AN INITIATIVE OF PRODUCED BY bilder/2018/2.2.6 - Presentation... · ‐ RCM ‐ RCA ‐ RBI ‐ CMMS ... Ichikawa, RCM/FMEA, FTA, ETA, + 5W Advanced (Linear and Nonlinear) Model Based

AN INITIATIVE OF PRODUCED BY

Session :

Andreas MarhaugHead of applied digitalization

MainTech AS

Gunnar Andreas AarvoldManager Business Development

MainTech AS

2.2.6

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.

Copyright © 2018 – Do not use without permission or proper licence from the author or rights holder 2

MainTech – NorwayPractical solutions to genuine needs. Always!

2000

2014

2016

Mo i Rana

Molde

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!

RCM

ML

Lean

RBI

More…

RCA


Predictive maintenance

Many factors affect reliability:Maintenance routinesOperations

ClimateMore…




ClimateMore…

Preventive maintenance

Time




ClimateMore…

Preventive maintenance??

Time


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

Raw 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

Raw 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

[hou

rs]

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

[hou

rs]


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

[hou

rs]


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

[email protected]

+47 982 09 752

[email protected]

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.

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AN INITIATIVE OF PRODUCED BY bilder/2018/2.2.6 - Presentation... · ‐ RCM ‐ RCA ‐ RBI ‐ CMMS ... Ichikawa, RCM/FMEA, FTA, ETA, + 5W Advanced (Linear and Nonlinear) Model Based