state of the industry: predictive maintenance · how industry leaders in oil & gas, energy, and...

eHANDBOOK

State of the Industry: Predictive Maintenance

TABLE OF CONTENTSHolding Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Our 2018 predictive maintenance survey suggests that obstacles to PdM

success are being overcome by motivated plant teams and their partners

Smile and Say PdM! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

2018 snapshot shows an industry ready for next digital steps

The Road to RxM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

How industry leaders in oil & gas, energy, and more are mapping out

the prescriptive maintenance future.

www.plantservices.com

eHANDBOOK: 2018 PdM market research survey 2

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Want to see the full set of data from the Plant Services 2018 PdM survey?

Go to http://plnt .sv/1810-PDM and register to download and share your copy of the results .


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HOLDINGTOGETHER

Our 2018 predictive maintenance survey suggests that obstacles to PdM success are being overcome by motivated plant

teams and their partners

http://plnt.sv/1810-PDM

It has been more than 18 months since the last Plant Services survey on predictive mainte-

nance – a stretch of time during which manufacturing has seen a resurgence in the United

States, with 17 of the 18 major industries in growth mode, according to the Institute for Sup-

ply Management. Interest in the IoT and cloud-based technologies is sky-high and, at 3.7%,

unemployment is way low.

The results of this year’s PdM survey reflect the optimism in our industry. We’re able now

to look back across five years of data, thanks to your continued interest and input into this

research project. Although much of the data indicated only slight differences from 2017,

several new insights emerged that had not been observed across previous years – specifi-

cally, increased outsourcing and servitization of predictive maintenance activities, and fewer

perceived obstacles to PdM program success.

The full set of 2018 survey data are available at http://plnt.sv/1810-PDM.

Read on for survey highlights, and come back in November for additional

analysis from several leading industry experts.



Holding TogetherOur 2018 predictive maintenance survey suggests that obstacles to PdM success are being overcome by motivated plant teams and their partnersHOLDING

TOGETHEROur 2018 predictive maintenance survey suggests that obstacles to PdM success are being overcome by motivated plant


By Thomas Wilk, Editor-in-Chief



SERVITIZATION AND OUTSOURCINGThe first big surprise in the

survey data came within

responses to the first three

questions, which are de-

signed to establish a basic

demographic profile of

respondents. Figures 1, 2,

and 3 chart out the primary

job function of this year’s

respondents, the number

of plants their organization

manages, and the size of

the maintenance and reli-

ability staff.

When it comes to reported

job titles, we observed a

moderate rise in the num-

ber of reliability engineers,

maintenance technicians,

and controls engineers; in

fact, since 2014, the number

of people in these roles has

in each survey. Also of note,

the reduction in the number

of plant managers seems to

have halted in 2018, after a

significant drop from 2014-

2017. It’s more difficult to

identify patterns in the data

on number of plants man-

aged, aside from a trend in

the rise of companies that

manage 2-5 plants.

WHAT IS YOUR PRIMARY JOB FUNCTION? 2014 2017 2018

Plant manager

Plant engineer

Maintenance manager

Maintenance engineer

Maintenance technician

Reliability engineer

Reliability technician

Controls engineer

figure 1

12.8%6.7%6.9%

15.2%4.5%4.6%

24.2%22.5%19.5%

7.1%12.4%12.6%

7.1%7.9%11.5%

9.0%15.7%20.7%

7.1%12.4%9.2%

5.2%5.6%6.9%

HOW MANY TOTAL PLANTS DOES YOUR ORGANIZATION MANAGE? 2014 2017 2018

figure 2

1 plant

2-5 plants

6-15 plants

More than 15

35.9%

21.9%

20.7%

29.3%

31.5%

37.9%

13.7%

16.3%

20.7%

21.2%

30.3%

20.7%

HOW BIG IS THE MAINTENANCE AND RELIABILITY STAFF AT YOUR PLANT? 2017 2018

figure 3

Just me (the MRO is outsourced)

2-4 people

5-10

11-50

51-100

More than 100

2.8%6.9%

16.0%24.1%

27.1%10.3%

35.9%35.6%

9.9%13.8%

8.3%9.2%



However, the data in Figure

3 (the size of maintenance

and reliability teams) point

to a very interesting trend: a

significant rise in the level of

outsourced MRO. After stay-

ing relatively stable from

2016 to 2017, the share of

people reporting that they

fully outsource this function

jumped by nearly 150% from

2017 (2.8%) to 2018 (6.9%).

