Iron Lake Reliability tutorial

Iron Lake Reliability closes the reliability loop for a system: 1 Reliability Prediction (MIL-HDBK-217F parts count / part stress, NSWC-11) → 2 Reliability Block Diagram → 3 Bayesian Analysis → 4 Monte Carlo Simulation against the requirement → 5 Reliability Loop (run the loop, design what-ifs) → back to the prediction. The Weibull tools follow: life data on the Weibull Workbook, the Weibull Calculator, Mission / Constellation Prediction, and RDT. This page is the public tutorial. The in-app Tutorial tab adds live math callouts, Rank models, and the same walkthrough next to the workbook chrome.

Open the Iron Lake Reliability workbook Open Weibull Calculator Open the interactive tutorial

Product walkthrough

A guided tour of Iron Lake Reliability (~19 minutes): the closed Reliability Loop first, then every tab in menu order. Same self-hosted file as the in-app Tutorial tab.

Product in one paragraph

Iron Lake Reliability is a client-side reliability workbook for hardware. It fits two-parameter Weibull (β, η) or lognormal (μ, σ) to right-censored times, projects Arrhenius or inverse-power accelerated life to use conditions, sizes an RDT, models systems with reliability block diagrams, predicts failure rates from a parts list, and exports each tab to PowerPoint, Excel, PDF, HTML, or CSV. It is not a certified assessment and it is not an Omni-style PDF/CDF curve tool.

Reliability Loop overview

Stages 1 and 2 (Reliability Prediction and RBD) are the top-row tabs. The Reliability Analysis Loop Tools drop-down next to them opens the Reliability Loop tab itself (first in the list), which runs the loop and shows all four stages and their status, along with the Weibull Workbook, the Weibull Calculator and stages 3 and 4 (Bayesian Analysis and Monte Carlo Simulation). Each stage tab links to the loop with Open Reliability Loop.

1. 1 · Reliability Prediction. Reliability Prediction estimates the failure rate λ from the parts list with MIL-HDBK-217F Notice 2 parts count or part stress, and NSWC-11 for mechanical parts.
2. 2 · Reliability Block Diagram. Send to RBD creates linked blocks on the Reliability Block Diagram; model the system with series, parallel, k-out-of-n, and cold-standby groups.
3. 3 · Bayesian Analysis. The prediction becomes the prior; test and field data update it to a posterior that the linked blocks follow, with a credible band.
4. 4 · Monte Carlo Simulation. Simulate the system with parameter uncertainty and compare P(success) and its confidence interval with the requirement.
5. Design what-ifs. The Reliability Loop tab ranks the top contributors and what-ifs (redundancy, a better quality level). Apply one, update the loop, and the next cycle starts again from the prediction.

Fastest way in: open the Reliability Loop tab in the app and click Load EXAMPLE loop… (or Load the example loop at the top of the in-app Tutorial). Cycle 1 lands at about 0.974 against R ≥ 0.98; a redundant Controller lifts cycle 2 to about 0.998. Everything works signed out; only Ask Walt AI needs a sign-in.

Reliability Prediction

The Reliability Prediction tab estimates an equipment failure rate from its parts list. Electronic parts use MIL-HDBK-217F Notice 2 (1995): the Appendix A parts count tables or the full part stress models (Sections 5–23) with junction temperatures; bearings, gears, motors, springs, and couplings use NSWC-11 (2011). 217F is not maintained: results are estimates for comparison, not field predictions. It works signed out and autosaves in your browser. Load example loads a small controller board with a cooling fan.

Environment and handbook basis

1. Enter a Prediction name and Analyst. Keep Handbook revision at MIL-HDBK-217F Notice 2.
2. Pick the Environment (default), one of the 14 handbook environments (GB … CL); lines can override it.
3. Enter Mission time (h), Duty cycle (%), and Prior operating hours T0. Leave Adjustments at None (native 217) until you have a set.

