how sample size and censoring drive bound width
• Run and defend goodness-of-fit assessment and model selection across candidate distributions, recognizing when the "best-fit" curve is physically wrong
• Detect and separate mixed/multi-modal populations and competing failure modes, then re-analyze subpopulations to recover a useful model
• Analyze warranty and field data in dates-of-sale/dates-of-claim format, converting claim records into adjusted failure-time data and usage-based failure forecasts
• Plan an accelerated life test using Arrhenius, Eyring, inverse power law, and temperature-humidity models, and translate accelerated results to use-condition reliability
• Build reliability block diagrams for series, parallel, and k-out-of-n structures and roll component models up into system reliability targets

Consulting Sessions

Seminar attendees can sign up for individual consulting sessions with the instructor. Sessions are free for registered attendees, first-come first-served — sign up when registering by calling 248-539-0473 or during the seminar.