AN INITIATIVE OF PRODUCED BY bilder/2018/2.2.6 - Presentation... · ‐ RCM ‐ RCA ‐ RBI ‐...

transcript

AN INITIATIVE OF PRODUCED BY

Session :

Andreas MarhaugHead of applied digitalization

MainTech AS

Gunnar Andreas AarvoldManager Business Development

MainTech AS

Remaining Useful Life Prediction –Lessons learned from aluminum production

Copyright © 2018 by MainTech AS All rights reserved.This presentation or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the author or rights holder.

MainTech – NorwayPractical solutions to genuine needs. Always!

Mo i Rana

Trondheim

> 40 000 employees

MainTech – NorwayPractical solutions to genuine needs. Always!

MainTech: Practical , to genuine . Always.

‐ Project management‐ Engineering‐ Materials‐ FMECA

‐ Lean‐ Applied digitalization‐ Corrosion monitoring‐ Supply chain optimizing

‐ Courses and coaching‐ Organizational

development‐ Lean

‐ RCM‐ RCA‐ RBI‐ CMMS

Solutions?

OPTIMIZED AND RELIABLE OPERATIONGoal

Design Operational context Human MaintenanceNeeds

solutions needs

MainTech – Continues developing Competence

A fantastic week‐ visit from Nancy Regan

MainTech – Continues developing competence

Practical solutions to genuine needs. Always!Mark Brian Chris Rik Plattel

Brian Oxnham and Mark Horton visiting MainTech 2018

Chris James and Rik Plattelvisiting MainTech 2017

Norwegian Society of MaintenanceMainTech ‐ Proud member

Agenda Evolution of maintenanceWhy predictive maintenanceMachine learning vs mathematical models Case study: Digitalization of aluminum production

Evolution of Maintenance

“30 % of periodic maintenance is unnecessary.

Another 30 % might be damaging.” ‐ Emerson

“85 % of equipment fail despite calender based

preventative maintenance” – Boeing

Team Norway alpine ski team

Practical solutions to genuine needs. Always!

More…

Predictive maintenance

Many factors affect reliability:Maintenance routinesOperations

ClimateMore…

Preventive maintenance

ClimateMore…

Preventive maintenance??

Internet of things for maintenance professionals

Collect and analyze data

Predict technical condition

Avoid expensive breakdowns and unnecessary maintenance

Machine learning definition

"Field of study that gives computers the ability to learn without being explicitly programmed“

Arthur Samuel 1959

Mathematical models vs machine learning

Works well for simple relationships For more complex relationships we need to make assumptions

Can describe more complex relationships No assumptions No mathematical proof

Dataset

Mathematical proof

X f(x) y

Dataset

Learning algorithm

X h(x) y

ML for maintenance is a multi‐disciplinary process

Pre‐processing of data

Raw data

Prediction model

Data scienceUnderstanding of data science

Characteristics / DegradationContext

Maintenance organization

Domain knowledge maintenance

Data science knowledge

Train ML algorithms

Source: http://www.tylervigen.com/spurious‐correlations

Diagnostics methods Knowledge based Data driven

Deterministic Model BasedPhysical and chemical calculation models

Simple Statistical MethodsControl limits / Variance / covariance / correlation / anti correlation, etc.

Cause effect basedIchikawa, RCM/FMEA, FTA, ETA, + 5W

Advanced (Linear and Nonlinear) Model BasedSet of I/O data, NN, FL, Kalman mm.

Test and event basedMeasurements, Alarms and Assessments

Supervised Machine LearningLearning set of I/O Error Signature, Pattern Recognition andclassification algorithms

Rule/experience basedFMSA – expert systems

Unsupervised Machine learningOnly the relationship between input variables, algorithms

Applied digitalization for maintenance use cases

*example photos, not directly related to specific projects

Alcoa Mosjøen – “the sexy little thing up north”

Alcoa Mosjøen machine learning for maintenanceProject objectives Can we use existing data for machine learning If not, what data do we need to collect in the future, and how?

Data Ten years of operation or anode factory

Vision for the future All failures are known in advance Correct maintenance is done at exactly the right time

ML for maintenance is a multi‐disciplinary process

Pre‐processing of data

Raw data

Prediction model

Data scienceUnderstanding of data science

Characteristics / DegradationContext

Maintenance organization

Domain knowledge maintenance

Data science knowledge

Train ML algorithms

Predicting remaining useful life of equipment 80% of data is used for training the model 20% year data is used for testing the model

Observed remaining useful life is represented as blue lines Predicted remaining useful life is represented as orange dots Ideally the orange dots should trace the blue line

Remaining

useful life

Time [operation cycles]

First model for predicting remaining useful life The model is trained on all available data

No relationships is observed We are not able to predict remaining useful life

Time [month]Remaining

useful life

Fourth model for predicting remaining useful life The model is trained on a limited dataset Domain knowledge and other methods is used to limit the dataset

In this model we can foresee 25% of failures

Time [month]

Remaining

useful life

Variables relative importance Input for shift plan Input for modifications Input for resource priorities Input for spare parts Input for competency and training Input for …

Maintenance management process – NORSOK Z‐008

Goals and strategyHumans

Improvement measures

PlanningMaintenance program

KPI’s and acceptance criteria

Analysis

Execution

Reporting

Documentation

Supporting systems

Spare parts

Resources

Management and verification

Risk level Availability

General conclusions Predictive maintenance can eliminate unnecessary maintenance and prevent breakdowns Combining diagnostics methods to find real correlations is key to effective predictive maintenance Machine learning for maintenance is a multidisciplinary process; including data scientist, maintenance engineer, and technician Machine learning affects all aspects of the maintenance management loop

Most important: There are no shortcuts to anywhere worth going!

AN INITIATIVE OF PRODUCED BY

C O N T A C T

Andreas MarhaugHead of applied digitalization

MainTech AS

Gunnar Andreas AarvoldManager Business Development

MainTech AS

+47 950 36 070

andreas.marhaug@maintech.no

+47 982 09 752

gunnar.aarvold@maintech.no

Copyright © 2018 by MainTech AS All rights reserved.This presentation or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the author or rights holder.

AN INITIATIVE OF PRODUCED BY bilder/2018/2.2.6 - Presentation... · ‐ RCM ‐ RCA ‐ RBI ‐...

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