Method, temperatures, and part stress

1. Pick the Method: Parts Count (MIL-HDBK-217F N2 Appendix A) uses λg·πQ and ignores temperature; Part Stress (MIL-HDBK-217F N2 Sections 5–23) evaluates each part with its section model λp = λb·πT·πQ·πE·(section factors). The method is saved with the project and printed in every report.
2. Enter the Interface / baseplate temperature (°C) from your thermal analysis.
3. Give each BOM line a ΔT (°C): the rise from the baseplate to the temperature the handbook uses for that part (junction Tj for microcircuits and semiconductors; case for resistors; ambient for capacitors and relays; hot spot for inductors; insert for connectors). The Tj / T column shows T = T_baseplate + ΔT. ΔT can be imported from an Excel / CSV column, set with Global change, or stored as a library default.
4. πT comes from each section’s own equation and activation energy at T, e.g. πT = exp(−Ea/k · (1/(T+273) − 1/298)) for transistors (§6.3), resistors (§9.1) and capacitors (§10.1); microcircuits use λp = (C1·πT + C2·πE)·πQ·πL (§5.1, πT from §5.8). Relays, motors and synchros carry temperature inside their own λb equations. Click a part to see every factor with its section and page and to edit the stress inputs (power, voltage stress, contact form, complexity…); each default is shown with its source.
5. Assumptions: a blank ΔT or baseplate uses the Appendix A default temperature for the environment. Part types without an implemented part stress model (SMT boards, §16.2) fall back to parts count and are flagged. A ⚠ marks temperatures above a section’s model range or a typical maximum rating. The summary shows both totals side by side.

Add parts by hand or from the library

1. + Add line: reference designators, part number, description, quantity, subassembly.
2. Set the part type (217F family and subtype, quality level πQ, πL years for microcircuits, or an NSWC-11 mechanical type). Each line shows λg, πQ, πL, unit and line FIT, share, and the handbook citation.
3. Part numbers in your library fill in automatically (matched by part number, then category and keywords). + Custom part takes your own λ. Unmatched lines are flagged; select them and use Bulk assign.

Import an Excel / CSV BOM

1. Open Actions and click Import BOM…. Download Excel BOM template gives you the recognized columns: reference designators, part number, description, manufacturer, quantity, category / part type, quality level, subassembly, environment override, years in production, notes.
2. Choose .xlsx, .xls, or CSV (or paste). Pick the sheet; check the detected header row.
3. Map columns to fields (common names are guessed). Remember this mapping reuses it for files with the same columns.
4. Choose Replace current lines or Append, then import. Fix unmatched lines with Bulk assign.

Parts library

1. Parts library… lists your standard parts, assignments, and keywords. Add or edit entries, or use Save to library on a BOM line.
2. Export and import the library as JSON or CSV to back it up or share it; it is stored in this browser.

VITA / Other Adjustments

1. VITA / Other Adjustments… holds named sets (source, edition, notes). Each row replaces or multiplies πQ, πE / environment λg, πL, a λ multiplier, or an NSWC-11 C-factor for one family, all 217 parts, all NSWC-11 parts, or all parts. The most specific row wins.
2. The ANSI/VITA 51.1 set starts empty because the standard is sold by VITA; enter values from your licensed copy.
3. Choose the set under Adjustments. Lines can override it.

Global change

1. Global change…: choose a scope (all parts, selected rows, current filter, categories, part families, or a subassembly).
2. Choose the field (quality level, environment, πL years, NSWC-11 temperatures, duty, adjustment set, λ multiplier, or a C-factor) and set or multiply a value.
3. Preview the lines changed and λ before → after, then Apply. Undo reverses it.

Highlight adjusted parts

1. Tick Highlight adjusted parts to tint every line whose factors differ from the native handbook default; the badge names each factor and its source.
2. Filter with Adjusted only. The report lists them in an Adjusted parts table.

