Lessons learned in Heavy Assets Predictive Maintenance

Serigne Gaye

Senior Industry Consultant

TERADATA

5 Octobre 2017

– Demand is increasing while budgets are shrinking

– Infrastructure aand Rollong Stock are under stress

– Regulatory bodies are more demanding on Quality

of Service

– Trains have to be on-time and mission ready

– MRO costs need to be drastically reduced

– Sacrifying Customer Service for Cost reduction is not an option

Challenges

Doing more with Less

Safety Availability Costs Maturity

Air Forces X X X

Airlines X X X

Aircraft Manufacturer X X X

Aircraft Engines Manufacturer X X X

Railway Company X X X

Rolling Stock Manufacturer X

Turbine Manufacturer X X X

Truck / Tractor manufacturer X

Manufacturing Process X X

Electrical Grid X

Telco X

Mining Industry X X X

Facility Management

Ol & Gas

Car manufacturer

Maturity driven by safety, availability and TCO

Even though Railway

pays lot of attention

to availability and

TCO, it’s still lagging

behind on predictive

maintenance

Hard constraint

Average constraint

Soft Constraint

Use Cases

• Predictive Maintenance program has produced for a fleet of 120 helicopters: – 11 % increase in fleet availability – 41 000 Maintenance Man Hours saved

in a year

US Army

Caterpillar

• 3350 trains/day on a 32,000 miles network

• Infra red sensors installed every 20 miles – 20 millions readings / day – Monitioring of mechanical wear,

overheating and derailments – 1500 problems qualified every day

• Reduction of damages, delays and customers claims

• Monitoring and analysis of Truck engines sensor data has led to 100 M$ savings in executing Performance Based Contracts

Navistar

• Telematics data is enabling – Failures predicton – Fleet operator notification – Proactive routing od spare parts

• Logistic response enhanced from 7 to 2 days

More availability and 5-10 % reduction in life cycle costs

Union Pacific

Siemens Vision 2020

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Identifying risks and anticipating breakdowns

Sensors continuously gather a breadth of data

Most of the data is analysed on the fly

Drafts are identified

Alerts are triggered on abnormal situations

Predictive Maintenance

• «Performance-based-contracting

• One delay out of 2300 trips

• Valero E est the reliable RENFE High Speed Train

• Big market share on Madrid – Barcelone leg

Identifying risky situations

Market share Railway vs Airlines

Benefits

Diagnostics Health & Performance Awareness

Working OK Failure occurs Collateral damage Precursor Faults Drift starts Fa

ilure

cyc

le

An

aly

tic

al

fu

nc

tio

na

lity

Monitor and trend fault progression

Predict Continuously Remaining Life

Assess health

Prognostics & Health Management

Incipient Fault propagation

Detect and isolate precursor faults Identify drifts

Explore events prior to failure

Unveil failure signature

Impact of Predictive Maintenance

Before After

Preventive Corrective Preventive

Predictive

Corrective

The amount and mix of Maintenance workload will change

− Develop a deeper knowledge on

equipment health and aging effects

− Improve Equipmement reliability and

maintainability

− Optimise Maintenance Planning and

Execution

– Make better trade off between

Repair vs. Leave in field with Partial

Mission readinesss

– Increase the sharing ot equipmenent

life cycle data

– Process Reengineering

Focus on Predictive maintenance

Health and performance Surveillance Scrutinise asset operations & pathologies

Material Management Predict spare parts demand and align supply & inventories

Maintenance & Repair Efficiency

Accelerate MRO tasks completion

Intelligent Asset Tracking Analyse asset demography

LifeCycle Costing Planning and controlling MRO costs over the asset life

Maintenance Planning Fine tune the maintenance ailor maintenance pro

Asset Improvement & retrofit Upgrading and extending asset capabilities

Tra

ck

Pla

n

Execu

te

Value first ! Post Maintenance

Test Hours

Gains

Operations interruptions

Over Maintenance

Maintenance Scheduling

False alarms & No Faults

Found

OPEX

CAPEX

Spares

Maintenance Man Hours

Energy Consumption

Inspection Hours

Maintenance Induced Damage

Collateral Damages

Event Driven

Maintenance

Supply Chain

Pipe Line

Inventory Sizing &

Mix

Repair vs.

