Asset Maintenance: Using a Layered Approach

NEW - Walk through scenarios to illustrate the use of process and machine condition data and a layered approach to maintenance.

About this course

In this lab, we walk through scenarios to illustrate the use of process data and machine condition data and a layered approach to maintenance via usage-based, condition-based and predictive maintenance. Data sources include traditional plant instrumentation such as PLCs and SCADA, the newer IoT devices, and from machine condition monitoring such as vibration, oil analysis etc. In this lab will also discuss predictive maintenance use cases that require advanced analytics, including machine learning, such as APR (advanced pattern recognition), anomaly detection, and others.

By the end of the course, you will be able to...

  • Discuss PI System’s role in maintenance and reliability – and explain the layered approach to usage-based, condition-based, predictive (simple and advanced) maintenance.
  • Use the techniques learnt in the lab to use the PI System tools to implement usage-based, condition-based, predictive (simple and advanced) maintenance.

Audience

This course is designed for: Experienced PI users.

You are a subject-matter-expert in industrial operations – and have functional responsibilities in process operations and equipment maintenance/reliability. If your role is strictly “process operations” but you work with a separate group of reliability/plant maintenance engineers, you are encouraged to review this content as a team.

Level: Intermediate

Study time: 4 hours

Course Access: Unlimited access. The only exception is the Training Cloud Environment for which you have 30 day access. After those 30 days you can purchase additional access with one of the two options below:

Prerequisites

For this lab, we’ve made the following assumptions:

  • your organization already has a PI System set up
  • you are familiar with OSIsoft and its suite of products
  • you have a few years of experience applying the PI System to solve problems in industrial operations
  • you have an interest in using the PI System data and its capabilities for maintenance & reliability – specifically, understand PI System’s role in usage-based, condition-based and predictive maintenance
  • you can access our YouTube content.

This Course Includes...

  • Videos, discussion opportunities and quizzes to help you learn the material
  • A Cloud Environment accessible for 30 days and configured to complete all the exercises in the course
  • A sharable certificate of completion

Further Information

  • The course is accompanied by an Azure-deployed virtual environment and a workbook, which allows you to walk through the contents in detail at your own pace.
  • This is a self-paced course. Any questions or assistance needed about the material can be asked in this course's space in the OSIsoft PI Square community
  • When you complete the examination at the end of the course, you will receive a certificate of completion which can be shared and directly posted on LinkedIn.
  • For more information about our Online Courses please visit our FAQ page

You can audit the full video lecture content right now on the OSIsoft Learning YouTube Channel

Link to Course Workbook and Condition Based Maintenance eBook Reference

 

Curriculum

  • Getting Started
  • Key Course Information
  • Course Grading Scheme
  • How to Navigate This Course
  • Offline Course Videos for Blocked YouTube Users
  • Course Workbook
  • Training Cloud Environment
  • Cloud Environments Introduction
  • Cloud Environments Instructions
  • Launch Cloud Environment
  • Overview of this course
  • Course Introduction (5:02)
  • [POLL] Who's from what industry
  • [DISCUSSION] Introductions
  • Exercises Overview (6:49)
  • Survey
  • Questions on this part of the course?
  • Exercise 1: Usage Based Maintenance – motor run-hours and valve actuation counts
  • Introduction (0:48)
  • Familiarizing with the AF structure (3:04)
  • Filling out a status attribute with analysis and data (6:34)
  • Creating usage-based run hour attributes (6:13)
  • Creating a usage-based counter (3:16)
  • Our data in PI Vision (1:34)
  • Questions on this part of the course?
  • Exercise 2: Condition Based Maintenance – bearing temperature high alert
  • Introduction (0:48)
  • Familiarizing with the AF structure (2:08)
  • Attributes to monitor the bearing temperature (1:34)
  • Event Frame generation (4:18)
  • Configuring notifications (3:53)
  • Reporting the number and duration of bearing temperature alerts (2:08)
  • Our PI Vision display (1:18)
  • Questions on this part of the course?
  • Exercise 3a: Predictive maintenance (simple) – increasing vibration trend for RUL
  • Introduction (0:57)
  • Examining the attributes (2:50)
  • AF Analytics (3:36)
  • Our display in PI Vision (2:28)
  • Questions on this part of the course?
  • Exercise 3b: Engine failure – Moving from corrective to preventive, to predictive maintenance
  • Engine failure - CM, PM and PdM -early fault detection via machine learning
  • Questions on this part of the course?
  • Exercise 4: Overall health score
  • Condition assessment rules and asset health score
  • Questions on this part of the course?
  • Final Exam
  • Final Exam
  • Course Evaluation

About this course

In this lab, we walk through scenarios to illustrate the use of process data and machine condition data and a layered approach to maintenance via usage-based, condition-based and predictive maintenance. Data sources include traditional plant instrumentation such as PLCs and SCADA, the newer IoT devices, and from machine condition monitoring such as vibration, oil analysis etc. In this lab will also discuss predictive maintenance use cases that require advanced analytics, including machine learning, such as APR (advanced pattern recognition), anomaly detection, and others.

