Overview of Advanced Chart Types

Radar

Compares multiple performance criteria on axes radiating from a common center; highlights balance and imbalance.

Marimekko

Uses column width together with the distribution within each column to show total magnitude and composition.

Pareto2D

Provides prioritization by showing absolute values with columns and cumulative percentage with a line.

Pareto3D

Preserves the analytical logic of Pareto2D while enhancing presentation impact through the perception of depth and volume.

The right chart for the right question For questions such as “Is the system balanced?”, “What is the composition of the total load?”, and “Which problem should we address first?”, the Radar, Marimekko, and Pareto families are respectively more appropriate.

Radar — Multidimensional Performance and Balance Analysis

A Radar (Spider) chart displays multiple performance indicators on axes radiating from a common center. Each axis represents a criterion, while the closed polygon represents a system, unit, time period, or scenario. This makes it possible to see not only a single value, but also the shape and balance of the performance profile.

Basic Structure

  • Center: starting or minimum value.
  • Axes: criteria such as availability, efficiency, alarm intensity, downtime, and maintenance compliance.
  • Closed area: the performance profile of a system, unit, or scenario.
  • Normalized scale: making indicators with different units comparable.

Illustrative Radar Chart

Availability Efficiency Alarm Stability Maintenance Load Tracking

The shape of the area represents performance balance, while the distances along the axes show the relative level of each criterion.

Use in SCADA and Energy Systems

  • Plant performance: availability, generation efficiency, alarm intensity, unplanned downtime, and maintenance compliance.
  • Alarm profile: distribution of mechanical, hydraulic, electrical, instrumentation, communication, and safety alarms.
  • Maintenance performance: preventive maintenance, predictive maintenance, MTTR, schedule compliance, and spare-parts availability.
  • Unit comparison: relative performance profiles of units.
  • Seasonal operation: flow, cooling, oil temperature, efficiency, cavitation risk, and alarm intensity during summer/winter periods.
  • Load-following capability: load increase/decrease, gate response time, frequency deviation, power oscillation, and alarm triggering rate.
  • Before/after maintenance: multidimensional comparison to determine whether maintenance has produced a real improvement.

Example Radar Scenarios for Hydroelectric Power Plants

  • Turbine-generator health status: bearing temperature, vibration, oil pressure, oil temperature, electrical load stability, and efficiency.
  • Hydraulic auxiliary systems: oil pressure, oil cleanliness, filter condition, pump operating ratio, leakage alarms, and cooling performance.
  • Alarm profile: comparison of mechanical, hydraulic, electrical, instrumentation, and safety alarm intensities.
Operational Insight If vibration and bearing temperature rise simultaneously, the radar shape can provide a quick visual indication that a mechanically originated problem should be investigated.

Marimekko — Combined Visualization of Composition and Total Magnitude

Marimekko (Mekko / Mosaic) combines two different dimensions of data within the same visual structure. Unlike conventional stacked bar charts, both column width and the heights within each column carry meaning. This allows the total magnitude of a category and the distribution of that total across its subcomponents to be read simultaneously.

Basic Structure

  • Width: the category's total magnitude or total share.
  • Height: the proportion of subcomponents within the category.
  • Area: width × height provides a strong visual indication of total share/impact.

Illustrative Marimekko

Plant A Plant B Plant C D Width = total • height = component proportion

Column width represents total magnitude, while the heights of internal sections represent composition.

Use in SCADA and Energy Systems

  • Plant capacity distribution: installed capacity and fuel/source composition.
  • Regional load-source: regional total load and the composition of source types.
  • Alarm load: units' total alarm share and alarm classes.
  • Power generation × alarm intensity: alarm characteristics during low, medium, nominal, and peak load periods.
  • Maintenance type × alarm reduction effect: the impact of mechanical, hydraulic, electrical, and instrumentation maintenance.
  • Flow range × turbine behavior: stable operation, cavitation, vibration, and alarm/downtime ratio.
  • Season × operational problems: distribution of the problem composition by period.

Example for HPP Alarm Analysis

Columns can be defined as turbine, generator, hydraulic system, gates, transformer-switchyard, and instrumentation. The sections within each column can show the proportions of mechanical, hydraulic, electrical, instrumentation, and safety alarms.