Replace

Obsolescence

Asset Life

Fleet size

Logistic footprint

“Waiting for Parts”

Turn Around Times

Focus first on organs that actually drive failures

Shafts

Gears

Bearings

compressors, turbines, pumps, motors, etc

Leverage any kind of data

BEARING FAILURE

ODM, EBM, VIBES, RLI, SWAN

COMBUSTOR/HPT EROSION

EDMS

BEARING FAILURE

ODM, EODM, EBM, VIBES, RLI,

SWAN

FAN 1 ROTOR TIP/ CRACKS

(ECS, BVM, AFD, IDMS,

EDMS, VIBES, RLI, SWAN

BULK OIL

DEGRADATION

OCM, ODM, EBM

VAPOR PHASE COKING

OCM, ODM, EODM, EBM

BULK OIL CONTAMINATION

OCM, EBM, ODM, EODM

OVERFLOWING / BLOCKED

FUEL NOZZLE

EDMS, VIBES, RLI, SWAN)

GEARBOX FAILURE

VIBES, RLI, SWAN

LPT BLADE RUB

EDMS,VIBES, RLI,SWAN

RLI Robust LASER Inferometer

SWAN Stress Wave Analysis

PZT Piezoceramic Patch Crack Detection

AFD Acoustic FOD Detector

IDMS Ingested Debris Monitoring

BVMX Blade Vibration Meter

ODM Oil Debris Monitor

EBM Electrostatic Bearing Monitor

EDMS Engine Distress Monitoring

ECS Eddy Current Blade Sensor

VIBES Vibrations sensor

EODM Eletrosatatic Oil Debris Monitor

Failure modes

Understand the dynamic of the machinery

Sensor Data Data characteristic

Shafts

Gears

Bearings

• Failure of outer/inner races, rollers or cage

• Tooth pitting

• Gear crack

• Wear

• Misalignment

• Unbalance

• Bend

• Crack

• Rubbing

• Vibration

• oil debris

• acoustic

• emission

• Vibrations

• high noise in raw data

• High noise

• Signal modulatIion (coupling with bearing, shaft transmission path)

• Vibration signal is relatively clean and harmonic

• frequency components of rotating speed can indicate the defects

• Vibration

• oil debris

• Acoustic emission

Pick and train relevant analytical technique

Fourier Transform Analysis

STFT Analysis

Wavelet Packet Decomposition

Autoregressive Modeling

Feature extraction

Pattern Recognition Forecasting (Remaining Life)

Diagnosis

Feature Map

Hidden markov Model

Bayesian Network

Neural network

Logistic Regression

Feature Map

Statistical Pattern Recognition

Hidden Markov Model

Autoregressive Moving Average

Match Matrix Prediction

Fuzzy Logic Prediction

Neural Network Predicton

Visualise and share

Flux

Hiérarchie

Accord

Affinité

Enlarge progressively your data scope

Operational data Engineering Data Repair Shop DAta Supply Chain Data

PDM data CRM Data Key Functional Scope

Track Schedule Disptach

Equipment Environment

Client Operations

Subsystem

Technical event

Maintenance Task

Maintenance Report

Maintenance Work Order

Maintenance Plan

Configuration Envelope

Engineering Change

Maintenance Policy

Maintenance Instructions

Supplier

Organ Manufacturer Delivery

Transport Localisation

Logistic Provider

Purchase Order

Inventory

Warranty

Claim

17

Adopt a step by step approach

Assess Plan Prioritize

Data Readiness Identify data

requirements and their

availability, quality and

timeliness

Prioritisation

Assess BIO value

versus deployability

9

3

5

7 10

Deplo

yability

Business impact

+

- -

6

11

2 8

4

12

13

Low

High

Ea

sy

Ha

rd

Low

High

High Value

Easy to deploy

Data ready

Data Readiness

Process Change

Deployment Roadmap

Reliability

Availability

Inventory Sizing

Turn Around Times

Service Chain Visibility

Obsolescence

Health

Maintenance schedulIng

Q1 Q2 Q8

Time

. . .

1

+

Develop/mature BIO

content

Cash

flo

w

Cost

reduct

ion

Cost

avoid

ance

Revenue

Business Impact Identify & valuate

Business improvement

Opportunities (BIOs)

Bu

sin

ess

va

lue

In-service Performance

Detectability

Impacts of Mods

Maintainability

MRO policy

Q5

Prioritisation

Contract Fulfilment

Asset contribution

Segmentation

Costs drivers

Profitability

Lifecycle costs

Inventory Sizing

Costs drivers

Segmentation

Q & A

Thank you !