The rest of the data from

Figure 3 indicate that the

middle ground is shrinking

on whether to outsource

work in this area, as the

numbers of both smaller

(2-4 people) and larger

(51-100 people) teams

increased, and the number

of teams in the middle fell,

especially teams of 5-10

people. One possible expla-

nation for this trend is that

plants used the industry

momentum of the past year

to either add to their teams

or seek out maintenance/

reliability partners external

to the organization.

Data from Figures 4 and 5

help put these data in con-

text. When asked if they are

using OEM-enabled remote

monitoring technologies

or services (see Figure 4),

respondents indicated an

increase in their current

use of these services and

forecast an increase in use

within the next three years;

there also was a drop in

the share of those who

reported they have no plans

to engage with partners

outside the organization.

Figure 5 illustrates the fre-

quency with which survey

respondents share their PdM

data, and with which types

of teams they share. Plant

teams seem increasingly

figure 4

Using now

In this year’s budget

Within 3 years

No plans

ARE YOU USING OEM-ENABLED REMOTE MONITORING TECHNOLOGIES AND/OR SERVICES?

1.7%%

27.1%

45.8%25.4%

2017 2018

6.2%

15.0% 60.2%

18.6%

WHO USES THE INFORMATION PROVIDED BY YOUR PdM SYSTEMS AND WITH WHAT FREQUENCY?

Never Weekly Monthly Quarterly

2014 2017 2018 2014 2017 2018 2014 2017 2018 2014 2017 2018

In-house maintenance

8.0% 6.2% 11.9% 59.4% 48.7% 45.8% 20.3% 33.6% 23.7% 12.3 11.5% 18.6%

In-house operations

29.0% 32.7% 39.0% 39.1% 34.5% 37.3% 24.6% 16.8% 13.6% 7.2% 15.9% 10.2%

In-house reliability engineers

31.9% 21.2% 22.0% 40.6% 44.2% 45.8% 16.7% 27.4% 20.3% 10.9% 7.1% 11.9%

Totally outsourced 65.2% 76.1% 59.3% 8.0% 6.2% 10.2% 14.5% 10.6% 13.6% 12.3% 7.1% 16.9%

Third-party remote monitoring

73.9% 76.1% 81.4% 10.9% 11.5% 5.1% 4.3% 8.8% 3.4% 10.9% 3.5% 10.2%

OEM supplier 71.7% 69.0% 76.3% 8.0% 6.2% 5.1% 7.2% 12.4% 5.1% 13.0% 12.4% 13.6%

figure 5



willing to share their data on a quarterly ba-

sis with partners outside the plant, and that

the share of respondents who would “never”

totally outsource PdM work is dropping.

Taken together, these data reflect an in-

creased interest in the servitization of PdM

work and an increased opportunity for third

parties to partner with industrial organiza-

tions to deliver these services. This trend

also aligns with the general challenge in our

industry to bridge the skills gaps left by re-

tirements in a period of low unemployment:

If full-time talent is difficult to find, then it

makes sense to explore which partners might

be available to drive PdM program success.

HOLDING YOUR PROGRAMS TOGETHERAt the heart of the Plant Services PdM sur-

vey are the questions on which technologies

respondents are using and how satisfied

they are with their PdM programs. Figure

6 provides the data on the first question,

WHICH PdM TECHNOLOGIES HAVE YOU DEPLOYED?