NSWC-11 mechanical parts

1. Set a line to Bearing (ball / roller), Gear, Electric motor, Spring (helical compression), or Mechanical coupling.
2. Fill in the model inputs. A bearing takes load rating and applied load (or rated L10 life), speed, reliability basis, lubricant viscosity ratio, water content, temperature, service condition, and contamination. Each cites its NSWC-11 equation.
3. The line shows the base rate and each C-factor; the λ adds to the total.

Summary, pie, and Pareto

1. The summary gives λ per 10⁶ h and FIT, MTBF in hours and years, mission reliability R(t) = exp(−λ·t·duty), and P(failure).
2. The pie shows failure rate by category, filtered to all, electronic, or mechanical parts, with small categories folded into Other.
3. The Pareto ranks BOM lines by FIT (quantity included), largest first; the top contributor is red, and hovering a bar shows its share and cumulative share. The downloaded PNG and the exports add a cumulative-share line and a dashed 80% marker. Both charts have Download PNG.

Report and exports

1. The tab’s Export drop-down builds PowerPoint, Excel, PDF (print), HTML, and BOM with λ CSV with only the prediction: summary and assumptions, pie and Pareto images, category and subassembly summaries, adjusted parts, flagged lines, and the full BOM.
2. Linked RBD results are left out unless you tick Include linked RBD results (off by default). Export → Report options… sets the title, project, and analyst.
3. In the File menu, BOM CSV / BOM XLSX export the BOM in the import format; Save JSON / Open JSON round-trip the prediction; Save to list keeps it in the browser’s saved predictions.

Send to RBD

1. Send to RBD…: one block for the whole assembly, one per subassembly, or one per category, into a new diagram or added to the current one.
2. Blocks stay linked and update when the BOM or library changes; unlink to freeze a value.

Ask Walt AI about the prediction

1. Keep “Walt AI can see: Prediction” on and ask “What drives my failure rate?”. Walt AI cites 217F and NSWC-11 and never invents handbook values.
2. Ask for a change (“change all resistors from quality level Lower to ER M”); Walt AI shows the lines affected and λ before → after. Apply to change it, Undo to reverse it.
3. Walt AI can also propose switching the Method, a new baseplate temperature, or a Global change to ΔT (“set ΔT to 30 °C on all capacitors”), with the same confirm card and Undo.

Worked example: parts count check

Worked example: part stress at T_j

The example’s 32-bit MOS microcontroller, GF, commercial quality, 40 °C baseplate with an illustrative ΔT of 25 °C, so T_j = 65 °C (§5.1 p. 5-3, §5.8 p. 5-13, §5.9 p. 5-14, §5.10 p. 5-15).

Reliability Block Diagram (RBD)

The Reliability Block Diagram tab models a system from its parts. It opens with a sample pump skid (two pumps in parallel → controller → 2-out-of-3 pressure sensors) wired left to right from Start to End; a second sample, Standby power, has a one-shot start battery and a cold-standby genset pair. It works signed out and autosaves in your browser.

Build the diagram: blocks and structures

1. Load a sample or click New. Type a Diagram name; it names every export.
2. From Building blocks, drag a chip onto the diagram or click it to add next to the selection: Component (one block with its own model), Series (every branch must work), Parallel (active redundancy: any one branch), k-out-of-n (at least k of n; set k in the group properties), or Cold standby (spares dormant until switchover; set the Switch reliability per switchover).
3. Drop on a block to place before or after it; drop on a group frame to put it inside. Groups nest. Undo reverses the last edit.
4. A long chain wraps onto the next row to fit the screen: each row reads left to right, and a return line leads into the next row. A group wider than the screen is drawn smaller on its own row. Exports use the same layout.
5. Shortcut: select a block and use Make redundant → Parallel pair, 2-out-of-3, or Cold standby pair.

Block life models

1. Click a block to open Block properties: name, icon (library, uploaded image resized in the browser, or a colored badge), and model.
2. Choose Weibull (β, η, optional γ), a constant failure rate λ or MTBF (exponential), or a fixed probability p for one-shot items.
3. Import β / η from workbook fit copies the current workbook fit; Copy model to siblings applies the model to the rest of the group.

