Advanced Diagnostics with Python
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.
Advanced Diagnostics with Python (DA202) will help you understand and execute advanced data modeling techniques beyond traditional Advanced Pattern Recognition with real-life examples of equipment failures and how each could have been detected using various statistical methods including Excel and/or Python. We’ll examine rule-, model-, and case-based predictive analysis, focusing on when and how each should be used and the strengths and limitations of each. Next, students will learn alert management techniques and best practices, utilizing previous data to complete alert management tasks.
To help you retain and develop long-term recall of the course material, over 50% 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
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Diagnostic modeling using limited or incomplete data
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Introduction to rule-based, model-based, and case-based diagnostics methods
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Weighting method exercise for determining faults
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Introduction to Prognostic Methods
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Alert prioritization best practices
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Complete alert philosophy, metrics, and continuous improvement strategies
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Define program goals, business problems, and risk-informed project plans
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Review and reinforce concepts from Data Analysis 102
DA202 Section Descriptions
DA202.1: Diagnostics and 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.
DA202.2: Advanced Data Analytics Modeling with Limited Data
Learn how to utilize multiple advanced modeling tools with very limited data (e.g., less than 60 days of normal steady state data), deploying Python and Excel data analysis techniques to build effective models for new or rarely-run equipment. Topics include data selection when steady state data does not exist, data supplementation utilizing Knowledge Engineering, and modeling with weak data. Exercises include model building and model verification with Excel and/or Python.
DA202.3: Alert Setting for Limited Data
Learn basic alert-setting philosophy and the importance of actionable alerts and setting the direction of the Monitoring & Diagnostics program. Additionally, learn alert setting techniques using different types of calculations, especially when data is limited, incomplete, or non-existent. Topics include actionable vs. non-actionable alerts, box-and-whisker plots, and proper persistence window adjustments. Exercises include continuations of proper alert setting and alert setting iterations.
DA202.4: Program Implementation Planning
Learn best practices for M&D program design, development, and implementation, including the business problem being solved, program goals, and a risk-informed project plan to maximize customer ROI. Topics include equipment and component selection, failure modes and effects analysis (FMEA), sensor and associated software selection, API management, organization change management, and how to calculate return on investment. Exercises include choosing related equipment, performing a FMEA on selected equipment, sensor and software selection, and program implementation.
DA202.5: Program Mission and Objectives Alternatives
Learn program design alternatives, such as:
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Operations-Focused - referring to operational performance, an operations-focused M&D program concentrates on system efficiency.
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Reliability-Focused - referring to equipment and component reliability, a reliability-focused M&D program concentrates on equipment health.
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Site-Based - referring to a single or multi-unit facility, a site-based program focuses on a single cycle.
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Fleet-Based - referring to multiple units spread across multiple sites, a fleet-based program focuses on several types of facilities by utilizing similar models.
Who Should Attend
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Data Analysts
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Asset Monitoring & Diagnostics Program Managers
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Directors or VPs of Data Analytics for manufacturing and utilities
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Information Technology professionals
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Systems Administrators who are responsible for M&D program implementation
Download our 2-page brochure. For more information, contact us today.