Using now In this year’s budget Within 3 years No plans

2014 2017 2018 2014 2017 2018 2014 2017 2018 2014 2017 2018

Vibration 60.0% 70.5% 64.1% 5.8% 9.8% 7.8% 12.9% 7.6% 12.5% 21.3% 12.1% 15.6%

Ultrasound 45.5% 59.1% 60.9% 5.2% 12.1% 6.3% 16.9% 7.6% 7.8% 32.5% 21.2% 25.0%

Acoustic 24.7% 34.4% 21.9% 6.5% 5.3% 4.7% 14.3% 16.0% 15.6% 54.5% 44.3% 57.8%

Corrosion 33.8% 45.0% 28.6% 7.8% 3.1% 11.1% 14.9% 10.7% 14.3% 43.5% 41.2% 46.0%

Infrared 65.8% 74.8% 71.4% 3.9% 3.1% 3.2% 15.5% 7.6% 6.3% 14.8% 14.5% 19.0%

Oil analysis 62.3% 73.5% 74.6% 4.5% 5.3% 6.3% 15.6% 6.1% 4.8% 17.5% 15.2% 14.3%

Predictive modeling software

17.5% 20.6% 11.1% 6.5% 10.7% 6.3% 25.3% 19.8% 33.3% 50.6% 48.9% 49.2%

Electric motor testing

50.0% 47.0% 42.9% 5.8% 9.8% 9.5% 14.9% 21.2% 17.5% 29.2% 22.0% 30.2%

PLEASE RATE THE OBSTACLES LIMITING THE SUCCESS OF YOUR PdM INITIATIVES

Not a factor Low Medium High

2014 2017 2018 2014 2017 2018 2014 2017 2018 2014 2017 2018

Budget constraints 5.8% 6.8% 10.9% 14.3% 18.2% 20.3% 37.7% 40.9% 48.4% 42.2% 34.1% 20.3%

Undefined financial benefits

9.1% 10.6% 17.2% 18.8% 27.3% 29.7% 46.1% 39.4% 35.9% 26.0% 22.7% 17.2%

Undefined operational benefits

16.9% 9.1% 15.6% 26.6% 33.3% 29.7% 39.6% 45.5% 40.6% 16.9% 12.1% 14.1%

Limited engineering resources

16.2% 10.6% 12.5% 22.7% 22.0% 28.1% 42.9% 39.4% 32.8% 18.2% 28.0% 26.6%

Poor program execution

24.0% 16.7% 17.2% 32.5% 25.8% 37.5% 30.5% 40.9% 29.7% 13.0% 16.7% 15.6%

Lack of executive support

24.2% 16.7% 20.3% 23.5% 25.0% 29.7% 35.9% 35.6% 29.7% 16.3% 22.7% 20.3%

figure 6

figure 7



with many of the responses

holding steady from 2017

to 2018. After several years

of having three PdM tech-

nologies (infrared, vibration,

and oil analysis) achieve a

reported use rate of 70% or

higher, this year saw vibra-

tion analysis fall back to

64.1%. Also, ultrasound con-

tinued its gradual rise in use,

breaking through into the

60s this year with a reported

use rate of 60.9%.

Beyond the big four, no

other technology achieved a

use rate higher than electric

motor testing did at 42.9%.

Predictive modeling software

dropped to a meager 11.1%

use rate, with almost 50% of

respondents saying they had

no plans to consider using

predictive modeling.

Although 2018 survey data

on use of various technolo-

gies didn’t deliver major

surprises, it did confirm

that PdM technologies (and

by extension, proactive

maintenance approaches)

are stable and mature. The

benefits of predictive main-

tenance are familiar and

widely known, both financial

and operational, and PdM

programs enjoy more execu-

tive support than ever.

A quick glance at the data in

Figure 7 reinforces the sense

that confidence in PdM pro-

grams is high. The perception

among respondents that an

obstacle was either “high”

or “medium” dropped nearly

across the board, and “low”

or “not a factor” increased

in all but one area. This is a

startling result, striking in its

consistency, especially since

it was only two years ago

that plant teams reported

having significant difficulty

articulating the financial and

operational benefits of their

programs. Also, in the area of

program execution, the share

of respondents indicating this

as a high or medium chal-

lenge dropped from 57.6% in

2017 to 45.3% this year.

The data on overall PdM

program satisfaction, as

reported in Figure 8, sug-

gest slightly reduced levels

of satisfaction this year

as compared with 2017.

However, the share of

respondents who defined

their programs as “satis-

factory” remained virtu-

ally unchanged, indicating

confidence in PdM’s ability

to generate benefits.

