Using PI Server Analytics to

See Rotating Equipment

Life Expectancy:

Best Practices for Consuming Wireless IIoT

Smart Vibration Data to Reduce

Downtime

Dated: Jan 2018

Submitted by: Steve Edwards, OSIsoft Field Service Engineer sedwards@osisoft.com

Contributors: Ted Birky, David Gray, Joe Van Roosendaal, Carroll Sparks, Chris Felts, Heath

Howland, Alexander Fiset

OSIsoft, LLC

OSIsoft, LLC Jan 2018

Abstract

Using PI servers to analyze overall vibration readings is a breakthrough. Smart EDGE/IoT wireless

vibration sensors are opening new doors. The low costs of connecting these data sources to PI servers

allow you to stream and store life expectancy on machines that have never been monitored. This will

create a tremendous boost in reliability and safety performance for many companies.

In this post we present best practices for consuming overall vibration data with PI servers. We

demonstrate how to forecast machine health, alert users and capture feedback with PI Analysis Service.

We provide sample templates for PI Asset Framework. We demonstrate effective dashboards and event

visualization with web displays in PI Vision™ . We present several best practices that leverage local

operations staff as key data consumers. We show you how to configure a standard PI system to stream

forecasted machine failure into future data. We demonstrate “plug-n-play” flexibility techniques needed

when sensors are often moved between machines in the field. We give you guidelines for selecting smart

vibration sensors. We present how to build vertical asset health rollups. We present best practices for

notification of machine health issues using eMail and SMS messages.

PI servers provide visibility into rotating equipment operation. Boosted tag counts streaming from

production operations transform real-time operational data into fast, actionable insights. Barriers

between Operational Technologies (OT) and IT are removed with PI Asset Framework. PI web-based

visualization tools accelerate discovery through mobility.

Target audience Plant Engineering designers and managers Plant Operations managers Plant Maintenance managers, Engineers, Mechanics Plant Process engineers and Process technologists Plant Reliability Engineers

OSIsoft, LLC Jan 2018

Executive Summary

Unplanned rotating machine failure drives up costs and risks injury in nearly all plants. If you could only

see the failures coming for more rotating machines in your plant…

More online sensors. Fewer inspections.

Fewer spare parts. Improved safety. These are

powerful moves toward world class. The only

questions are… how to do it well and avoid

overspending.

A PI System and EDGE/IIoT smart wireless

vibration sensors can make it happen.

Use PI servers to log overall vibration data,

analyze, forecast and inform your decision-

makers. PI does the work behind the scenes.

What you experience is simple beyond

complex.

Witnessing the early stages of rotating equipment failure is essential to finding latent root causes.

Response times are measured in minutes. Online PI analytics near the asset give local operations team’s

immediate focus.

Traditional vibration monitoring uses contracted experts,

hard-wired sensors, dedicated processors, and dense data

storage. This “deep dive” approach is well suited for ultra-

critical assets with high replacement cost. But what about

the rest of the plant?

In power generation there is a class of assets called Balance

of plant (BoP). These are the large quantities of pumps and

fans that support the site. Often these are unmonitored for

health due to cost of traditional hard-wired sensors. These

now are prime targets for improvement.

Don’t overspend on complex layers of contracted

service-provider relationships that ultimately are

too distant to sustain. With the PI system, keep a

larger portion of machine health decisions within

the reach of your operations staff.

We will show you how in this post.

3 Rooftop Air Handler Units

2 Industrial process air blower

1 Typical bank of centrifugal pumps

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Goals

For rotating equipment, vibration data from accelerometers

is the fundamental visibility into machine health. For BoP

assets you often don’t need very much detail to be

effective. Replacement costs are low making the decision-

making thresholds low. Early detection of machine failure

can boost repairs and reduce replacements.

The goals of this post are to help you answer questions like

these for all of your machines…

1) I know the machine is sick but is it getting worse?

2) How much longer can this machine run and how

confident are we?

3) If we know life expectancy do we need a spare?

4) Did we get a good repair?

5) Has this machine or any others like it ever had these

symptoms before?

6) How can we teach a new employee the history of each

machine?

7) Who saw the machine the last time it failed and what

did they say about it?

8) Did we capture the root cause from the last time it

failed?

Method

Using PI systems to store and analyze overall

vibration readings is the breakthrough method

presented here.

For BoP assets, the operations teams simply

seek “clean” machines. You need less about

why the machine is not healthy because the

corrective action is often to swap for a

refurbished unit. You push the heavier

diagnostics work to the offline repair teams.

Back in the plant the mission is simpler. You

confirm a machine is “clean” on entry, monitor

online, detect “unclean”, track progression and

plan for maintenance accordingly.