Mission time τ and prior age T0

1. Pick the Time unit (hours, cycles, miles, km) and enter Mission time τ.
2. Optional Prior age T0: each Weibull block is known good at T0 and uses R(T0 + τ) / R(T0). Exponential blocks are memoryless; fixed p ignores time; cold-standby spares start new.
3. Set BX life, X (%) and, if you like, Plot to.

Results, block FIT, and importance

1. The headline is the probability of success P(success) = R_sys(τ), with P(failure) = 1 − P(success), system MTTF (mean residual life when T0 > 0), and BX life.
2. The Per-block reliability and importance at τ table lists each block by rank with R(τ), FIT, and Criticality. FIT is failures per 10⁹ h: λ × 10⁹ = 10⁹ / MTBF for exponential blocks, and the average over the mission, −ln R(τ) / τ × 10⁹ (marked “avg over τ”), for Weibull and fixed-p blocks. Criticality is the block’s share of the system mission unreliability; the bar shows it.
3. Rank 1 is the weakest link, the block the system is most sensitive to, outlined and tagged WEAKEST on the diagram. A redundant block can have a high FIT and still low criticality.
4. The Importance ranking chart and the R(τ) / F(τ) and hazard charts each have Download PNG.

Link blocks to a prediction

1. Use Send to RBD… on the Reliability Prediction tab, or Assign from prediction on a selected block (whole assembly, one subassembly, or one category).
2. Linked blocks (🔗) are exponential with λ from the prediction × duty cycle. With Auto-update when the prediction changes on they follow BOM and library edits; otherwise out-of-date blocks wait for Refresh / Refresh all.
3. Open in prediction jumps to the lines; Unlink (freeze value) keeps the current λ.

Save and export

1. Save file (.json) / Open file… round-trip the diagram. The toolbar’s Export diagram drop-down has Diagram (.svg), Diagram (.png), and Results (.csv) (summary, blocks with FIT, curve).
2. The tab’s Export drop-down builds PowerPoint, Excel, PDF (print), HTML, and Importance CSV with only the RBD: system result, diagram, importance chart, reliability curve, and the block, structure, and importance tables (the curve data goes to Excel only).

Ask Walt AI about the diagram

1. Sign in, open Ask Walt AI, and keep “Walt AI can see: RBD” on.
2. Ask “Explain my weakest block” or “How do I reach R = 0.99?”. Walt AI reads the structure, block FIT, criticality, and system R, and runs what-ifs with the tab’s own code.
3. Ask for a change (“make the controller 2-of-3”); Walt AI shows before → after P(success) and nothing changes until you click Apply. Undo puts it back.

Worked example: pump skid

The pumps have the highest FIT, but they are redundant. The single-string controller carries criticality ≈ 0.93, so it ranks 1 as the weakest link.

Bayesian Analysis

⚠️ Validation pending: results in this section have not yet been independently validated. Verify before relying on them.

The Bayesian Analysis tab combines a prior (engineering judgment, a reliability prediction, or earlier data) with new test or field data to give a posterior: parameter estimates with credible intervals, R(t) bands, BX life, and the probability of meeting a requirement. It opens with three EXAMPLE analyses and works signed out.

Pick a model and prior

1. Weibull (β, η): priors as 95% ranges (lognormal) or bounds (uniform); posterior by MCMC (4 chains, R̂ and effective sample size reported) or exact grid quadrature, in a background worker.
2. WeiBayes (β known): exact gamma update of θ = η^−β; with a vague prior and zero failures it reproduces the classical 63.2% WeiBayes bound.
3. Exponential / Poisson: gamma prior on λ; Gamma(a, b) with r failures in time T gives Gamma(a + r, b + T). The prior mean can be the Reliability Prediction λ (217F / NSWC-11).
4. Binomial: beta prior on the success probability; Beta(a, b) with s successes in n trials gives Beta(a + s, b + n − s). Includes Bayesian and classical test planning.