REACTIVE OR PROACTIVE? AND, HOW PROACTIVE?The final data set we’ll con-

sider deals with maintenance

approaches that respondents

report using and the extent

to which respondents are

applying internet-enabled

and mobile technologies to

conduct predictive work.

Figures 9, 10, and 11 feature

respondents’ answers about

the approaches they take to

PdM PROGRAM: PERFORMANCE COMPARISON, 2014-2017

2014 2017 2018

Not effective 15.5% 8.4% 12.5%

Needs some improvement 40.3% 45.8% 45.3%

Satisfactory 24.8% 21.4% 21.9%

Effective 15.5% 17.6% 15.6%

Very effective 3.9% 6.9% 4.7%

figure 8



reactive and proactive main-

tenance. The data in Figure

9 are encouraging in that the

share of survey respondents

engaged with proactive

maintenance (69.8%) isn’t

drastically behind the share

engaged in planned and/or

reactive maintenance (84.1%

and 81.0%, respectively).

The survey also asked a few

questions on respondents’

experience with prescriptive

maintenance (often abbrevi-

ated “RxM”), in which data

is assimilated from diverse

process and performance

variables and then woven

into actionable recommen-

dations (or “prescriptions”)

on what to do, when to do it,

and how. Prescriptive main-

tenance is closely associated

with internet of things (IoT)

technology and is a useful in-

dex to assess whether orga-

nizations are moving down

a path that would involve

partial or full digital transfor-

mation of plant operations.

Figures 9 and 10 indicate

that from 11.1% to 17.5% of

respondents are currently

engaged in prescriptive

maintenance, whether at the

pilot or full project level, and

36.5% more are planning on

prescriptive maintenance

work within three years.

Figure 11 provides context to

these data by reporting on

the types of assets that re-

spondents say they are using

IoT technologies to manage.

It’s no surprise to see con-

trol system assets way out

in front of all other options

(47.2%), followed by electri-

cal systems (29.2%), pro-

duction assets (25.0%), and

automation assets (25%).

The surprise result in this

area is that respondents in-

dicated a slight reduction in

the use of IoT technologies

across most asset catego-

ries; there was no significant

drop in use reported, but

significant increases were

reported in only three asset

classes: control system as-

sets, manufacturing pro-

ductivity, and fleet. These

results, combined with the

general confidence that

plant teams are reporting

in their ability to overcome

obstacles, suggest that

organizations are locating

value in these three specific

asset classes for their digital

maintenance projects.

The final set of data included

in this article is from what

WHICH TYPES OF MAINTENANCE APPROACHES ARE CURRENTLY EMPLOYED BY YOUR PLANT?

Reactive (run-to-fail)

Preventive (periodic and/or route-based)

Predictive (diagnostics ahead of failure)

Prescriptive (diagnostics and guidance for repair)

figure 9

figure 10

Using now

In this year’s budget

Within 3 years

No plans

TO WHAT DEGREE DOES YOUR TEAM OR ORGANIZATION CURRENTLY ENGAGE IN PRESCRIPTIVE MAINTENANCE?

20 40 60 80 1000

10.7%23.7%

29.8% 37.4%7.9% 36.5%

38.1%17.5%

2017 2018



has become my favorite

question on the survey: What

data collection methods do

you currently use with your

PdM system? Figure 12 lists

results from all four surveys,

and once again, paper-based

systems (66.1%) are at the

top of the list, rebound-

ing upward this year after a

gradual three-year decline.

The data points that caught

my attention were signifi-

cant jumps from 2017 to this

year in the use of embedded

sensors, internet-enabled

sensors, and industrial-grade

tablets. Taken together, this

data cluster suggests that

condition-monitoring data

are increasingly being col-

lected and analyzed in real

time, and that organizations

are becoming much more

comfortable (even reliant) on

mobile devices and networks

to successfully execute their

maintenance strategy.

Several further articles are

in development based on

additional data from the

2018 PdM survey, and will

take deeper dives into the

areas of training and motors

analysis. In the meantime,

please consider download-

ing the full set of 2018 sur-

vey data and sharing your

thoughts with us on what

you see in the data.

