Master Data, From Inspection to Analytics to Business Decision

Welcome to our discussion of data, from sensor inspection to edge analytics, to
actionable information and business decisions.
Machinery inspection data comes in a variety of forms, from vibration to ultrasound
to infrared and oil analysis to motor current and human observations.
Each data set has its own characteristics, its own ability to detect defects in our
equipment, and its own data formats.
Unifying these data items into a collaborative system is a multi-step process, yielding
a transformative life of data and resulting information.
This presentation describes the data types, initial meta data, and equipment
conditioning indicating features we can extract from the data.
From this point, condition indicating features combine in new forms to
provide a holistic view of equipment health when combined with domain
knowledge.
The presentation describes the fusion of inspection data sources with encapsulated
domain knowledge that facilitates rapid assessments of machine health.
Case studies and a review of commercial systems supporting these concepts
are provided to illustrate data management concepts described.
1@ 2017 Allied Reliability Group

Why are we talking about the life of data? Data is the fuel we use to transform our
equipment maintenance strategy from reactive to condition based to predictive and
ultimately to prescriptive maintenance.

Ultimately, the owner operators of manufacturing and process equipment want to
know how much to budget for asset management. How do we get the most out of
our assets, how much should I budget for maintenance.
Reliability efforts provide the information the business needs. A good bit of this
information comes from analytics of inspection data, as well as adherence to our
maintenance plans.
Traditional rules of thumb play a significant role in guiding our analytics. Rules of
thumb include the interpretation of inspection data such as vibration, motor current,
ultrasonic/acoustic, thermography, oil analysis and more.
When information from our sensory data, as well as from our computerized
maintenance management systems is made available to all, our maintenance and
engineering teams are connected and collaboration to review sensor data is enabled.
As you expect, the process of predictive asset management starts with data from our
equipment; data that indicates the health of our equipment.

So how do we get there? Lets start from where we are, looking at standards and
similar embodied history. We also recall and leverage the wealth of knowledge in our
CBM workforce, our subject matter experts (SMEs).
One standard we have in our portfolio is ISO 13374 which has been with us for over a
decade. It provides one picture in the flow of data from sensor (at the top) to
advisory generation (what should we do?) at the bottom. It indicates on the right,
the need for collaborative accessible displays. It indicates on the left, the need to pull
data from static data systems such as machinery information.

In the industrial internet of things, we are building a roadmap. In my experience,
small pilots lead the way for the owner operator, the OEMs, and the CBM service
partners to adapt concepts of IIoT to our particular manufacturing environment.
Every implementation will be different. So we have to take small steps, to create an
digital smart factory for the owner operator that works for their specific
manufacturing or process environment.
Data is a key element in this journey, from sensor, to signal processing, to asset defect
identification (health), to remediation of any defect, to budgeting and business
impact.

Let’s take a quick look at and example that has gone “viral”. If you look on YouTube
for “Flowserve ThingWorx Pump” you will find several videos of a compelling
marketing demonstration of what is possible. In the video, we see the “entire stack”
at play.
Sensors measure physical parameters in the mechanical, process, and electrical
domains.
Signal processing transforms the big analog vibration data into indications of the
presence of defects in the equipment and process.
The accumulation of condition indicators yields (with subject matter expertise (SME))
a measure of asset health. Of course, we can use a rule based analysis to embody
our SME expertise into our evolving system.
Using SME knowledge, OEM engineering data and recommendations, and similar
remedial knowledge, our evolving system is able to recommend corrective
maintenance action.
Over time, as we digitize more and more history, and even collaborate across similar
applications in like industries, we begin to see patterns and can make predictions.

So if you agree, we have a vision: A Data driven, SME enabled, computationally
enabled predictive asset maintenance system yielding maximum production capacity
with lowest possible maintenance costs.

