understanding and applying different levels of machinery ......automation | valves | measurement |...

Automation | Valves | Measurement | Process Control

Understanding and Applying Different Levels of

Machinery Condition MonitoringPresenter: Carl Sheehan P.Eng.

September 15, 2020


Agenda:

• What is Condition Monitoring?

• Technologies for Condition Monitoring

• Reliability Strategies


What is Condition Monitoring?


Condition Monitoring Enables Moving from Reaction to Prediction

Reaction

Equ

ipm

en

t H

eal

th

0%

100%

Time

advanced warning gives plant time to

respond

Downtime

Prediction & Performance

Re

pair Eq

uip

me

nt

Pla

n M

ain

ten

ance

Ord

er

Par

ts

Ass

ign

Pe

rso

nal

Sch

ed

ule

Se

rvic

e c

on

trac

tor

Reduced

Identify Failure Precursors – Leading Indicators


An example.


Performance and Reliability are Key

Safety

3X fewer

Recordables and process

incidents

Process

Incidents

4th 1stQuartiles

Recordables

Production

20% lower operating costs

Utilization

4th 1stQuartiles

Operating Costs

10% higher Utilization Rate

4% higher availability

Half the maintenance

costs

Reliability

Availability

Maintenance

4th 1stQuartiles

Emissions

Energy Use

CO2 Emissions

4th 1stQuartiles

30% lower emissions

30% less energy use

Approximately

ONE TRILLION

DOLLARSin company value is

lost every year to

operating performance

suboptimal


Condition Monitoring

Technologies


Example – AC Induction Motors

Cooling

Fan

Enclosure

Stator

Terminal

Box

Mounting Feet

Bearing

& Seals

Rotor

Shaft


How does an Electric Motor Fail?

Bearings

40%

Stator Faults

38%

Rotor Faults

10%

Other

12%Mechanical Faults Electrical Faults


Electric Motor – Condition Indicators

TemperatureVibration Lubrication


Electric Motor – Vibration Considerations

Vibration

Why Vibration Monitoring?

Common Vibration Causes

How do I check?


What is Vibration

• Vibration is the motion of a body about a reference point caused by an

undesirable mechanical force.

• Causes include:

ImbalanceMisalignment

LoosenessBearing Wear


Vibration: Imbalance

0

+

-

AMPLITUDE

360degrees

Heavy Spot

1 revolution

Time

1750 rpm

FL

OW


Vibration: Misalignment

BEFORE

AFTER

GRRR!

hmmm!

SHAFT

BE

AR

ING

MOTOR

Offset

Angular

Both


Vibration: Soft Foot


Vibration Measurement: Accelerometers

Time (s)

Acc

el. (

G’s

)

ROTOR

Horizontal

Vertical


Time0

+

-

AMPLITUDE

Time0

+

-

AMPLITUDE

Vibration Basics from Mechanical Faults

Time0

+

-

AMPLITUDE

Balance

1 x RPM

Blade Pass

4 x RPM

Gear Mesh

12 x RPM

Component Configuration and Cycles per revolution


Time0

+

-

AMPLITUDE

Time0

+

-

AMPLITUDE

Vibration

Time0

+

-

AMPLITUDE

FL

OW

FLOW

FL

OW

Balance

1 x RPM

Misalignment

2 x RPM

Vane Pass

11 x RPM


The combined Time Waveformsdo not look like this


They look like this

In reality these waveforms are more complex

All the different frequencies are mixed

together in the waveform

Typical Waveform

Vibration: Time-Frequency Conversion

1X

900 rpm

15Hz

2X 9900 cpm

165Hz

COMPLEX FIELD TIME DATA

THE COMPLEX TIME DATA ISBROKEN INTO ITS COMPONENTS

IN A PROCESS CALLED

FFT

FAST FOURIER TRANSFORM

THE COMPONETS ARE GRAPHED AS

FREQUENCY VERSES AMPLITUDE

FREQUENCY DOMAIN

FREQUENCY SPECTRUM

SPECTRUM

FFT


Vibration: Frequency Alarming

Trend of

Balance

Trend of

Bearings

Alarm

Am

pli

tud

e

Balance AlignmentBearing Bearing Gears Bearing

1x 2x 50x

5mm/sec

1mm/secTime

(Days)

Time

(Days)


Electric Motor – Temperature Considerations

Temperature

Why Temperature Monitoring?

Common High Temperature Causes

How do I check?