WHICH TYPES OF ASSETS ARE YOU USING INTERNET-ENABLED / IIoT TECHNOLOGIES TO MANAGE? 2016 2017 2018

Automation assets (field devices, control valves)

Control system assets (DCS, I/O, controllers, networks)

Production assets (rotating equipment, mechanical pumps)

Distribution pipelines

Electrical systems

Fleet vehicles

HVAC/R system

Safety systems / devices

Manufacturing productivity

figure 11

6.5%7.9%6.9%

15.9%15.3%

19.5%27.2%25.0%

20.8%40.4%47.2%

27.3%25.2%25.0%

24.7%31.8%29.2%

9.1%6.0%6.9%

13.0%18.5%15.3%

15.2%23.6%23.6%

WHAT DATA COLLECTION METHODS DO YOU CURRENTLY USE WITH YOUR PdM SYSTEM?

2014 2017 2018

Paper-based system 71.7% 62.8% 66.1%

Handheld data collector 60.1% 59.3% 62.7%

Embedded sensors 48.6% 46.9% 57.6%

Wireless sensors – 29.2% 28.8%

Internet-enabled sensors – 12.4% 18.6%

Industrial-grade smartphone 7.2% 7.1% 10.2%

Consumer-grade smartphone 11.6% 18.6% 15.3%

Industrial-grade tablet 7.2% 15.0% 25.4%

Consumer-grade tablet 7.2% 13.3% 13.6%

Industrial-grade PC 41.3% 39.8% 33.9%

Consumer-grade PC 38.4% 35.4% 42.4%

figure 12

Have you ever gone on a trip, taken a picture of a key place or moment, and then returned

to that place years later to try to recreate that original shot?

I especially like taking this kind of picture, creating a secondary memory that gives new and

meaningful context to the first. Of course, much of the value is in observing the physical

changes in the people over time. People get older; families grow and change; friends occa-

sionally pass on. No one stays the same.

Also, it’s expected that the background will stay steady as a rock, the better to showcase

the changes in the subjects of the photo. If you took a picture at sunset by the south rim

of the Grand Canyon, chances are pretty good that both the south rim and the sun will be

there 10 years later in order for you to retake that picture.



Smile and Say PdM!2018 snapshot shows an industry ready for next digital steps

By Thomas Wilk, editor in chief



These are the kinds of things that run through

my mind when thinking through the results

of our PdM survey. We’ve been fortunate to

run this survey every 12–18 months since 2014,

and your donations of time and information

have generated a revealing series of snap-

shots over time. Each survey indicates a few

specific trends, with the collective set of data

adding up to a greater understanding of our

industry’s attitude toward predictive tech-

nologies and proactive maintenance.

This year, the two observations that stand

out the most to me are (1) the increased

movement by plant maintenance and reli-

ability teams toward servitization of PdM,

and (2) the general lack of perceived barriers

to PdM success. In essence, our respondents

seem to have grown over time into an ap-

preciation of the potential benefits of PdM

and feel confident that they can overcome

any real or perceived barriers to success.

However, that is not the full story. The

“background” has changed too, as seen

via changes in job function, the number of

plants managed per organization, and the

size of maintenance and reliability teams.

For example, a few years ago, reliability

professionals accounted for about 16% of

respondents; only four years later, that

share has doubled. There has also been

a 50% increase in the number of small

(1-4 persons) maintenance and reliability

teams, which may help explain the in-

creased interest in PdM services revealed

in this year’s survey.

Perhaps of most importance, the data indi-

cate that survey respondents are more than

just confident in PdM to make a difference;

more than 25% of respondents are either

currently engaged in prescriptive mainte-

nance or have it in their budget for next

year. This result represents more than just a

willingness of industry to engage in proac-

tive maintenance. It suggests that you’re

ready for the next step into a wider digital

transformation of operations, and that’s a

big change from four years ago.

People have grown into an appreciation

of thepotential benefits of PdM.