To get started in any condition based and predictive maintenance system, we first
have to understand what assets we are working with. This understanding includes
not only a listing of systems, subsystems, and equipment; yet it also includes the
components of equipment and replaceable or repairable parts.
We also need to know the failure modes that are likely and probable in the specific
environment and the consequences of failure. These two elements lend a criticality
ranking, so it is possible to rank equipment by importance to the maintenance team.
Failure codes allow us to track failures that do occur, such that we can improve the
equipment and maintenance plans to prevent future failures.
At Allied Reliability Group, we call these four elements “foundational elements”.

When we review the components and parts that comprise our assets, we build a bill
of materials that is used when repair efforts are needed. Our purchasing systems can
implement appropriate supplier contracts, understand lead times, and implement a
spare parts inventory for more critical components and parts.
When we understand how equipment might fail, we can apply condition monitoring
sensing technologies to detect defects that can lead to failure.

The real value of our asset catalog is the ability to develop an equipment
maintenance plan. There are really two types of maintenance plan activities.
The first is preventative maintenance (PM). These activities are calendar based or
usage based activities, that when completed on time and properly prevent defects
from entering our equipment, and thus prevent certain failure modes from occurring.
Preventative maintenance also includes human observations of the machinery, while
performing the preventative task.
The second is predictive maintenance (PdM). These activities are periodic
inspections of equipment with one or more sensing technologies such as vibration,
motor current, thermography, ultrasonic, oil analysis and so on.
Both of these maintenance plan activities may identify additional maintenance that
can be planned, which calls for a proactive (Proactive) maintenance activity that
modifies the machine to restore the equipment to as “like new” as possible.
Our goal is that 80% or even more of all maintenance work is planned work (PM,
PdM, identified Proactive), thus avoiding as many un-expected break-in work (“fire
drills”).

We often store all our Master Data in a Computerized Maintenance Management
System or CMMS.
An equipment hierarchy (reference ISO 14224) is created down to the components,
and even parts. Failure modes and reason codes are added to each component.
When proactive and break-in work is performed on the equipment, work order close
out codes are used to track the reason for part of component failure.
As proactive maintenance is performed, we then can track and accumulate failure
reasons and ultimately improve our equipment with further modifications or change
our operations or maintenance activities to prevent future failures.
Purpose
Identify defects in design, process, quality, or part application, which are the
underlying cause of a failure or which initiate a process which leads to failure
Understanding the failure modes present in each piece of equipment, and
how to identify those failure modes, should dictate your PM and CM activities

Given we have our Master Data in place, we know our equipment and how it fails, we
can progress to inspection technologies that work to detect equipment defects that
lead to failure.

There are a variety of inspections available for condition monitoring. Using our
domain knowledge, we map the expected failure modes of the equipment to
inspection technologies.
For example bearing fatigue can be detected by vibration sensors, accompanied by
the appropriate signal processing analysis.
Oil contamination can be detected by oil analysis, looking for contaminants in the
lubricants.
Motor faults can be detected by motor current signature analysis.
If we detect the defects that lead to failure early, we have time to prepare for a
maintenance activity that will prevent catastrophic failure of the equipment. The P
(detectable defect) to F (functional failure) curve helps us to determine the amount
of time we have to react.

It is possible to automate machine inspections with on-line data acquisition systems.
In the Industrial Internet of Things (IIoT) era, automated inspections are preferred.
Automated data collection systems have data management requirements of their
own. They must digitize the equipment physical phenomenon (vibration, current,
sound, chemical properties, temperature, etc.), store this digital data in memory, add
descriptive meta data, and calculate features from the data that serve as condition
indicators.

Ideally, the automated inspection data has onboard calculation abilities to transform
raw time waveform data to condition indicators. Indeed, there is a lot that goes into
configuring the calculations. These include asset information such as bearing type,
gear type and number of teeth, number of rotor bars, orientation of the equipment,
and so on. The asset information comes form our master data that serves as our
understanding of the equipment.