There are three pieces that must be done well to make this method work.

5 Typical chemical pump pair (A/B) being inspected.

4 Wireless Smart vibration sensor transmitting vibration readings and surface temperature of a motor.

OSIsoft, LLC Jan 2018

The human element is still today the most important consideration.

In plants it is the people that make the operating decisions. I read

recently an article in Scientific American by Mark Fischette where

IBM concluded our computing capability equates to the brain power

of a house cat. I always knew cats were smart. IBM estimates that it

will take 880,000 processors to demonstrate the capability of a

human brain. We are best at creative thinking and our computers

are best at repetitive operations. It is important to understand this

and design for it. We will do that here.

The second piece is IIoT hardware. It’s what we techies all want to

talk about. The Industrial Internet of Things (IIoT) is truly moving the

needle. Wireless sensors with on-board processors are scrubbing and

delivering data at ultra-low cost. It is important to choose the right

sensors.

Finally, a PI system must be in place to store data, shape it, slice it

and notify the right people. It must be intelligent so to not additional

labor. Streaming analytics are the repetitive tasks done for us by PI.

The PI system can store, aggregate, scrub, forecast and alert you. PI

is ready to go to work. Web-based visualization, SMS text messages

and email messages pull users in the loop on preventive

maintenance.

Selecting Sensors for Rotating Equipment

Let’s step back for a moment and cover some vibration monitoring basics. The key measurement

technology is an accelerometer. It can capture the smallest of forces before any of our five human

senses. What you can look for today are sensors and EDGE/gateway routing devices that have

processing capability to pre-process the raw data. With BoP assets we need only consume overall

results (typically one data point per hour) for storage and analysis.

Below are some suggestions for selecting overall vibration sensors:

1. First, use wireless sensors. They avoid costs of running conduit/wire while allowing for

flexibility to move sensors readily. Surprisingly, wiring costs in plant settings are very high,

particularly in hazardous facilities.

2. Look for sensors that use mesh-networking to navigate obstructions in your plant.

3. Use hardened industrial wireless protocols and equipment standards such as WirelessHART for

increased cyber security and reliability in a plant setting.

4. Use sensor suppliers with trusted brand names for longevity in reliability and support. Don’t cut

corners on sensor prices without first testing reliability and ruggedness.

5. Don’t forget to buy sensors rated for the hazardous area classifications and ambient conditions

they will see in your plant.

OSIsoft, LLC Jan 2018

6. ** Most folks in these industries (Chemical, Oil&Gas, Power Gen, Facilities, Food, Pharma, and

others) have numerous pumps, fans, blowers, compressors, and gearboxes. That means lots of

shaft bearings. Bearing failure is far and away the most common failure mode and should get

the majority of your focus. Make sure your sensor can provide a bearing fault overall signal.

What I mean is a signal designed specifically to detect early stages of bearing defects. This is a

streaming analytic derived from acceleration data. The key here is that the sensor does the high

frequency computations and leaves only a simple overall stream being sent to PI. Sample rates

of one data point per hour going to PI is often sufficient.

7. Be sure to have an overall sinusoidal velocity signal stream as well. This is another derived

overall signal from the accelerometer. Velocity is nice counterpart to bearing fault overall to

form a signature for the machine’s health. Each can be leading or lagging. Again, hourly sample

rate to PI is common.

8. Surface temperature is a helpful complement to accelerometer data. Temperature can be a

lagging indicator of poor health. Look for this on board your accelerometer sensor all in one

convenient unit.

One sensor that meets the requirements above is the CMWA-8800 from SKF. SKF a well-known bearing

manufacturer and trusted global brand. This is the only sensor I have direct experience with in this

context. There are many other sensor suppliers in the market that may provide an equivalent device.

Units of Measure

You’ll need some new units of measure (UOM’s) added to your PI system for vibration monitoring.

For the bearing fault indication you will use the units given by your chosen sensor supplier. Each

supplier defines their metric differently. For the CMWA8800 sensor from SKF it is defined as enveloped

acceleration (gE). This can be added to PI in a new UOM class of called Acceleration Enveloping. You

will also need a rate of change for this unit to support forecasting analytics. This will be a new UOM

class called Acceleration Enveloping Rate.

For overall Acceleration data you will need a new UOM class that includes several UOMs to express the

overall result of a sinusoidal wave. We called this class Sinusoidal WaveForm Acceleration. In this class

we add the following UOMs: Acceleration RMS, Acceleration Peak, Acceleration Peak to Peak,

Acceleration Average.

