Reliability Engineering

Advanced Weibull analysis, MTTF/MTBF tracking, and physics-based failure projection with ISO 14224 compliance. No black-box AI—just proven statistical models.

Production-Ready Reliability Analytics

Built on proven statistical methods and industry standards—not experimental AI. Every calculation is transparent, auditable, and defensible.

Weibull Analysis

Estimate shape (β) and scale (η) parameters from failure data. Calculate reliability curves, hazard functions, and project MTTF with statistical confidence.

2-parameter Weibull distribution
Maximum Likelihood Estimation (MLE)
Reliability curve plotting

MTBF/MTTF Tracking

Component-level failure tracking with calculated mean time calculations. Monitor trends over time and benchmark against industry standards.

MTBF (Mean Time Between Failures)
MTTF (Mean Time To Failure)
Component-level aggregation

ISO 14224 Compliance

Data structures aligned with ISO 14224 petroleum and natural gas industries reliability and maintenance data standard.

Standardized failure modes
Industry-standard taxonomy
Benchmark against peer data

Physics-Based, Not AI

In high-consequence industries, you can't afford black-box projections. Our reliability calculations use proven statistical models that have been validated over decades of industrial use.

Explainable Results

Every projection traces back to specific failure events and statistical assumptions.

Audit-Ready

Regulators understand Weibull distributions. They don't understand neural networks.

No Training Required

Works with small datasets. No need to wait for thousands of failure events.

Industry Standard

Accepted by API, ISO, and regulatory bodies worldwide.

Example: Pump Failure Analysis

Weibull Plot

Shape (β)

2.3

Wear-out failures

Scale (η)

2,450 hrs

Characteristic life

MTTF

2,200 hrs

Expected life

Confidence

95%

Statistical

Real-World Applications

Optimize Maintenance Intervals

Use Weibull analysis to determine optimal preventive maintenance schedules. Replace components just before the wear-out phase begins, minimizing both costs and unexpected failures.

Example: Centrifugal pump impellers showing β = 2.3 (wear-out) should be replaced at 80% of characteristic life (1,960 hours) rather than waiting for failure.

Justify Capital Expenditures

Show executives the financial impact of replacing aging equipment. When MTBF data shows increasing failure rates, you have quantitative evidence to support replacement decisions.

Example: Heat exchangers with declining MTBF (2,000 hrs → 1,200 hrs → 800 hrs) indicate impending catastrophic failure. Cost of replacement vs. cost of unplanned downtime.

Warranty Claims & Supplier Performance

Track component reliability by vendor. When purchased components fail earlier than specified MTTF, you have statistical evidence to support warranty claims or vendor negotiations.

Example: Vendor claims MTTF = 5,000 hours, but your data shows actual MTTF = 3,200 hours. Quantitative basis for renegotiating contracts or demanding replacements.

Regulatory Compliance & Audits

Demonstrate to regulators that your reliability program is data-driven and continuously improving. ISO 14224 compliance ensures your data is comparable to industry benchmarks.

Example: When auditors ask for evidence of proactive maintenance programs, export Weibull analysis reports showing statistically sound forecasting.

Technical Implementation

What You Get Out of the Box

Weibull Distribution Functions

• Parameter estimation (β, η) via Maximum Likelihood Estimation
• Reliability function R(t) = exp(-(t/η)^β)
• Hazard function h(t) = (β/η) * (t/η)^(β-1)
• MTTF calculation = η * Γ(1 + 1/β)
• Confidence intervals for parameters

Data Collection & Management

• Failure event logging with timestamps
• Component lifecycle tracking (install → failure → replace)
• Censored data handling (right-censored observations)
• ISO 14224 compliant data structures

Visualization & Reporting

• Weibull probability plots (log-log scale)
• Reliability curves over time
• MTBF/MTTF trend charts
• Exportable PDF reports for audits

Data Quality Requirements

Weibull analysis requires accurate failure data. Minimum recommended dataset: 10-15 failure events per component type. More data = higher confidence. Garbage in, garbage out applies— ensure failure timestamps and component identifiers are accurate.