Introduction
The programme “Using Artificial Intelligence to Monitor Mechanical Performance” 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 mechanical equipment analysis and fault diagnosis to engineering design, modeling, and simulation, with practical work on artificial intelligence applications and controls, maintenance planning, work orders, and execution measurement, reliability, predictive maintenance, and failure analysis. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Interpret mechanical equipment performance and identify fault hypotheses and safe verification steps in an application related to “Using Artificial Intelligence to Monitor Mechanical Performance”.
- Design an AI use case with verification criteria and human review.
- Build a maintenance plan and work order connecting asset criticality, resources, and documentation.
- Analyze asset failure and condition data to define a justified warning and maintenance intervention.
- Build an engineering model and assess its assumptions, limitations, and verification results.
Learning areas
1. 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. The application examines a case related to “Using Artificial Intelligence to Monitor Mechanical Performance”.
2. 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.
3. 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.
4. 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.
5. 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.
Practical outputs
- An equipment profile, fault diagnosis tree, and preliminary inspection plan.
- A use case brief and a test template for the quality of AI outputs.
- A maintenance plan, work schedule, work order card, and monitoring indicators.
- A failure mode analysis, condition monitoring plan, and alert and review rules.
- Design specifications, a simulation model, and a verification and review report.
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 “Using Artificial Intelligence to Monitor Mechanical Performance” 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 “Using Artificial Intelligence to Monitor Mechanical Performance” 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.