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Usage-based, condition-based and predictive maintenance with the PI System - a layered approach

Blog Post created by gopal on May 5, 2020

10-minute read

 

 

 

 

Intro video 

(watch in full screen mode)


Audience for this lab

  • An experienced PI System user
  • 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 lab content as a team


This blog is an extract from the Summary pages of the Lab Manual - enough details are included in this blog to give you a feel for the lab content even if you skip the hands-on portions of the lab. 

 

Lab description

PI World 2020 - Hands-on Lab - San Francisco

This event was cancelled due to COVID-19. However, an on-line version of this lab is at:

https://learning.osisoft.com/series/power-user/asset-maintenance-using-a-layered-approach

 (must be purchased separately or  you must have a PI Dev Club subscription).

 

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.

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.


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.


Usage-based maintenance includes utilizing operational metrics such as motor run-hours, compressor starts/stops, grinder tonnage etc. And, condition-based maintenance utilizes measurements such as filter deltaP, bearing temperature, valve stroke travel, and others. Predictive maintenance can be using simple analytics such as monitoring vibration (rms, peak etc.) to predict RUL (remaining useful life), heat-exchanger fouling to schedule cleaning, etc.

 

In this lab, we will also discuss predictive maintenance use cases that require advanced analytics, including machine learning, such as APR (advanced pattern recognition), anomaly detection, and others.

 

Who should attend? Experienced PI user

 

Summary

This lab’s objective is to walk-through the use of equipment and process data for a layered approach to uptime and reliability via usage based, condition-based and predictive – simple and advanced (machine learning) - maintenance.

 

  • Exercise 1: Usage base maintenance – motor run-hours and valve actuation counts

 

  • Exercise 2: Condition-based maintenance – bearing temperature high alert

 

  • Exercise 3a: Predictive maintenance (simple) – univariate (single variable) – increasing bearing vibration trend extrapolated to predict time to maintenance

 

  • Exercise 3b: CM, PM and PdM - Using engine failure history to support the decision criteria and quantify the benefits for moving from corrective maintenance (CM) to preventive maintenance (PM) to predictive maintenance (PdM)

For details of PdM multivariate model development, i.e. early fault detection via machine learning for predicting failure within a time window, see...more.

 

  • Exercise 4: Asset health score - utilize multiple condition assessment rules with appropriate weighting factors to process/equipment indicators to calculate an overall asset health score

 

Exercise 1: Usage-based Maintenance - motor runhours and valve actuation counts

In this exercise, motor run-hours and valve actuation counts are calculated to serve as a basis for usage-based maintenance.

We use an ice-cream factory running two process lines – Line 1 and Line 2, with two mixers on each line.

 

The hands-on portion includes building the run-hours calculations in AF, and the relevant PI Vision displays as shown below.

Exercise 2: Condition-based maintenance - high bearing temperature alert

In this exercise, we assess the condition of an equipment by calculating metrics that can serve as leading indicators of equipment failure or loss of efficiency – for example, bearing temperature to evaluate the pump bearing condition. 

 

We track the alerts for the bearing temperature and then discuss the use of PI Notification to send an email or use the web service delivery channel to notify a system (i.e. triggering a work order in a work management system such as SAP or IBM Maximo) for follow-up action. 

 

The bearing temperature events are viewed in a watchlist in PI Vision – see screens below.

 

 

 

 

 

Exercise 3a: Predictive Maintenance (PdM) - bearing vibration trend 


For certain classes of process equipment, their condition can be evaluated by monitoring some key metric, such as efficiency for a compressor, fouling for a heat-exchanger, bearing vibration on a pump, etc. Often, these metrics show a pattern with time – and, linear, piece-wise linear or non-linear trend can be extrapolated to estimate remaining-useful-life.


The screen below shows increasing vibration over time (100+ days). The trend can be extrapolated to estimate when it will reach a defined threshold and schedule maintenance.

 

Exercise 3b: Engine failure - CM, PM and PdM - early fault detection via machine learning

In this Exercise, we use engine operations data and failure history to guide maintenance decisions, and quantify the benefits when moving from CM to PM to PdM:

 

 CM - Corrective maintenance - break-fix
 PM - Preventive maintenance - run-hours based
 PdM - Predictive maintenance - early fault detection (via machine learning - multi-variate condition assessment)

 

In a deployment with about 100 similar engines, sensor data such as rpm, burner fuel/air ratio, pressure at fan inlet, and twenty other measurements plus settings for each engine – for a total of about 2000 tags – are available. On average, an engine fails after 206 cycles, but it varies widely - from about 130 to 360 cycles – each cycle is about one hour.

 

With a given failure history for the engines and known costs for PMs vs. repairs, we calculate the benefits in moving from CM to PM to PdM.

 

As part of the lab, we discuss:

 Can you quantify the $ spent on maintenance with the break-fix strategy (corrective maintenance)?
 A sister company with similar operations, failure history and repair/PM costs uses the median failure rate of 199 cycles for PMs. Should you adopt this?
 Can you do better? If so, after how many cycles will you do the PMs?
 Can you quantify the benefits in moving from corrective to run-hours based PMs?

