Anvendt maskinlæring · RCM ML Lean RBI More… RCA. ... Ichikawa, RCM/FMEA, FTA, ETA, + 5W Advanced (Linear and Nonlinear) Model Based Set of I/O data, ANN, FuzzyLogic, Kalman etc

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Anvendt maskinlæringViken Teknologiklynge 4.0

Andreas Marhaug


MainTech –Practical solutions to genuine needs. Always!

2000

2014

2016

Mo i Rana

Molde

Trondheim

> 40 000 employees


MainTech: Practical solutions, to needs. 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 OPERATION

Goal

Design Operational context Human MaintenanceNeeds


Agenda

Evolution of maintenance

Why predictive maintenance

Machine 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 routines

Operations

Climate

More…





Operations

Climate

More…

Preventive maintenance

Time





Operations

Climate

More…

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 / Degradation

Context

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, (Physics of failure, formulas etc.)

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, ANN, FuzzyLogic, Kalman etc.

Test and event basedMeasurements, Alarms and Assessments

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

Rule/experience basedFMSA – expert systems, BOOLEAN logic

Unsupervised Machine learningUsing only the relationship between input variables, algorithms


Digital twin for processes and control


Applied digitalization for maintenance use cases

*example photos, not directly related to specific projects


«All roads lead to Rome»

Systematically examine failure modes and look for parameters that could predict failures

Use all available parameters in machine learning to predict failures

Time [month]


XX customer – gas compressor




Alcoa Mosjøen – “the sexy little thing up north”


Alcoa Mosjøen machine learning for maintenance

Project 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 / Degradation

Context

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

Rem

ain

ing

use

ful l

ife [

ho

urs

]

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]

Rem

ain

ing

use

ful l

ife [

ho

urs

]


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]

Rem

ain

ing

use

ful l

ife [

ho

urs

]


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

programKPI’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!


Anvendt maskinlæringViken Teknologiklynge 4.0

Andreas Marhaug

Documents

Anvendt maskinlæring · RCM ML Lean RBI More… RCA. ... Ichikawa, RCM/FMEA, FTA, ETA, + 5W Advanced (Linear and Nonlinear) Model Based Set of I/O data, ANN, FuzzyLogic, Kalman etc