Introduction
The programme “Smart Refinery Maintenance Using AI” develops professional practice in Mechanical Engineering. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from process safety, barriers, and management of change to reliability, predictive maintenance, and failure analysis, with practical work on maintenance planning, work orders, and execution measurement, artificial intelligence applications and controls, mechanical equipment analysis and fault diagnosis. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Analyze an operational hazard scenario and review barriers and change control responsibilities in an application related to “Smart Refinery Maintenance Using AI”.
- Build a maintenance plan and work order connecting asset criticality, resources, and documentation.
- Design an AI use case with verification criteria and human review.
- Interpret mechanical equipment performance and identify fault hypotheses and safe verification steps.
- Analyze asset failure and condition data to define a justified warning and maintenance intervention.
Learning areas
1. Process Safety, Barriers, and Management of Change
- Identify sources of hazardous materials and energy and process boundaries, distinguishing process safety from individual occupational safety in the case presented.
- Analyze the sequence, causes, and consequences of a hazardous event, and identify preventive and mitigating barriers and evidence for verifying their readiness.
- Review work permits, isolation, and coordination among parties under facility procedures, and identify hold points requiring specialist approval.
- Prepare a change request identifying its rationale, risks, and effects on documents, training, and emergencies, with technical review before implementation.
- Conduct a tabletop exercise involving a hypothetical barrier failure or leak, and document escalation and response actions, lessons, and the subsequent verification plan. The application examines a case related to “Smart Refinery Maintenance Using AI”.
2. Maintenance Planning, Work Orders, and Execution Measurement
- Inventory assets and build a hierarchy, coding system, technical data, and maintenance history, classifying criticality by safety, service, and cost.
- Select a mix of preventive, condition-based, and corrective maintenance, connecting the decision to failure modes, data, and manufacturer instructions.
- Plan the work order's scope, resources, materials, time, isolation, and permits, and define readiness conditions before scheduling.
- Organize schedules, shutdowns, and equipment handovers between operations and maintenance, recording actual work, causes, parts, and time.
- Analyze backlog, recurrence, plan compliance, and reliability, and propose documented adjustments to maintenance tasks and their frequency.
3. Artificial Intelligence Applications and Controls
- Identify a task that AI can support, and separate inputs and human decisions from outputs produced by the tool.
- Prepare suitable test data and examples while respecting confidentiality, privacy, and permissions, and use de-identified training exercise data.
- Write clear instructions covering the task, context, constraints, and output format, and compare multiple formulations using a fixed set of examples.
- Check outputs for errors, bias, and unsupported information, and identify cases that require specialist review.
- Assess the feasibility of use in terms of quality, time, cost, and risk, and document controls for approval, monitoring, and stopping when failures occur.
4. Mechanical Equipment Analysis and Fault Diagnosis
- Read training equipment data and diagrams, identifying function, operating conditions, design limits, and key measurement points.
- Analyze relationships between load, flow, pressure, temperature, and vibration, and review measurement quality before interpreting performance deviations.
- Identify failure modes involving components, seals, bearings, alignment, and lubrication, and connect fault symptoms to multiple testable hypotheses.
- Select inspections and diagnostic methods suited to the condition and criticality, and refer to manufacturer instructions and isolation and permit procedures before any intervention.
- Prepare a diagnostic report showing readings, evidence, possibilities, the next verification step, and the limits of conclusions from the available data.
5. Reliability, Predictive Maintenance, and Failure Analysis
- Define the asset's function, functional failure, and usage context, and assess the effects of failure modes before selecting a maintenance task type.
- Organize fault, downtime, repair, and condition reading histories, checking the quality of timestamps, classifications, and record completeness.
- Analyze trends, vibration, temperature, or other appropriate condition indicators, linking alerts to documented thresholds and specialist technical review.
- Distinguish correlation and prediction from proof of a failure's cause, and assess false and missed alarms and warning lead time before intervention.
- Design a condition monitoring pilot for a training asset, with escalation criteria and documentation of the maintenance decision, and verify the intervention's effect on reliability.
Practical outputs
- A register of risk scenarios and barriers, and an operational change review plan.
- A maintenance plan, work schedule, work order card, and monitoring indicators.
- A use case brief and a test template for the quality of AI outputs.
- An equipment profile, fault diagnosis tree, and preliminary inspection plan.
- A failure mode analysis, condition monitoring plan, and alert and review rules.
Who should attend
- Engineers, technicians, supervisors and operations staff whose work relates to the programme subject.
- Managers and teams working in mechanical engineering, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Smart Refinery Maintenance Using AI” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Smart Refinery Maintenance Using AI” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.