Basic Data Analytics for Utilities
The objectives of any Asset Monitoring & Diagnostics (M&D) program include minimizing the opportunity for catastrophic failure, maximizing machine and cycle efficiency, and eliminating time-based preventive maintenance tasks. Benefits of each of these three objectives rely on understanding the principles behind applications found in M&D programs as well as developing their own applications to meet specific M&D program needs.
Basic Data Analytics for Utilities (DA101: Data Analytics - Basic) will help you understand the analytics tools you use beyond the user manual. You will learn how to be more effective and efficient with your daily tasks, as well as new concepts and techniques to boost your M&D program value. We’ll examine the basics, starting with an introduction to data analytics techniques and moving to alert fundamentals. To help you develop retention and long-term recall of the course material, over 25% of class time is spent on hands-on exercises using visual association tools to break down complex topics. This course prepares you to identify and execute proper analytical techniques to help you develop these in-demand skills.
You Will Learn
A brief history of machine learning and advanced pattern recognition (APR)
Regression, statistics, and first principles techniques for prediction
Single-In Single-Out (SISO), Multi-In Single-Out (MISO), and Multi-In Multi-Out (MIMO) modeling definitions and techniques
Scatter Plots, Time Series diagrams, and Histograms and how each are utilized
The differences between dependent and independent variables and how each are used
How to train, validate, and test data sets for optimal performance
Avoidance cost, direct savings, and how to manage and report each
How to create “actionable limits” to avoid nuisance alerts
How to use a bell curve to determine if a residual is zero-centered and balanced
An introduction to program metrics, cost-benefit analysis, and alert philosophy
DA101 Section Descriptions
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.
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.
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.
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.
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.
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.
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.
Who Should Attend
Asset Monitoring & Diagnostics Program Managers
Directors or VPs of Data Analytics for manufacturing and utilities
Information Technology professionals
Systems Administrators who are responsible for M&D program implementation