By the end of the course, you will be able to...

  • Discuss PI System’s role in maintenance and reliability – and explain the layered approach to usage-based, condition-based, predictive (simple and advanced) maintenance.
  • Use the techniques learnt in the lab to use the PI System tools to implement usage-based, condition-based, predictive (simple and advanced) maintenance.

Audience

This course is designed for: Experienced PI users.

You are a subject-matter-expert in industrial operations – and have functional responsibilities in process operations and equipment maintenance/reliability. If your role is strictly “process operations” but you work with a separate group of reliability/plant maintenance engineers, you are encouraged to review this content as a team.

Level: Intermediate

Study time: 4 hours

Course Access: Unlimited access. The only exception is the Training Cloud Environment for which you have 30 day access. After those 30 days you can purchase additional access with one of the two options below:

Prerequisites

For this lab, we’ve made the following assumptions:

  • your organization already has a PI System set up
  • you are familiar with OSIsoft and its suite of products
  • you have a few years of experience applying the PI System to solve problems in industrial operations
  • you have an interest in using the PI System data and its capabilities for maintenance & reliability – specifically, understand PI System’s role in usage-based, condition-based and predictive maintenance
  • you can access our YouTube content.

This Course Includes...

  • Videos, discussion opportunities and quizzes to help you learn the material
  • A Cloud Environment accessible for 30 days and configured to complete all the exercises in the course
  • A sharable certificate of completion

Further Information

  • The course is accompanied by an Azure-deployed virtual environment and a workbook, which allows you to walk through the contents in detail at your own pace.
  • This is a self-paced course. Any questions or assistance needed about the material can be asked in this course's space in the OSIsoft PI Square community
  • When you complete the examination at the end of the course, you will receive a certificate of completion which can be shared and directly posted on LinkedIn.
  • For more information about our Online Courses please visit our FAQ page

You can audit the full video lecture content right now on the OSIsoft Learning YouTube Channel

Link to Course Workbook and Condition Based Maintenance eBook Reference

 

Curriculum

  • Getting Started
  • Key Course Information
  • Course Grading Scheme
  • How to Navigate This Course
  • Offline Course Videos for Blocked YouTube Users
  • Course Workbook
  • Training Cloud Environment
  • Cloud Environments Introduction
  • Cloud Environments Instructions
  • Launch Cloud Environment
  • Overview of this course
  • Course Introduction (5:02)
  • [POLL] Who's from what industry
  • [DISCUSSION] Introductions
  • Exercises Overview (6:49)
  • Survey
  • Questions on this part of the course?
  • Exercise 1: Usage Based Maintenance – motor run-hours and valve actuation counts
  • Introduction (0:48)
  • Familiarizing with the AF structure (3:04)
  • Filling out a status attribute with analysis and data (6:34)
  • Creating usage-based run hour attributes (6:13)
  • Creating a usage-based counter (3:16)
  • Our data in PI Vision (1:34)
  • Questions on this part of the course?
  • Exercise 2: Condition Based Maintenance – bearing temperature high alert
  • Introduction (0:48)
  • Familiarizing with the AF structure (2:08)
  • Attributes to monitor the bearing temperature (1:34)
  • Event Frame generation (4:18)
  • Configuring notifications (3:53)
  • Reporting the number and duration of bearing temperature alerts (2:08)
  • Our PI Vision display (1:18)
  • Questions on this part of the course?
  • Exercise 3a: Predictive maintenance (simple) – increasing vibration trend for RUL
  • Introduction (0:57)
  • Examining the attributes (2:50)
  • AF Analytics (3:36)
  • Our display in PI Vision (2:28)
  • Questions on this part of the course?
  • Exercise 3b: Engine failure – Moving from corrective to preventive, to predictive maintenance
  • Engine failure - CM, PM and PdM -early fault detection via machine learning
  • Questions on this part of the course?
  • Exercise 4: Overall health score
  • Condition assessment rules and asset health score
  • Questions on this part of the course?
  • Final Exam
  • Final Exam
  • Course Evaluation