Introduction
The programme “Preventive and Predictive Maintenance for Heavy Industrial Equipment Using Artificial Intelligence and the Internet of Things” develops professional practice in 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 reliability, predictive maintenance, and failure analysis to project planning and implementation control, with practical work on maintenance planning, work orders, and execution measurement, artificial intelligence applications and controls, engineering design, modeling, and simulation. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Analyze asset failure and condition data to define a justified warning and maintenance intervention in an application related to “Preventive and Predictive Maintenance for Heavy Industrial Equipment Using Artificial Intelligence and the Internet of Things”.
- Build a maintenance plan and work order connecting asset criticality, resources, and documentation.
- Design an AI use case with verification criteria and human review.
- Build an engineering model and assess its assumptions, limitations, and verification results.
- Prepare a project plan connecting scope, time, resources, and risks.
Learning areas
1. 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. The application examines a case related to “Preventive and Predictive Maintenance for Heavy Industrial Equipment Using Artificial Intelligence and the Internet of Things”.
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. Engineering Design, Modeling, and Simulation
- Translate needs into requirements, design limits, and load or operating cases, documenting criteria and references relevant to the case.
- Prepare an engineering model showing dimensions, materials, relationships, and constraints, and review consistency between drawings, assumptions, and inputs.
- Select a simulation method suited to the problem, define boundary conditions, and examine the sensitivity of results to input quality and model settings.
- Compare outputs with a reference calculation or available test data, distinguishing calculation accuracy from the validity of the model's representation of reality.
- Prepare a design report explaining results, limitations, risks, and required modifications, specifying matters requiring specialist engineering review and approval.
5. Project Planning and Implementation Control
- Define the project rationale, expected results, stakeholders, and success criteria, and document the scope of work and exclusions.
- Break deliverables into work packages and related activities, and estimate durations, resources, and activity dependencies.
- Prepare a schedule and cost baseline, identify delivery and review milestones, and clarify approval and escalation responsibilities.
- Analyze implementation risks, define responses and warning indicators, and control change requests and their effects on time and cost.
- Build a progress report covering variances, decisions, and lessons learned, and define procedures for accepting deliverables and closing the project.
Practical outputs
- A failure mode analysis, condition monitoring plan, and alert and review rules.
- 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.
- Design specifications, a simulation model, and a verification and review report.
- A project charter, implementation schedule, and change and risk tracking register.
Who should attend
- Engineers, technicians, supervisors and operations staff whose work relates to the programme subject.
- Managers and teams working in engineering, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Preventive and Predictive Maintenance for Heavy Industrial Equipment Using Artificial Intelligence and the Internet of Things” 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 “Preventive and Predictive Maintenance for Heavy Industrial Equipment Using Artificial Intelligence and the Internet of Things” 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.