Maintenance & Reliability Analytics

Turn your maintenance and operational data into clear, usable reliability insight.

You already collect maintenance data from your CMMS, fleet systems, and logs. I help mining teams structure that data and extract the metrics that actually matter — downtime breakdowns, failure trends, MTBF, MTTR, and equipment availability.

Request a Data Review See How It Works
CMMS Integration · Fleet Telemetry · Failure Analysis · MTBF / MTTR / Availability · Downtime Reporting
The Problem

You're collecting data. But it's not giving you answers.

Most mining operations have years of maintenance records — work orders, failure codes, repair durations, parts consumption. The data is there, but extracting structured reliability insight from it is a different problem. Spreadsheets hit their limits. CMMS reports don't answer the right questions. And the gap between what you're collecting and what you need to see keeps widening.

Data trapped in silos

Maintenance logs, fleet telemetry, and production records sit in separate systems with no unified view of equipment performance.

Inconsistent data quality

Free-text failure descriptions, missing timestamps, and inconsistent categorisation make raw data unreliable for analysis.

No visibility into failure trends

Without structured metrics, you can't see whether MTBF is improving, which assets drive downtime, or where repeat failures cluster.

Reactive reporting cycles

By the time downtime data gets compiled manually, it's already outdated. Teams need current, structured visibility — not month-old summaries.

What I Do

Transform raw maintenance events into structured operational datasets and reliability metrics.

Data Integration

Connect to CMMS exports, fleet management systems, historian data, and maintenance logs. Work with what you already have.

Reliability Metrics

Calculate MTBF, MTTR, availability, and failure rates at asset and fleet level. Structured, auditable, repeatable.

Failure Analysis

Pareto analysis of downtime contributors, failure mode categorisation, and pattern identification across equipment classes.

Operational Reporting

Downtime breakdowns, production-impacting failure summaries, and trend reports built for operational teams, not analysts.

Process

Four steps from raw data to operational clarity.

01

Connect Data

Receive CMMS exports, maintenance logs, fleet data, or historian extracts. No system integration required — file-based is fine.

02

Transform & Structure

Clean, validate, and standardise maintenance events. Map failure codes, normalise timestamps, and build structured datasets.

03

Generate Metrics

Calculate reliability KPIs: MTBF, MTTR, availability, failure frequency, and downtime attribution by asset and failure mode.

04

Deliver Outputs

Provide dashboards, structured datasets, and summary reports designed for maintenance planners and operations teams.

Example Outputs

Structured deliverables built for operational decision-making.

Downtime Breakdown by Asset

Ranked view of which equipment accounts for the most downtime hours, segmented by failure category and time period.

MTBF / MTTR Trends

Time-series tracking of mean time between failures and mean time to repair, by asset class or fleet level, with period comparisons.

Availability Metrics

Equipment and fleet availability calculations with clear methodology, showing inherent, achieved, and operational availability where data permits.

Failure Categorisation

Structured classification of failures by type, component, cause, and severity — turning free-text work orders into analysable data.

Production-Impacting Downtime

Isolation of maintenance events that directly affected production output, with duration and frequency analysis tied to production loss.

Structured Datasets

Clean, validated datasets with standardised fields ready for your own analysis, BI tools, or reliability modelling workflows.

Dashboard Outputs

Sample visualisation panels.

Sample data — illustrative only
Top Downtime Contributors
HOURS — YTD
0 200 400 600 Drill D15 0h Haul Truck #07 0h Excavator #04 0h Loader L09 0h Crusher C3 0h Conveyor 0h
Fleet Availability
BY EQUIPMENT CLASS
80% 85% 90% 95% 100% Mobile Fleet 0.0% −2.7% Fixed Plant 0.0% −1.2% Support Equip 0.0% −0.5%
MTBF Trend — Haul Trucks
HOURS
250 300 350 400 450 500 J F M A M J 0h +48% YTD
Maintenance Mix
WORK ORDERS — YTD
0% Scheduled Scheduled 58% Reactive 42% 0 Work orders YTD Best-in-class 80–85% sched. Industry average 55–70% sched. Scheduled vs Reactive split
Background

Engineering rigour.
Data-focused practice.

This work sits at the intersection of reliability engineering and data systems — where structured methodology meets messy operational data.

Defence-grade analytical rigour, applied directly to real mining maintenance and performance problems. In operational environments, data is imperfect and timelines are real, so outputs are built to be defensible, repeatable, and practical — not theoretical.

Get in Touch

Mining & Resources Experience

Direct experience with mining maintenance data systems, reliability analysis, and equipment performance reporting. Understanding operational context — how events are recorded in the field, production pressures, and what teams need from their numbers.

Defence-grade Reliability

Methodology rooted in defence systems engineering, where failure analysis and data integrity are non-negotiable. The same discipline — defensible calculations, auditable methodology, traceable outputs — applied to mining maintenance data.

Data Engineering & Analysis

Data transformation pipelines and structured analytics workflows. Technical reporting informs the standard of every deliverable — built for operational use and professional scrutiny. Every process is repeatable and documented, not one-off spreadsheet work.

Working Model

Direct engagement — single point of contact, full accountability. No intermediaries, no account managers. The expertise you engage is the expertise doing the work.

Name — Reliability Analytics Aaron Mietzel RuneMech
Get Started

See what your maintenance data looks like when it's properly structured.

Send a sample of your maintenance data — CMMS export, work order log, or fleet data — and I'll walk through how it can be transformed into reliability metrics and operational dashboards. No cost, no obligation.

Request a Data Review amietzel@runemech.com
Typical turnaround: 5–7 business days · File-based — no system access needed · Confidential by default