How industry leaders in oil & gas, energy, and more are mapping out the prescriptive maintenance future.

by Sheila Kennedy, CMRP, contributing editor



Who would have imagined how dramatically the industrial internet of things (IIoT) would

elevate reliability and maintenance practices? Today, we have sophisticated sensors moni-

toring multiple variables, closing information gaps, eliminating data silos, and populating

Big Data repositories in the cloud, where artificial intelligence (AI), advanced pattern recog-

nition (APR), machine learning (ML), and advanced analytics work their magic on common

industrial challenges.

Predictive maintenance (PdM) gave us our first taste of the power of monitoring individual

machine conditions. With prescriptive maintenance (RxM), data is assimilated from diverse

process and performance variables and woven into actionable recommendations (or “pre-

scriptions”) on what to do, when to do it, and how.

The benefits are readily evident – better-quality data, earlier problem detection, more timely

and accurate response, and perhaps of the most importance, less reliance on manual knowl-

edge capture. Following are some companies that are on the cusp of this new level of main-

tenance maturity called RxM.



NETWORK PREPARATION AT PENN STATE Maintenance strategies such as PdM and

RxM are possible only in connected environ-

ments. Tempered Networks recently helped

Penn State’s Office of Physical Plant (OPP)

instantaneously connect, segment, secure,

and manage all of its network devices cohe-

sively despite unique building and campus

challenges. As a result, OPP is now mak-

ing real-time control adjustments based on

conditions, entering the predictive stage of

maintenance and preparing for a future in

which recommendations will be prescribed

Previously, each building was a separate

entity. A lot of the systems in use were

standalone, and there was a server for

every application. “It causes headaches for

maintenance when buildings are disjointed

like that,” says Tom Walker, environmental

systems design specialist at Penn State

(www.psu.edu).

Now, about 300–350 buildings are connect-

ed at University Park, with all or most serv-

ers housed at the data center. Everything is

on a virtualized server; hardware is shared

among multiple systems; and authorized

personnel have instant access to the sys-

tems. “This increased our resiliency, reliabil-

ity, and overall uptime,” Walker says. “It also

gave us the path to start sharing data with

other systems and stakeholders.”

For instance, OPP is now working to en-

able fault detection and diagnostics within

the building automation systems, which is

expected to help reduce energy use and

maintain optimum facility operation. OPP’s

new energy dashboard visualizes when an

energy problem emerges in a building so

the issue can be addressed proactively. In

the future, OPP would like it to prescribe

what to do based on ML and data analytics

from the connected systems.

Efforts are also underway to automate

work orders in IBM’s Maximo based on

certain fault conditions and eventually

prescribe corrective actions. “Right now

the work orders are only telling that there’s

an issue that needs to be investigated,”

Walker explains. “We’re working with our

Maximo group on being able to feed more

data on the assets.”

Walker’s biggest lesson learned so far is

that the use of analytics packages that

read directly from the server is a better

option than pulling data directly from the

controllers, which does not scale. There are

also issues with legacy control systems.

“With Tempered Networks, we’re putting

a shell around all of our legacy systems by

locking them out and using microsegmen-

tation to say only this device can talk to

this server,” says Walker. “It’s really solved

a lot of problems.”



Segmentation and isolation has become a

best practice, but it is fragile using tradi-

tional technologies. “You can set it up once,

but as time goes on, it becomes impossible

to maintain, so it’s important to keep it

simple,” observes Erik Giesa, vice president

of products at Tempered Networks (www.

temperednetworks.com). Instead of using

a traditional enterprise IT solution to force-

fit connections, Tempered Networks tech-

nology was borne in an ICS and OT data

environment and bridges legacy systems in

a simplified manner, Giesa says.

PRESCRIPTIVE SERVICES FOR REFINING NZ Industry has come to expect maintenance

service providers to employ state-of-

the-art technologies and practices. The

outcome-based maintenance service for

industrial control systems from Honeywell

Process Solutions is relied upon by com-

panies such as Refining NZ, New Zealand’s

only oil refinery.

Peter Smit, head of process control at Re-

fining NZ (www.refiningnz.com), says: “The

Honeywell Assurance 360 program we have

in place provides us with the confidence

that we have our Honeywell distributed

control systems and Honeywell Advanced

Solution applications at an agreed level of

availability. We are very clear what out-

comes we expect, and this allows Honey-

well to leverage their knowledge and re-

sources to meet the agreed outcomes in a

structured and planned way.”

