Reliability Engineering Tools for Mining: A Complete Guide to Weibull Analysis, Spares Optimisation & Downtime Prioritisation
Practical methods and ready-to-use templates for maintenance engineers who need to make data-driven decisions.
Every maintenance manager in mining and heavy industry faces the same challenge: too much equipment, too many failure modes, and not enough time to analyse it all properly.
You know the data holds answers. You know there's a better way than gut-feel decisions about when to replace components or how many spares to hold. But between fighting daily breakdowns and keeping production moving, who has time to build analysis tools from scratch?
That's exactly why we created the Reliability Engineering Essentials Bundle. (Add to cart below- $15)
What's Inside the Bundle
This collection brings together four comprehensive PDF guides and three ready-to-use Excel templates covering the core analytical methods used by reliability engineers across the mining industry.
Weibull Analysis Guide & Excel Template
Weibull analysis is the foundation of reliability engineering. Named after Swedish engineer Waloddi Weibull, this statistical method helps you understand how and when your equipment fails.
The guide walks you through:
The mathematical foundations behind the Weibull distribution
How to collect and classify life data (including handling censored data from components still in service)
Step-by-step parameter estimation using rank regression
Interpreting beta values to identify infant mortality, random failures, or wear-out patterns
Calculating reliability metrics like mean time to failure and survival probability
The accompanying Excel template lets you input your own failure data and immediately generate Weibull plots, beta and eta values, and reliability curves—no programming required.
Real-world application: The guide includes a complete case study using hydraulic cylinder failure data from a mining operation, demonstrating how to handle 283 data points including both failures and suspended (still-running) components.
Conditional Probability of Failure Guide
Here's a scenario every maintenance planner recognises: equipment comes in for scheduled maintenance, but something goes wrong—the tyre handler breaks down, parts haven't arrived, or labour is stretched thin. The machine gets sent back to work with the old components still installed.
What are the actual chances those components fail before the next service window?
This guide shows you how to quantify that risk using conditional probability of failure—the likelihood of failure within a specific interval, given the component has already survived to a certain age.
More importantly, it demonstrates how to translate that probability into dollars, comparing the expected cost of different maintenance strategies.
Real-world application: The case study analyses haul truck tyres at a Queensland mine, calculating whether it made economic sense to delay a tyre replacement by 5,000 km. The analysis revealed a 56% chance of failure and showed the decision cost the business approximately $127,000 in expected value.
Jack-Knife Diagram Guide & Excel Template
Most maintenance teams use Pareto charts to prioritise downtime. The problem? Pareto charts only show total downtime—they can't distinguish between a single catastrophic failure and a hundred small nuisance stoppages that collectively consume the same hours.
Jack-Knife diagrams solve this by plotting Mean Time to Repair (MTTR) against frequency of occurrence, creating four distinct quadrants:
Acute & Chronic: High frequency, long repair times—your top priority
Chronic: High frequency, shorter repairs—reliability problems
Acute: Low frequency, long repairs—maintainability problems
Ideal Zone: Where you want everything to end up
The guide explains how to calculate the acute and chronic limits, plot your downtime data, and extract prioritised action lists for reliability, availability, and maintainability improvements.
Real-world application: Using data from 13 electric shovels at a Chilean copper mine, the case study shows how Jack-Knife analysis identifies different priorities than a standard Pareto chart—revealing chronic issues like electrical inspections and overload relays that Pareto would rank lower.
Spares Management Guide & Excel Template
Inventory decisions in mining carry real consequences. Hold too many spares and you're tying up capital—holding costs can reach 38-48% of inventory value annually. Hold too few and a stock-out on a critical component means production losses that dwarf the cost of the part.
This guide covers both high-rotation consumables and low-rotation critical spares:
For high-rotation items:
Economic Order Quantity (EOQ) using Wilson's formula
Economic Order Point (EOP) calculations accounting for variable demand and lead times
Safety stock factors for different service levels
For low-rotation critical spares:
Using Weibull parameters to estimate expected failures between maintenance intervals
Applying Poisson distribution to determine spare quantities for a target availability
Balancing planned maintenance spares with unplanned failure coverage
Real-world application: The case study calculates the optimal cylinder holding for a mine with a 3,500-hour PM interval, showing how to combine scheduled replacement needs with probabilistic coverage for unexpected failures to achieve 99% parts availability.
Who This Bundle Is For
These guides are designed for:
Reliability Engineers looking for practical methods they can apply immediately
Maintenance Planners who need to justify spares holdings and maintenance intervals with data
Maintenance Managers wanting to prioritise improvement efforts effectively
Asset Management Professionals building business cases for reliability investments
Engineering Students studying reliability engineering with real industrial examples
The methods apply across mining, oil and gas, manufacturing, and any industry with capital-intensive rotating equipment.
Why Excel Templates Matter
Theory is important, but application is everything.
Each Excel template is built to match the methodology in its companion guide. You can follow along with the case study data to understand how the calculations work, then clear the example data and input your own.
No VBA macros, no complex setup—just structured spreadsheets with formulas you can inspect and modify.
The templates handle:
Kaplan-Meier adjusted ranks for censored Weibull data
Automatic beta and eta calculation from failure times
Dynamic Jack-Knife diagram limits that update as you add data
Poisson-based spare quantity calculations for any target availability
The Bigger Picture
These four topics aren't isolated techniques—they connect into a complete reliability engineering workflow:
Weibull Analysis gives you the failure distribution parameters
Conditional Probability helps you assess risk when plans change
Jack-Knife Diagrams prioritise where to focus improvement efforts
Spares Optimisation ensures you have the right parts when you need them
Master these fundamentals and you have the analytical foundation to tackle most reliability challenges in heavy industry.
What You Get
4 PDF Guides:
Weibull Analysis Guide & Case Study (16 pages)
Conditional Probability of Failure – Haul Truck Tyres Case Study (7 pages)
Jack-Knife Diagram Guide & Case Study (11 pages)
Spares Management Guide & Case Study (17 pages)
3 Excel Templates:
Weibull Analysis Calculator
Jack-Knife Diagram Builder
Optimal Spares Calculator
All guides include the mathematical foundations, step-by-step processes, and worked examples from real mining operations.
Start Making Data-Driven Maintenance Decisions
Stop relying on gut feel and vendor recommendations. These guides give you the tools to analyse your own equipment data and make maintenance decisions you can defend with numbers.
The methods aren't new—they're proven techniques used by reliability engineers worldwide. What's different is having them compiled into practical, accessible guides with templates ready for your data.
Download the Reliability Engineering Essentials Bundle and start applying these methods to your equipment today.
Pardus Consulting Australia specialises in reliability engineering and maintenance optimisation for the mining and resources sector. Based in Brisbane, Queensland.