Condition Monitoring Strategies


On Demand

Planned

Predictive

Prescriptive Analytics

Condition Monitoring Strategies


On Demand

• Periodic high resolution monitoring

• Monitor assets and faults on a monthly

• Manual Collection, Analysis and

Reporting

FL

OW

FL

OW

MOTOR

7X 8Y 5X 6Y

9

TACH

FIX

ED

BE

AR

ING

=

TH

RU

ST

BE

AR

ING

3X 4Y 1X 2Y

1011


On Demand




Reporting

FL

OW

FL

OW

MOTOR

MECHANICAL FAULTS

• ELECTRIC MOTOR

1. BALANCE

2. ALIGNMENT

3. LOOSENESS

4. BEARING FAULTS

5. MOTOR FAULTS

6. COUPLING FAULTS (PeakVue)

7X 8Y 5X 6Y

9

TACH

FIX

ED

BE

AR

ING

=

TH

RU

ST

BE

AR

ING

3X 4Y 1X 2Y

1011


On Demand




Reporting

FL

OW

FL

OW

MOTOR

MECHANICAL FAULTS

• ELECTRIC MOTOR

1. BALANCE

2. ALIGNMENT

3. LOOSENESS

4. BEARING FAULTS

5. MOTOR FAULTS

6. COUPLING FAULTS (PeakVue)

7X 8Y 5X 6Y

9

TACH

FIX

ED

BE

AR

ING

=

TH

RU

ST

BE

AR

ING

3X 4Y 1X 2Y

1011

MECHANICAL FAULTS

• ELECTRIC MOTOR

1.BALANCE

2.ALIGNMENT

3.LOOSENESS

4.BEARING FAULTS

5.MOTOR FAULTS

6.COUPLING FAULTS

• PUMP ROTOR

1.BALANCE

2.ALIGNMENT

3.LOOSENESS

4.BEARING FAULTS


• Frequent high-resolution Vibration and

Temperature Monitoring

• Permanently-installed monitoring equipment

• Monitor assets and faults on an hourly basis

• Wired or Wireless Collection

Planned


• Frequent high-resolution Vibration and


• Permanently-installed monitoring equipment

• Monitor assets and faults on an hourly basis

• Wired or Wireless Collection

PlannedMECHANICAL FAULTS

• ELECTRIC MOTOR

1.BALANCE

2.ALIGNMENT

3.LOOSENESS

4.BEARING FAULTS

5.MOTOR FAULTS

6.COUPLING FAULTS

• PUMP ROTOR

1.BALANCE

2.ALIGNMENT

3.LOOSENESS

4.BEARING FAULTS


• Continuous high-resolution Vibration and


• Monitor assets and faults on a second-by-second

basis while incorporating site process data

• Health Scores from the Edge Device provide easy

to understand fault analysis

2

1

3

46

5

TACH

Predictive


Edge Analytics Prediction Logic – Examples of Known Failure Modes

Balance

• Running speed amplitude

• 2 times running speed amplitude

• 3 times running speed amplitude

• Running speed amplitude > Balance Level

Other 1X phenomena

1.Resonance

2.Intermittent based on Defects

▪ Rub/Clearance/Runout issues

▪ Mechanical – missing gear tooth

▪ Shaft Crack

Alignment

• Running speed amplitude

• 2 times running speed amplitude relationship to running speed amplitude

Looseness

• Running speed harmonic amplitudes

• Synchronous and non-synchronous energy

VIBRATION SPECTRUM

1X

RUNNING

SPEED

2X 3

X


• Continuous high-resolution Vibration and


• Monitor assets and faults on a second-by-second

basis while incorporating site process data

• Health Scores from the Edge Device provide easy

to understand fault analysis

2

1

3

46

5

TACH

Predictive


2

1

3

46

5

TACH

Predictive

MECHANICAL FAULTS

• ELECTRIC MOTOR

1.BALANCE

2.ALIGNMENT

3.LOOSENESS

4.BEARING FAULTS

5.MOTOR FAULTS

6.COUPLING FAULTS

• PUMP ROTOR

1.BALANCE

2.ALIGNMENT

3.LOOSENESS

4.BEARING FAULTS


Instrument and Valve Health

Rotating Equipment Health

Machinery Protection and Prediction

DCS

Process Data

Persona based content delivery

Data Analytics Platform

Prescriptive Analytics


Analytical Techniques

DATA-DRIVEN

Statistical models

based on data using

regression techniques

(linear, logistic,

polynomial, etc.)

First principles

based on physical and

thermodynamic laws

governing how things work

PRINCIPLES-DRIVEN

EXAMPLE: ESTIMATING PUMP

REMAINING USEFUL LIFE (RUL)

EXAMPLE: PUMP FAILURE EXAMPLE: ASSET AVAILABILITY

PATTERN RECOGNITION

EXAMPLE: PUMP EFFICIENCY

Rules-based

on observation and

domain expertise,

including Failure Mode

Effect Analysis (FMEA)

Pump

Not Available

Pump

Available

40

Advanced analytics

leveraging …

Artificial Intelligence (AI)

and…

Machine Learning (ML)

for pattern recognition


Example – Root Cause Analysis

SymptomLow Compressor

Performance

Root CauseExpander Vibration


Collect condition

data

Centralized

software

application

Manual analysis /

Expert tools

Manual reporting /

integration to

other systems

C

U

R

R

E

N

T

Collect vibration

and process data,

Apply embedded

analytics

Reports / Alerts /

Messages

delivered to user

Advanced

analytical tools

N

E

W

Remote

Monitoring


Results and Benefits


Propane Export Facility

• In the first 8 months of operation, over 200 potential risks have been identified

and avoided via this program.

• Included within these risks have been potential failures of the most critical

control valves on site, as well as multiple compressor gearbox and LPG feed

pump faults. All detected with PeakVue, trended and highlighted with the onsite

continuous and periodic vibration monitoring program.

Predictive


Pulp & Paper Mill

Year Number of

Unpredicted Failures

2006 52

↓ ↓

2012 0

2013 3

2014 5

2015 4

2016 4

2017 3

2018 4

2019 1

Predictive


Summary:

• Condition monitoring enables predictive maintenance

• Many fault-prediction technologies available

• Technologies applied in different programs

• Quantifiable business results


Understanding and Applying Different Levels of

Machinery Condition Monitoring

Presenter: Carl Sheehan P. Eng.

September 15, 2020

Questions?

understanding and applying different levels of machinery ......automation | valves | measurement |...

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