Reading Example If the hydraulic system column is wide and most of its content consists of hydraulic alarms, both the magnitude of the total alarm load and the hydraulic nature of the problem can be seen simultaneously.

Pareto2D — Prioritization and Root Cause Analysis

Pareto2D uses the principle that “a small number of causes can lead to a large number of outcomes” to show which problems should be addressed first. It is a powerful decision-support tool for SCADA alarm management, energy losses, maintenance, and downtime analysis.

Basic Structure

  • Columns: absolute values such as alarm count, number of failures, downtime, or lost energy.
  • Cumulative line: cumulative percentage within the total.
  • X-axis: alarm, failure, equipment, or downtime categories.
  • Left Y-axis: count, duration, or energy loss.
  • Right Y-axis: cumulative percentage.
  • Ordering: categories are arranged from largest to smallest.

Illustrative Pareto2D

Example priority threshold Categories — largest to smallest

Columns represent absolute values; the line represents cumulative percentage.

Use in SCADA and Energy Systems

  • Alarm Pareto: the first few alarms that account for most of the alarm load.
  • Generation loss Pareto: the lost-energy impact of downtime causes.
  • Maintenance failure Pareto: number of failures or downtime by equipment type.
  • Unit-based Pareto: units with a disproportionate share of the total problem.
  • Post-maintenance Pareto: alarm types that continue.
  • Seasonal Pareto: dominant problem types by period.

Example Pareto Scenarios for Hydroelectric Power Plants

  • By alarm name: turbine bearing temperature, hydraulic oil temperature, gate opening time, vibration, level sensor, and other alarms.
  • By subsystem: hydraulic system, turbine, generator, gates, instrumentation, and electrical/switchyard systems.
  • By downtime: gate failure, transformer protection trip, excessive turbine vibration, and hydraulic pump failure.
  • By maintenance type: mechanical, hydraulic, electrical, and instrumentation maintenance activities.
  • By energy loss: gate control problems, hydraulic oil overheating, frequency instability, and sensor faults.
Decision-Support Example If the first few alarms account for most of the total alarms, alarm rationalization, threshold review, and maintenance priorities can be directed toward this group. The percentage values in the source text are provided as examples.

Pareto3D — Powerful Visual Presentation of Pareto Analysis

Pareto3D preserves the basic analytical logic of Pareto2D; it presents absolute values with 3D columns and cumulative percentage with a line. The perception of depth and volume enhances communication, particularly in management presentations.

Illustrative Pareto3D

A B C D E 3D columns = absolute value • line = cumulative percentage

3D columns with emphasized front, top, and side faces, together with the cumulative line.

Use in SCADA and Energy Systems

  • Alarm Pareto: visually highlighting critical alarm groups.
  • Generation loss Pareto: presenting the lost-energy impact of downtime causes in management presentations.
  • Maintenance failure Pareto: visual presentation of the number of failures or duration by equipment type.
  • HPP alarm analysis: presentation of Pareto results based on alarm names, subsystems, downtime, units, and maintenance activities.
3D Usage Note Pareto3D is not a more accurate analysis method than Pareto2D. Depth only strengthens visual presentation. For precise comparisons, actual values, axes, and cumulative percentages must be clearly readable.

Comparison of Chart Types

Chart Key question Data approach Primary use
Radar Is the system balanced? Multidimensional normalized criteria Performance, health, unit comparison
Marimekko How is the total magnitude formed? Width + component proportion Capacity, alarm load, source composition
Pareto2D Which problem should we focus on first? Absolute value + cumulative % Alarm, failure, downtime, energy loss
Pareto3D How can we present the Pareto result effectively? 3D absolute value + cumulative % Management and presentation screens

Selection Approach in EOS LIVE DASH

  1. Performance profile and balance are being analyzed: Radar.
  2. Total magnitude and the distribution of subcomponents are to be viewed together: Marimekko.
  3. Prioritization and root cause analysis are required: Pareto2D.
  4. The visual presentation impact of Pareto analysis is to be enhanced: Pareto3D.
  5. In HPPs, thermal power plants, or other energy facilities, charts should be evaluated together with actual SCADA values, measurement units, and the data collection period.
Data Interpretation The charts and percentage examples on this page are provided for conceptual/illustrative purposes. Actual alarm, generation, downtime, maintenance, and loss decisions should be based on real data obtained by EOS LIVE DASH.
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