Read the posterior

Cards show R at the mission time (median and credible interval), P(meet requirement), and BX life; the table and charts compare prior and posterior. Worked check: prior mean MTBF 50,000 h with strength 2 is Gamma(2, 100,000 h); one failure in 60,000 h gives Gamma(3, 160,000 h), posterior mean MTBF 53,333 h.

Send the posterior to the RBD

Send to RBD creates or updates a linked block (B badge) that follows the posterior. From any RBD block, Update with test/field data (Bayesian) opens the tab with that block’s model as the prior. With Bayes-linked blocks the RBD shows P(success) with a 90% credible band from posterior draws.

Step by step: a prior from the prediction, updated with field data

1. Load the EXAMPLE controller-board prediction on the Reliability Prediction tab: 52,783 FIT, MTBF 18,945 h. (Load EXAMPLE loop… on the Reliability Loop tab sets up all of this in one click.)
2. On Bayesian Analysis, pick “EXAMPLE Controller — field data”, or New analysis… with Model = Exponential / Poisson.
3. Prior on λ: Prior source = Reliability Prediction λ, Prediction scope = Whole assembly, Strength = 2 equivalent failures (a weak prior, so the data dominate).
4. Test / field data: 2 failures in 120,000 total operating hours. Requirement: mission 1,000 h, required R = 0.97, credible level 0.9.
5. Read the posterior: R(1,000 h) ≈ 0.977 (90% credible 0.952–0.991); P(meet R ≥ 0.97) rises from 32% (prior) to about 71%; posterior median MTBF ≈ 43,000 h. The density chart shows the posterior against the dashed prior; the R(t) chart shows the 90% credible band.
6. Under “Use in the RBD (Reliability Loop)”, choose Add as a new block (or replace an existing block) and click Send to RBD. The block gets the B badge and the RBD shows P(success) with its credible band.

Monte Carlo Simulation

⚠️ Validation pending: results in this section have not yet been independently validated. Verify before relying on them.

The Monte Carlo Simulation tab simulates 10,000 to 1,000,000 missions of the live RBD (series, parallel, k-out-of-n, cold standby with switch reliability, prediction- and Bayes-linked blocks) or of a component list (Weibull, exponential, lognormal, normal, fixed p). It reports simulated P(success) with a Clopper–Pearson interval next to the analytic value, MTTF, BX, a time-to-failure histogram, empirical R(t) against the analytic curve, and a failure-cause Pareto; Show on RBD highlights the top contributors. Parameter uncertainty samples the Bayesian posteriors each run; an optional spares mode estimates fleet spares. A fixed seed makes results reproducible. Sampling error: SE = √(P(1 − P)/N), about 0.00044 for P = 0.98 and N = 100,000.

Step by step: simulate the EXAMPLE pump skid

1. Load the EXAMPLE loop (Load EXAMPLE loop… on the Reliability Loop tab) or build a diagram on the RBD tab. On Monte Carlo Simulation, System defaults to the live RBD.
2. Keep 100,000 runs and seed 12345, tick Parameter uncertainty so Bayes-linked blocks draw from their posteriors, and click Run simulation.
3. Compare simulated P(success) ≈ 0.9738 (95% CI 0.9728–0.9748) with the analytic point-model value ≈ 0.9741 and the RBD posterior band (≈ 0.975, 90% credible 0.953–0.989).
4. Check the time-to-failure histogram (dashed line = mission time) and the empirical R(t) against the analytic curve.
5. The failure-cause Pareto shows the single Controller causing about 95% of mission failures. Show on RBD highlights the top contributors on the diagram.

Reliability Loop (closed loop)

The Reliability Loop tab (first in the Reliability Analysis Loop Tools drop-down) runs one cycle across the Prediction, RBD, Bayesian, and Monte Carlo tabs: the prediction λ is the prior → test and field data give the posterior → linked RBD blocks take it → Monte Carlo gives system reliability with uncertainty → compared with the requirement. Each stage recomputes when its inputs change, and the loop shows Stale until you press Update loop (or turn on Live). Every cycle is stored (timestamp, what changed, prior → posterior, RBD and Monte Carlo results) with a trend chart, saved in the project and in exports. After a run, the Reliability Loop tab lists the top contributors and what-ifs (add redundancy, improve a block) with confirm and Undo, and points prediction-linked blocks to Global Change. Ask Walt AI can read the loop state and propose next steps.

Step by step: close the loop on the EXAMPLE pump skid

1. Open the Reliability Loop tab, click Load EXAMPLE loop… and confirm (or click Load the example loop at the top of the in-app Tutorial). It loads the EXAMPLE pump skid, controller and pump analyses, sets R ≥ 0.98 at 1,000 hours, and runs cycle 1.
2. Cycle 1: RBD P(success) 0.9741 (90% credible 0.950–0.991, P(R ≥ 0.98) = 0.34) and Monte Carlo 0.9738 (95% CI 0.9728–0.9748): below the requirement.
3. Feedback lists the Controller as the top contributor; the top what-if, Add a redundant Controller, lifts P(success) from 0.9741 to 0.9985. Click Apply to RBD… and confirm (Undo is offered).
4. The loop shows Stale. Press Update loop (or turn on Live): cycle 2 lands at about 0.998 (95% CI 0.9974–0.9980) and meets the requirement.
5. The history table and the trend chart show both cycles against the requirement line. Undo next to “EXAMPLE loop loaded” restores your previous work.

Constellation mode (Mission / Constellation Prediction)

The Mission / Constellation Prediction tab has a Mode drop-down: Fleet (the mission / fleet prediction from a life model: the Weibull Workbook fit, a Bayesian Analysis posterior or the MIL-HDBK-217 prediction, chosen with Life model source) or Constellation. Constellation mode asks whether a satellite constellation keeps enough operational satellites over its mission. Inputs: orbital planes, operational satellites per plane, hot in-orbit spares per plane and ground spares; the satellite reliability (Weibull β, η in years or exponential λ / MTBF, or linked from a Reliability Prediction λ, the RBD system result such as the satellite bus, or a Bayesian posterior; linked values in hours are converted at 8766 h per year and refresh like linked RBD blocks); the success rule (at least k operational overall and/or at least m in every plane), mission life and an optional requirement such as P(rule met) ≥ 0.95 throughout the mission; and replenishment (launch success probability, lead time, satellites per launch, maximum launches, spare drift time).

Without replenishment the answer is exact: each plane has X ~ Binomial(n + s, R(t)) live satellites and min(n, X) operational, and the constellation total is the convolution over the planes, so P(total ≥ k) and P(every plane ≥ m) = P(Y ≥ m)^P are exact (2-of-3 gives 3R² − 2R³). Monte Carlo (background worker, seed, progress and cancel) adds replenishment launches, ground spares and drift time and reports P(rule met) at end of life with a Clopper–Pearson interval, P(rule met over the whole mission), availability, mean outage duration, launches used, spares consumed and a spares search for the in-orbit and ground spares that meet the requirement. Charts show operational satellites vs time (mean and 5–95% band), P(rule met) vs time and the spares sensitivity. What-ifs such as “Add one in-orbit spare per plane” use a confirm card with Undo; Walt AI can read the constellation and offer the same edits. In this mode the Reliability Loop tab shows an extra 5 · Constellation card.