With our edge analytics calculations in place, we can then use our domain expertise
to set alert and alarm level limits. Each calculation and alarm level maps to a specific
failure mode of the equipment.
Now we know we have a bearing defect, or a gear defect, based on the inspection
data, the edge analytics, and the map of alarm values to failure modes.

If we know we have a specific defect, we can take action.

Depending on the type of defect we have detected, we understand the failure mode
that is developing. We can map work activities that can remove the defect, or reduce
its impact (even temporarily). For example, if we receive an alarm indicating a
bearing defect, we might re-lubricate the bearing to extend our time to failure.
We can use a rate of change of our feature / condition indicator which created the
alarm, to predict how long we have until failure.
We then have an activity (lubricate the bearing) and a time within the task needs to
be performed. We use this information to enter a work request into our
Computerized Maintenance Management System to plan and schedule the specific
pro-active maintenance activity.
We have prevented the failure, and added to our data set for the specific defect and
failure mode. Our IIoT system can use this “pattern” in the future to improve
detections and preventions.

Here is an example work request. It is labeled here as a Fault Entry Report. This
report details the defect, the failure mode, and the failure code if known. It provides
text describing the human’s interpretation of the data, along with supporting data.

As we accumulate fault reports, we accumulate potential failure consequences, and
statistics regarding failures and defects experienced within our facility. We can
perform Root Cause Analysis (RCA) to determine what can be done to prevent future
failures and defects.

The last major step in the process is executing the work order to remove any defects
(repair the equipment) and to establish any future preventative measure.
We may add new sensing to the equipment, for improved defect detection.
Of course, after any repair of equipment modification we need to conduct a
commissioning test, and set new baselines and update our alarm settings.

Let’s look at an example.

The systems continually monitor their inputs looking for those trigger conditions (or
combinations there-of). The embedded systems use a dual-core processor to analyze
sensory data in both the time domain and the frequency domain. Once they are
triggered, they send their data files to the server for storage and access by an analyst.

The graphics here help us summarize a fleet-wide monitoring system implemented at
Duke Energy.
Here, over 10,000 assets are monitored, using over 30,000 sensors.
Over 2000 NI CompactRIO systems are deployed to capture sensory data under
specific operating conditions across 60 plants.
The calculated condition indicators are transferred to a single monitoring and
diagnostic center, built around the OSIsoft PI™ platform.
Since 2014, NI has been supplying one of the more cost effective, open architecture,
and integrated inline monitoring systems available on the market.
Built on its widely accepted CompactRIO platform for data acquisition and analysis,
condition indicators are calculated for trending and alarming and for recording in
their OSIsoft PI™ platform.
Allied builds on this infrastructure to add the Reliability Management System,
providing the high level dashboards and reliability activities that drive the two core
benefits we all seek. These are increased revenue resulting from increased capacity
and lower maintenance costs resulting from condition monitoring and predictive
maintenance activities.

This graphic points out that most manufacturers and plant operators are still in the
manual data collection phase of their IIoT journey.
However, as companies adopt a more continuous monitoring and IIoT approach,
benefits reveal themselves.
The following are some sample dashboards that prove helpful in machine failure
prevention technologies.

Data can be exported across equipment, systems, areas, sites, and even the
entire enterprise. This report is a built-in Key Performance Indicator provided
by Allied’s RMS.
An introduction to iReliability™ can be seen on the Allied’s YouTube site:
https://www.youtube.com/watch?v=3lShLag9Efc.

Asset health dashboards illustrate the existence of defects within equipment across
the plant. When sorted by equipment criticality (a master data element), it helps
prioritize maintenance activities to the most important equipment.

With the Industrial Internet of Things, customized dashboards are possible, building
on both condition monitoring data, and on process and production data.

At Allied Reliability Group, we work to balance technology with people and process.
People and Business need to be aligned and supportive of any new technology that is
incorporated in a pilot or larger scale. With People, Business Processes, and
Technology consider as a whole, success comes easier and sooner.

Master Data, From Inspection to Analytics to Business Decision

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