Steve Linton, director of programs and

contracts at Honeywell Process Solutions

(www.honeywellprocess.com), explains the

underlying goal. “We are trying to facilitate

achievement of our customers’ business

drivers and provide the outcomes they ex-

pect,” he says, “whether it’s control system

performance, control system availability, or

reduced incidences on the control system.”

Tools such as planned, preventive, predic-

tive, prognostic, and prescriptive analytics

and maintenance aid in driving toward those

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outcomes. Prescriptive approaches are being

beta-tested at some customer sites.

With RxM, Honeywell’s goal is to amalgam-

ate data across multiple control systems

to provide insights that say, “There is X

probability in X time frame that X is go-

ing to happen, so go look at these things

to prevent an undesirable outcome.” To

do this, information from multiple cus-

tomer systems is put into a data lake in the

Honeywell Sentience IoT platform, which is

appropriately controlled, cordoned off, and

anonymized. Self-learning algorithms use

and analyze the data and provide informa-

tion that the customer can use to better

maintain its control systems.

Prescriptive reliability analytics for MOL

Corrosion, fouling, opportunity crudes, and

resulting process fluctuations are the most

common operative challenges faced daily

at MOL, an integrated oil, gas, and petro-

chemicals company based in Hungary. It is

a member of MOL Group, one of the largest

companies in Central and Eastern Europe.

MOL Group’s 2030–Enter Tomorrow pro-

gram and recent strategic initiatives require

a dynamic enterprise-operations-focused

data and information infrastructure to

improve productivity and increase process

safety performance, says Gábor Bereznai,

maintenance engineering manager at MOL

(www.mol.hu/en). “Crude analysis, process

simulations, continuous data monitoring,

and early failure detection are the only pos-

sible answers to keeping our processes safe

and under control,” Bereznai says.

MOL began its journey to refinery mainte-

nance excellence with reliability-centered

maintenance (RCM) almost two decades

ago. At that time, a race to acquire software

led to implementation islands and a lack of

deliberate business process re-engineering.

In the next era, the focus was on software

integration and connecting the systems

with the corporate SAP ERP solution.

MOL’s daily operations have come to rely

on the company’s successful integration

of asset management software, including

Emerson AMS with SAP EAM and OSIsoft’s

PI System with SAP PM.

The PI System provides the real-time opera-

tional data infrastructure and configurable,

streaming analytical platform for MOL’s

refining division. Predictive and condition-

based maintenance, data aggregation,

and health scoring is done in the PI Asset

Framework (PI AF) and sent to SAP PM,

which generates the work orders.

MOL is using a “layers of analytics ap-

proach,” with human analytics and real-

time/streaming analytics providing a



foundation for higher-level, operationally

focused ML/AI, explains Craig Harclerode,

global industry principal for O&G/Petro-

chem at OSIsoft (www.osisoft.com). MOL

built momentum and awareness of the

power of analytics by asking the operations

managers what problems needed to be

solved and then quickly solving them.

“Once they had an analytical foundation,

they moved to identifying areas where

more-advanced prescriptive and predic-

tive analytics would have value and began

developing ML applications accordingly,”

Harclerode says, noting that MOL current-

ly has more than 25 ML-based applications

in production.

This approach works because, as Bereznai

explains, IT/OT transformation is a long jour-

ney that involves not only architectural and

analytical method changes but also multilev-

el synergies among people and processes.

“This is a really long journey, especially

in terms of mindset change and cultural

development,” Bereznai says. “The technol-

ogy and software side is much easier to

change than the mindset, and the impact

of this is underestimated.”

The efforts are paying off. MOL’s digital and

downstream business transformation has

delivered $1 billion in its first four years, and

the goal for the next two-year period (2017-

2018) is an additional $500M in EBITDA.

PRESCRIPTIVE PERFORMANCE ANALYTICS FOR TATA POWERSoftware companies such as AVEVA are

working quickly to answer the call for RxM.