 

 If engine operations data can be used for early detection of failure – say, within 20 cycles of a failure with 100% certainty – if and how much will you save by using PdM vs the PM approach

 

For details of PdM model development, i.e. early fault detection via machine learning for predicting failure within a time window, see...more.

 

Exercise 4: Multiple condition assessment rules and asset health score

In this Exercise, you apply the appropriate condition assessment rules and corresponding weighting factors to process/equipment measurements to calculate an overall asset health score.

 

It uses AF Analytics to convert a “Raw Value” (sensor data) to a normalized i.e. a “Case Value”. And then, by applying a Weight%, it is transformed to a Score.

 

Each measurement gets a normalized weighted score (0 to 10) by applying a condition assessment rule.  And, then the normalized scores are rolled up to arrive at a composite asset health score. The Weight% applied to each attribute depends on its contribution to the overall asset health.

 

The composite asset health score ranges from 0 to 10 (0=Good, 10=Bad)

 

A Transformer asset health score example is used with the following measurements:

 

  • LTC (Load Tap Changer) counter operations
  • LTC through neutral count
  • DGA (dissolved gas analysis) detectable acetylene
  • DGA high gas rate of change
  • Low dielectric
  • High water
  • Low nitrogen pressure

 

An example Transformer template is shown below:

And, as you configure Transformers using these templates, the composite health score is periodically calculated by PI System Asset Analytics.

The composite health score for transformer TR01 is 2 i.e. asset is in good health (0=Good, 10=Bad).

 

Recap

 

 

 

In this lab, we covered 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.

 

Quiz

The quiz is not intended to test “recall” – but is more about synthesizing the concepts from the lab and the PI System's role in the maintenance decision process.

 

1. To implement run-hours or similar counter-based logic, it is a prerequisite to have such counter measurements directly from the SCADA or PLC or other such instrumentation for the equipment.
_ True _ False

 

2. APM (asset performance management), CBM (condition-based maintenance), CM (condition monitoring) etc. – all broadly refer to the use of continuous monitoring and analytics in the maintenance decision process

_ True _ False

 

3. Oil analysis is a form of predictive maintenance that focuses on evaluating the condition of an equipment’s lubricant… (extract from https://www.onupkeep.com/learning/maintenance-types/oil-analysis). The term “predictive maintenance” used on the web page is in line with that used in the lab. 

_ True _ False

 

4. In Exercise 3b, the median failure of 199 cycles is the recommended threshold for PMs.
_ True  False

 

5.Asset health score (Exercise 4) is more useful for comparison among peer-group assets than for comparing across different asset classes (motors, transformers, heat-exchangers…).

_ True _ False

 

6. In a mature maintenance setting, you will typically have a mix of all the different layers – reactive, usage-based, condition-based, and predictive deployed.
_ True _ False

 

7. For usage-based, it is recommended to do lifetime total calculations instead of doing meter reset in PI after every PM.
_ True _ False

 

8. When adopting predictive maintenance, you must start with advanced machine learning models for quick wins.

_ True _ False


9. Exercise 2 shows PI Notification via email for high bearing temperature alert. Event frames and PI Notification (web service delivery channel) can also be utilized as a basis for triggering a service request in work management systems such as SAP PM, IBM Maximo, and others.
_ True _ False

 

10. To incorporate the layered approach to maintenance, you can simultaneously deploy one or more of the appropriate techniques (reactive, usage-based, condition-based, and predictive) for an equipment and its components.

_ True _ False


11. To adopt the linear extrapolation example illustrated in Exercise 3a, you must fit the entire vibration history to ensure that all available data is used.
_ True _ False


12. Generically, the reason code manual entry feature for an event (Exercise 2) in PI Vision allows a pick list based on a hierarchical list of text entries.
_ True _ False


13. For usage-based, it is a good practice to use year-to-date calculations as illustrated by:

 

 

_ True _ False

 

14. AF calculations can include machine condition data such as oil analysis, vibration data etc. that may not be natively in PI tags but are available externally and referenced via table look-ups or other data reference methods.
_ True _ False

 

15. Integration with work management systems such as SAP-PM, IBM Maximo etc. is a prerequisite to adopting the layered approach to maintenance shown in this lab.
_ True _ False

 

Lab Manual

Lab manual is attached.

Link to the lab in OSIsoft Learning

 

Other resources

Incorporating Condition Monitoring Data - Vibration, Infrared and Acoustic for Condition-based Maintenance 

Getting started with IIoT sensor deployment 

Operationalizing analytics - simple and advanced (data-science) models with the PI System

A layered approach to maintenance - PI World 2020 Q&A session slides

Introduction to Asset Monitoring and Condition-based Maintenance with the PI System - PI World 2019 video and slides 

Transformer monitoring and maintenance at Alectra Utilities

Condition Monitoring Using Statistical & Machine Learning Models in PI AF at TC Energy

 

Outcomes