AI-Powered Predictive Maintenance Strategy Optimisation Platform¶

Industry: Mining, manufacturing, and asset-intensive operations
Role: Solution architecture, AI design, and platform development
Technologies: Python, Azure, React, Generative AI, Agentic AI, Machine Learning, LangChain, reliability engineering, optimisation, SAP integration, Weibull analysis, predictive maintenance

Overview¶

Designed and developed an AI-powered predictive maintenance and strategy optimisation platform to help mining and manufacturing organisations move from reactive and calendar-based maintenance to risk-driven, data-informed decisions.

Organisations often hold years of maintenance history in SAP and other enterprise asset management systems. Yet critical failure signals remain buried in engineer notes, work order descriptions, comments, and free-text logs. Inconsistent functional locations, material-to-equipment mappings, and data quality issues make it difficult to identify failure behaviour, perform reliable analysis, and put predictive maintenance models into practice.

The platform combines Generative AI, machine learning, reliability engineering, and Agentic AI. It standardises maintenance records, identifies historical replacement events, analyses asset reliability, estimates future maintenance requirements, and recommends schedules that account for operational constraints.

Business Problem¶

Asset-intensive organisations commonly face several related challenges:

Failure events are hidden in unstructured work orders and maintenance logs.
Functional locations may be incorrect or inconsistent.
Material-to-equipment mappings can become unreliable over time.
Preventive maintenance schedules are applied uniformly, without reflecting asset risk.
Planning depends on manual decisions and subject-matter expertise that can be difficult to scale.
Poor historical data quality limits the success of predictive maintenance initiatives.
Maintenance expenditure is difficult to connect directly to risk reduction and business outcomes.

These issues can lead to over-maintenance of low-risk assets, under-maintenance of high-risk assets, unplanned downtime, poor use of labour and shutdown windows, and excess maintenance and inventory costs.

My Role¶

Led solution architecture, AI design, and platform development. Responsibilities included:

Designing the end-to-end product architecture.
Building Generative AI workflows for maintenance data cleansing.
Defining reliability engineering methodologies and predictive failure modelling approaches.
Designing Agentic AI workflows for scheduling and planning.
Developing optimisation logic for maintenance prioritisation.
Shaping analytics dashboards and operational interfaces.
Establishing governance and human-in-the-loop validation processes.

Solution Architecture¶

The platform brings together four functional layers.

1. AI-Augmented Data Review¶

Generative AI reviews work order descriptions, service engineer notes, long-text SAP logs, material records, equipment details, and functional locations. It flags and helps correct inconsistent functional location assignments, material classifications, equipment mappings, missing maintenance relationships, and other data quality issues. This creates a more trusted foundation for downstream analysis.

2. Failure Intelligence¶

Generative AI and machine learning analyse maintenance records to identify failure and replacement events, including events that are not explicitly recorded in structured fields. The platform determines whether a work order describes a repair or replacement, the component and asset involved, the likely failure mechanism, replacement history, and maintenance frequency patterns.

3. Reliability Engineering¶

Once failure data has been reviewed and established, the platform supports asset age analysis and reliability modelling. Age analysis examines asset age profiles, lifecycle patterns, industry replacement benchmarks, and expected lifespan. Weibull analysis is used to assess failure probability, failure rates, hazard curves, and remaining useful life (RUL).

Analysis can be performed at material, equipment code, equipment group, and functional location levels.

4. Agentic Maintenance Planning¶

Specialised planning agents evaluate asset criticality, failure probability, labour requirements, cost constraints, shutdown windows, material availability, inventory levels, workforce skills, and resource availability. The platform uses these inputs to recommend future maintenance schedules that balance business objectives with operational constraints.

Specialised AI Agents¶

Data Cleansing Agent: Reviews functional location and material mappings.
Replacement Detection Agent: Identifies failure and replacement activities in maintenance text.
Asset Health Agent: Calculates health scores and risk indicators.
Maintenance Planning Agent: Recommends future maintenance actions.
Material Expeditor Agent: Checks inventory availability and procurement requirements.
Scheduling Agent: Optimises shutdown schedules and labour allocation.
AI Prioritisation Agent: Ranks maintenance activities by risk, business value, and operational feasibility.

Human-in-the-Loop Governance¶

AI recommendations are presented for review rather than executed without oversight. Reliability engineers can review, approve, reject, or modify recommendations; override outputs; validate replacement classifications; and audit prior decisions. Version history and traceability support governance and compliance.

Key Capabilities¶

AI-assisted material grouping, functional location, and component replacement review.
Asset age, Weibull survival, and hazard analysis.
Asset health dashboards.
Resource planning, labour forecasting, cost projections, shutdown planning, and material planning.
Dynamic, constraint-based scheduling and risk prioritisation.
SAP data integration, data refresh management, model execution, and configuration management.

Business Impact¶

The platform is designed to improve maintenance data quality and classification, reduce root-cause analysis effort by up to 90%, and support faster repair of critical assets. Risk-based planning can help reduce unplanned downtime and live failures, improve asset availability, strengthen maintenance spend justification, and make better use of labour and shutdown windows.

What Makes the Platform Different¶

Rather than focusing only on predicting failures, the solution supports a broader maintenance lifecycle:

Data review → Failure identification → Reliability analysis → Planning → Scheduling → Governance

It brings Generative AI, Agentic AI, reliability engineering, machine learning, operations research, and maintenance optimisation into one workflow, with engineers retaining decision authority over recommendations.