Step by step: the EXAMPLE constellation

1. Set Mode to Constellation and press Load EXAMPLE. The EXAMPLE (illustrative numbers): 6 planes × 11 satellites, 1 in-orbit spare per plane, 24 ground spares, Weibull β = 1.2, η = 15 years, 7-year mission, rule ≥ 60 of 66 operational, requirement P ≥ 0.95 throughout, replenishment with 95% launch success, 6-month lead time, 2 satellites per launch, at most 20 launches, 30-day spare drift.
2. Monte Carlo runs automatically (10,000 runs, seed 12345): P(≥ 60 at end of life) is about 0.89 and availability about 0.99, so the requirement is not met; the spares search suggests 2 in-orbit spares per plane, or 28 ground spares.
3. Untick Replenishment: the exact analytic gives satellite R(7 yr) = 0.670, 48.2 of 66 operational expected and P(≥ 60) = 0.0010.
4. Turn replenishment back on and try What if… → Add one in-orbit spare per plane (confirm, re-run, Undo).

Weibull tools walk-through

The Weibull tools analyze life data and feed the same diagram.

1. Workbook grid. Time, F/S, quantity, optional STRESS. Open Weibull Workbook and try Load bearing sample (single stress) or the first-load ALT demo (500 K / 523 K / 573 K).
2. Fit. Default Distribution model is Arrhenius-Weibull, Fit method MLE. Read β and η (or μ, σ). Check the probability plot before you believe a B-life.
3. Calculator. Weibull Calculator evaluates R(t), F(t), BX% (B10), mean life (MTTF), remaining life, h(t), and AF from that LifeFit. See the B10, MTTF, and hazard guides for the identities.
4. Mission / constellation. Warranty life and constellation size → expected failures. B1 / B10 / B50 at use stay under Test vs use on the workbook after ALT.
5. RDT. Reliability Demonstration sizes a success-run (or related) demonstration at a stated life, with optional AF from the life curve. Its Life model source can also take the shape from a Bayesian posterior or the 217 prediction (β = 1).
6. Export. Every tab has its own Export drop-down at the bottom: PowerPoint, Excel, PDF (print), HTML, and CSV with only that tab’s charts and tables. Sign in is optional on the website if you want saved analyses.

Export any tab

Each tab’s Export drop-down (under “Export: <tab>”, for example “Export: Weibull Workbook”) builds PowerPoint, Excel, PDF (print), HTML, and CSV files with only that tab’s results: charts as high-resolution images with legends, plus its tables. Plot data goes to Excel and CSV only. Files are named after the tab, the analysis, and the date. Every chart also has Download PNG. Exports work signed out.

Ask Walt AI (AI chat)

Ask Walt AI is an AI chat assistant in the panel at the bottom of the app. Ask about your data in plain English — “What’s my B10 life?”, “Is β above 1, is this wear-out?”, “Reliability at 500 hours with bounds?” — and Walt AI answers by running the site’s own Weibull Calculator on the data in your workbook, so the numbers match what you would get by hand.

Walt AI can also see your Reliability Block Diagram and your Reliability Prediction: “Explain my weakest block”, “What drives my failure rate?”, “How do I reach R = 0.99?”. What-ifs use the app’s own RBD and MIL-HDBK-217F / NSWC-11 code. Ask it to change something (“change all resistors from quality level Lower to ER M”) and it shows the lines affected and the before → after numbers; nothing changes until you click Apply, and Undo puts it back. The “Walt AI can see” toggles choose what is shared.

• Needs sign-in. Chat is the only feature that requires an account; the workbook, plots, calculator, export, and tutorial all work signed out.
• 30 questions a day per signed-in user, free.
• Privacy: questions and the data you share (workbook, and RBD / prediction if switched on) are processed by xAI (Grok). Don’t paste confidential data.
• Answers are decision support, not a certification. Check critical numbers in the Calculator.

Worked example (bearing sample)

Load bearing sample, Recalculate. You should see about β = 2.17, η = 716 h (wear-out). Then:

Step-by-step Weibull identities for this dataset are on Weibull analysis. Rank / recommend distribution (device type, physics of failure, close-call Hybrid weights) lives on the in-app Tutorial so it can read the live grid.

Open the Iron Lake Reliability workbook Open Weibull Calculator