AVEVA PI System Maintenance Performance Analysis Training Course

Course overview

Maintenance teams often hold years of PI data but struggle to turn it into defensible evidence for reliability decisions. Alarm and process histories may be available, yet repeated failures, maintenance-induced downtime, poor-performing assets, and production losses remain hidden across tags, spreadsheets, and work-order systems. This course addresses the gap between collecting operational data and using it to explain maintenance performance. Participants learn to structure PI System data so engineers and planners can investigate failure patterns, quantify downtime, and prioritize interventions using a shared asset context.

The course covers AVEVA PI Data Archive, PI Asset Framework (AF), PI System Explorer, PI Vision, PI Analysis Service, and Event Frames as a maintenance-performance analysis environment. Participants configure AF asset hierarchies and templates, define maintenance-relevant attributes, build calculations for availability and mean time between failures, create Event Frames for downtime and abnormal operating periods, and develop PI Vision displays for asset health and maintenance reviews. They also learn to validate tag quality, establish calculation assumptions, interpret trends and event patterns, and connect PI evidence to maintenance KPIs such as MTBF, MTTR, availability, reliability, and lost-production exposure.

Instructor-led demonstrations are followed by guided work in a realistic plant scenario involving recurrent pump failures, process excursions, and delayed maintenance response. Participants build an asset-based analysis model, investigate a failure sequence, and create role-specific PI Vision views for operations, maintenance, and management. They leave with a documented maintenance performance analysis pack: an AF model design, calculated KPI definitions, Event Frame logic, dashboard wireframes, and an implementation plan that can be adapted to their own PI System. A certificate is awarded on completion.

The programme is suited to experienced maintenance, reliability, operations, and PI professionals who need to make PI System data useful in maintenance planning, engineering reviews, and operational-improvement decisions.

Course objectives

By the end of this course, participants will be able to:

• Configure PI Asset Framework templates, attributes, and asset hierarchies for maintenance-critical equipment
• Validate PI tag quality, timestamp coverage, units of measure, and exception patterns before KPI analysis
• Build PI Analysis Service calculations for availability, runtime, MTBF, MTTR, and maintenance-response measures
• Create Event Frames that capture downtime, trips, operating excursions, and maintenance-relevant event windows
• Investigate recurrent equipment failures through time-series comparison, asset context, and event correlation
• Develop PI Vision displays for asset health, downtime review, maintenance backlog discussion, and management reporting
• Define a traceable maintenance KPI specification linking PI calculations to operational assumptions and data sources
• Produce an implementation roadmap for deploying a PI-based maintenance performance analysis workflow

Benefits of attending

Target competencies

Who should attend

• Reliability Engineers — who need defensible evidence to identify chronic asset failures and prioritize reliability actions
• Maintenance Engineers — who must analyse downtime, repair response, and equipment history to improve maintenance strategies
• Maintenance Planners and Schedulers — who need asset-performance signals to sequence preventive and corrective work
• PI System Administrators and AF Developers — who configure data structures and calculations for engineering users
• Operations Engineers — who need to relate process conditions and operator events to equipment performance
• Asset Performance Managers — who require consistent availability and reliability reporting across critical assets

Requirements and prerequisites

Participants should have practical experience working with maintenance, reliability, operations, or asset-performance data and be comfortable interpreting trends, timestamps, engineering units, downtime records, and basic KPI definitions. Familiarity with the AVEVA PI System is expected, including finding PI tags and viewing time-series data in PI Vision or an equivalent client. Prior exposure to PI Asset Framework is helpful but not essential. Participants do not need to be programmers, database administrators, or PI installation specialists. No advanced statistical modelling or prior experience building AF templates is required.

Training methodology

The five-day programme combines focused instructor-led sessions with hands-on configuration and analysis in an AVEVA PI training environment. Each topic is demonstrated first in PI System Explorer, PI Vision, and PI Data Archive, then applied to a plant maintenance case containing pump trips, changing operating conditions, and incomplete event records. Participants work individually and in small groups to challenge KPI assumptions, diagnose data-quality issues, and review dashboard designs. The final day is an application-planning workshop in which each participant adapts the methods to a nominated asset, failure mode, or maintenance-reporting need.

Course outline

Day 1: PI System foundations for maintenance evidence

• Maintenance performance use cases for time-series operational data
• PI Data Archive tag architecture, point types, and timestamp behaviour
• Finding equipment signals through PI Vision and PI System Explorer
• Data-quality checks for gaps, bad values, compression, and exception reporting
• Engineering units, scaling, and signal suitability for maintenance calculations
• Time alignment of process, equipment-state, and alarm-related data
• Maintenance KPI definitions for availability, MTBF, MTTR, and runtime

Workshop: Participants audit a pump asset's PI tags and produce a data-readiness checklist identifying calculation risks and missing maintenance evidence.

Day 2: Asset Framework modelling for maintainable assets

• PI Asset Framework hierarchy design for sites, areas, systems, and equipment
• AF templates and inheritance for pumps, motors, compressors, and valves
• Attribute configuration using PI Point, static, formula, and table lookup data references
• Naming conventions, categories, and units of measure for maintenance attributes
• Element relative display paths and contextual navigation in PI Vision
• AF model governance, change control, and template versioning
• Mapping equipment context to criticality, duty, and maintenance classification

Workshop: Participants create an AF template and asset hierarchy for a critical pump train, including operational, condition, and maintenance-context attributes.

Day 3: Calculating maintenance and reliability performance

• PI Analysis Service expressions and scheduling options
• Runtime and operating-hours calculations from equipment-state signals
• Availability, downtime, and lost-time calculation logic
• MTBF and MTTR calculation assumptions and boundary conditions
• Rolling-window calculations for repeat-failure and performance monitoring
• Calculation validation against raw PI time-series evidence
• Handling startup, shutdown, standby, and bad-data periods in KPI logic

Workshop: Participants configure and test AF analyses that calculate runtime, downtime, availability, MTBF, and MTTR for the pump train.

Day 4: Event-based investigation and operational visualization

• PI Event Frames for trips, downtime episodes, process excursions, and maintenance windows
• Event Frame generation from trigger conditions and analysis outputs
• Capturing event metadata, severity, duration, and asset context
• Correlating equipment events with suction conditions, vibration, and process demand
• PI Vision trend, value, table, and asset comparison symbols
• Designing maintenance-review dashboards for technicians, engineers, and managers
• Investigating recurring failure patterns through event comparison and drill-down

Workshop: Participants create Event Frames for repeated pump trips and build a PI Vision investigation display that compares event conditions and durations.

Day 5: Deployment, governance, and performance improvement

• Translating PI findings into maintenance actions and reliability recommendations
• KPI ownership, calculation approval, and dashboard governance
• Integration considerations for CMMS and work-order data
• Exception-based reporting and daily maintenance-performance review routines
• Data-security roles and access considerations for PI maintenance views
• Scaling AF templates and analyses across similar asset populations
• Implementation roadmap, success measures, and adoption checkpoints

Workshop: Participants complete a maintenance performance analysis pack and present a 90-day PI deployment roadmap for a selected asset or asset class.

Tools & standards covered

AVEVA PI Data Archive, AVEVA PI Asset Framework, AVEVA PI Vision, PI System Explorer

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions