A Training Plan Customized for Your Needs
Each Integral Analytics training course is divided into individual sections. Each section is designed as a stand-alone course so you can start at the level best suited for your needs. Prerequisites are highly recommended to maximize student success.
DA101.1: Analytics Toolbox Orientation
Learn the need for the “Analytics Toolbox” rather than simply relying on Advanced Pattern Recognition (APR) for your M&D program. Topics include the history of machine learning and APR, regression techniques, statistical techniques, and first principles techniques. Duration: 1 hour | Prerequisite: None
DA101.2: Explore Data Analysis Part 1
Analyze different variable relationships using time-series, x-y scatter, correlation analysis, and 3D scatter plots, including dependent-independent variables as well as “influence” and “predicted“ variables. Topic include analytics capabilities, the need for specific sensors to detect failure modes, and avoided costs versus direct savings. Exercises include scatter plot analysis, time-series analysis, and histogram analysis. Duration: 2 hours | Prerequisite: None
DA101.3: Explore Data Analysis Part 2
Continue learning how to analyze different variable relationships using more complex modeling techniques combined with data requirements and data fidelity. Topics include data set types and purposes, data historian considerations, and model and data relationships. Exercises include exploratory data analysis and advanced scatter plot analysis. Duration: 2 hours | Prerequisite: DA102.2
DA101.4: Analytics Algorithm Basics
Assess how supervised learning compares to unsupervised learning and acquire more advanced techniques for regression, clustering, and vector-based analysis. Attendees also learn when a model is overfit and when one spills over into another model negatively affecting the output. Topics include regression analysis, clustering analysis, vector-based analysis, and supervised and unsupervised learning. Duration: 2 hours | Prerequisite: DA101.1
DA101.5: Analytics Model Development - Basic
Learn how to scope assets for criticality and available data, model design criteria, and how to tell if program capabilities are being oversold. Additionally, attendees also learn proper model documentation and workflow. Topics include model scoping, model coverage, model deployment consideration, data selection, and model documentation. Exercises include regression model analysis and APR model - regression model comparison. Duration: 2 hours | Prerequisite: None
DA101.6: Alert Setting Fundamentals Part 1
Understand the importance of and learn how to set actionable limits, minimize nuisance alerts and increase the effectiveness of their M&D program. Gain experience working with actionable alert settings. Topics include alert settings, alert definitions, actionable alerts, and the iterative refinement process. Duration: 2 hours | Prerequisite: None
DA101.7: Alert Setting Fundamentals Part 2
Learn how to start an Alert Philosophy, how to analyze residual quality using statistics, statistical analysis, and trend analysis, and how to automate value tracking, moving from lengthy manual processes to automated processes. Topics include alert philosophy, metrics vs. measures, and residual analysis. Duration: 2 hours | Prerequisite: DA101.6
DA201.1: Diagnostics & Prognostics Methods
Continue to explore multiple technologies and when each should and should not be used, including rule-based, model-based, and case-based technologies, neural networks, and pure database query, including the pros and cons of each. Learn forecasting and mean-time-to-action techniques when determining action limits, determining reliability, stressors, symptoms, and effects. Topics include rule-based analytics, case-based analysis, and Bayesian Belief Networks for more complex diagnostics. Exercises include weighting for simple diagnostics, Bayesian Belief Networks for more complex diagnostics, and Time-to-Action forecasting. Duration: 4 hours | Prerequisite: None
DA201.2: Advanced Data Analytics Modeling
Build upon the models that were developed in DA101 to cover additional operational modes (transients and steady-state) and use advanced capabilities of modern machine learning techniques. Additionally, simple models from previous exercises are compared to more advanced models to determine which perform optimally. Comparison and contrasts are discussed to learn trade offs of simple vs. advanced analytical models. Topics include steady-state and transient analysis, introduction to machine learning, and moving beyond Advanced Pattern Recognition. Exercises include regression modeling, Advanced Pattern Recognition modeling, and residual analysis for alert setting. Duration: 2 hours | Prerequisite: DA101 (all modules)
DA201.3: Alert Management Practices Part 1
Learn basic alert-setting philosophy and the importance of actionable alerts. Using box-and-whisker plots, learn how to set residual alerts in Advanced Pattern Recognition tools to maximize efficiency and minimize false or non-actionable alerts. Topics include actionable vs. non-actionable alerts, box-and-whisker plots, and proper persistence window adjustments. Exercises include continuations of proper alarm setting and alarm setting iterations. Duration: 2 hours | Prerequisite: DA101.7
DA201.4: Issue Management - Process and Communications
Learn the link between model management, alert management, and case management, all from frequent points of view, including the data analyst, unit operations, and a manager or director. Also, learn how to determine response actions when faced with limited data, how to interact with work management systems, and Monitoring & Diagnostics center operations. Topics include keeping the big picture in mind, how to manage you or your customer’s program, and how to continually improve. Exercises include properly reacting to critical-to-generation turbine bearing oil temperatures with limited data. Duration: 2 hours | Prerequisite: DA201.3
DA201.5: Alert Management Practices Part 2
Learn how to create a strategy for prioritizing alerts, choosing persistence windows for modeled components that drive actions. Also, learn how to develop a remediation strategy for previous or poor practices that led to numerous, persistent alerts with no value. Topics include prioritization, consistency, organization, and establishing a routine. Exercises include establishing a prioritization strategy and establishing a remediation strategy for numerous non-actionable alerts. Duration: 2 hours | Prerequisite: DA201.3
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