“We are building prescriptive maintenance

and analytic capabilities into all of our as-

set performance management solutions

to help our customers optimize the entire

asset lifecycle and to ensure they have

access to the most advanced technology

available,” says Sean Gregerson, global

director of asset performance management

sales at AVEVA (www.aveva.com).

HOW DEVON ENERGY’S TANK LIQUID IS SCHEDULED FOR HAUL-OFF HAS

BECOME PRESCRIPTIVE.

Sou

rce:

Dev

on E

nerg

y



Tata Power (www.tatapower.com), one of

the largest integrated power companies

in India, has rolled out AVEVA’s Predictive

Asset Analytics software to 10 units at three

plants to enhance the reliability of its crit-

ical-power plant equipment. The rollout is

putting Tata Power in a position to quickly

incorporate RxM capabilities.

The utility set its sights on remote, fleetwide

continuous monitoring and diagnostics of

critical asset health and performance in

2014 with the goal of improving efficiency,

enabling proactive maintenance, and avoid-

ing unplanned downtime. It built a new

Advanced center for Diagnostics and Reli-

ability Enhancement (ADoRE) powered by

Predictive Asset Analytics.

The software learns an asset’s unique oper-

ating profile during all loading, ambient, and

operational process conditions. When exist-

ing machinery sensor data is compared with

real-time operating data, subtle deviations

are revealed. Alerts and fault diagnostics

are generated and plant personnel are dis-

patched quickly to take corrective action.

One recent catch yielded an estimated

$270,000 (U.S.) in cost savings. Analytics

revealed that the top thrust and guide bear-

ing temperatures of some circulation water

pumps were exceeding expected levels.

During a brief planned outage, clogging in

the bearing-cooling water line was identi-

fied and cleared, thus normalizing subse-

quent operation.

“Tata Power demonstrates the power of

using analytics to move away from a reac-

tive maintenance strategy,” AVEVA’s Gre-

gerson says. “By catching problems early

using APR and ML, the company was able

to reduce maintenance costs, minimize

unscheduled downtime, and prevent equip-

ment failures.”

PRESCRIPTIVE SCHEDULING FOR DEVON ENERGY Prescriptive approaches can be simple to

introduce incrementally. Devon Energy

(www.devonenergy.com) has thousands of

batteries of tanks that collect water and oil

during the course of operations, and how

that liquid is scheduled for haul-off has re-

cently become prescriptive. Real-time data

engineer Don Morrison described the transi-

tion in a presentation at the ARC Industry

Forum in Orlando in February.

Previously, scheduling liquid tank haul-offs

for the Oklahoma City-based independent

oil and gas company involved collect-

ing data from multiple parties in an Excel

spreadsheet and then using that file to

create schedules. A centralized, more-ac-

curate, on-demand process was needed to

prescribe when, where, and how haul-offs

would be needed.



Morrison explained: “We already had SCA-

DA systems monitoring oil and water tank

levels, so why not use them to detect when

haul-off trucks are on site and how many;

whether water or oil is removed from the

tanks and how much – we only want full

loads – and the fill rate?”

Two specific answers were sought: Could

the engineers predict when the next load

needed to occur so they could schedule the

right number of trucks 3–4 days out? Could

they gain enough data to “grade” their ser-

vice providers?

Devon Energy chose Seeq analytics soft-

ware to quickly detect haul-off events

based on real-time OSIsoft PI data. With the

push of a “get loads” button, all of the data

from PI are pulled; forecasts up to three

days out are generated; and the spread-

sheet gets filled automatically. The results

are reported in Microsoft Power BI, where

they can be sliced and diced as needed.

Excel was retained in the first stage be-

cause “we didn’t want to change every-

thing the users were doing and they were

comfortable using it,” Morrison explained.

Other future goals for Devon Energy in-

clude auditing and grading haul-off vendor

performance and potentially incorporating

opportunities such as RxM, smart contracts,

and blockchain.

As more companies like these advance to

prescriptive analytics and RxM, prescriptive

maintenance has the potential to further

heighten visibility and respect for the main-

tenance profession and its positive impact

on the bottom line.

Sheila Kennedy, CMRP, is managing director of Additive

Communications. Contact her at [email protected].

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