For overall Velocity data you will need a new UOM class that includes several UOMs to express the

result of a sinusoidal wave. We called this class Sinusoidal WaveForm Velocity. In this class we add the

following UOMs: Velocity RMS, Velocity Peak, Velocity Peak to Peak, Velocity Average. Conversion

factors can be found here.

OSIsoft, LLC Jan 2018

Run Counters

Counters keep track of how often a machine has been started and how long it has been running. This

information is necessary for making decisions about when to repair a machine. Run counters should be

a basic piece of your equipment templates such that they appear consistently in the same place across

all of your machine visuals.

Below find some useful run counters and the expression code for them.

Forecasting

It is important to express your results in the simplest form. Successful teams forecast well in any

organization. For a machine we use LifeExpectancy (Months or Days) and the ForecastedFailureDate

(MM-DD-YYYY). To supplement these we store a 30day forecast, not unlike a weather forecast. Simple

to understand for occasional users.

We said earlier that bearing failure is our focus. Let’s expand on that by looking at one typical failure

sequence. Small defects in the balls or rollers of a bearing assembly can occur, like a scratch or a chip.

This can be caused by lack of lubricant, poor quality metals, poor assembly or numerous other

possibilities. This is the beginning of poor health. Initially, there is nothing to hear, smell, touch or see.

Not sure about taste, no data for that one. However, there is data to see in the bearing fault overall

(gE) sensor stream from an accelerometer. Over time the defect damages the other surfaces it touches.

The bearing fault sensor increases as more run hours allow for more physical damage. You may begin to

see increases in overall velocity (in/sec peak). Later, this damage may begin to make a sound and

create heat. The surface temperature may increase. Finally, the bearing gets super-hot and it locks up.

To forecast a time series sensor stream is leverage its history. In doing this you are going beyond the

capability of a traditional alarm system. To forecast utilize the linear regression tool in PI AF to fit lines

to bearing fault signals over time that are trending up. We frame these regressions in time starting from

the last maintenance date to the present. These fits are all done in continuous streaming fashion by the

PI system.

For this post, we focused on this particular failure mode/sequence merely to demonstrate the

technique. You will find many more scenarios to design for and I hope you can use the same techniques.

We demonstrate forecasts in three formats in this post.

1. Bearing Life Expectancy – Time remaining until bearing fault overall reaches its HiHi threshold.

2. Bearing Forecasted Failure Date – Timestamp when bearing fault will reach its HiHi threshold.

OSIsoft, LLC Jan 2018

3. Bearing Fault Overall in 30days – Projected bearing fault overall reading in 30days from now.

Techniques in PI Analytics Service

PV While Running – For analyzing the vibration history we must only fit to data taken while running.

The easy way (albeit a tad expensive) is to create a second PV tag in the data archive that only contains

values taken while machine was running. Use a natually scheduled AF analytic to make this happen.

Use a trait to house this second PI tag.

Emergency Inhibit – Ideally we feed machine health diagnoses from PI back to your SCADA/Control

system as an output point from PI to inhibit operation of the machine. This is especially useful to

guarantee that machine health policies are enforced to prevent damage. This inhibit signal can be an

alarm point in the SCADA/Control system or a interlock logic input. To demonstrate we apply PI analysis

logic that simply looks for PV greater than HIHI traits from bearing fault, velocity or temperature.

Certainly, in your application you might expand this logic.

Linear Regression – Shown below is the stack of commands for executing a look back at vibration history

since last maintenance. We are focused on bearing fault overall. We want to characterize from the

data whether it is trending up and how fast. We require a minimum amount of good data points and

require that the machine be running before enabling the linear regression fit. In the analysis we only

look back at events while the machine is running. The linear fit yields a slope (m), intercept (b) and

goodness of fit ratio (R-squared). As you can see the code is quite short and simple. Once we see a

positive slope (m) and an goodness of fit ratio > 90% we release the forecast to update archived tags for

the user to see. You’ll notice we are archiving just about every item since this is a demonstration. In a

real build you could archive fewer items

Tracking Unhealthy Runtimes with PI Event Frames

Event frames (EF’s) in the PI system are great tool for machine health work. PI captures the start and

end times for periods of unhealthy machine operation. You might say PI is like an assistant that

organizes information you may need later while you are playing golf.

PI Vision can overlay multiple time series events on top of each other regardless of when they occurred.

For example, you can quickly compare a failure that happened last year against a current failure in

OSIsoft, LLC Jan 2018

progress. Another useful example is to overlay failures across sister machines. Time-series overlay tools

are an absolute must for finding answers quickly in large data sets. They are a huge part of allowing

your PI system to work for you. Visit our PI System YouTube channel for Event Frames for more details.

In the example display below we compare nine previous failures of the same type machine. Very quickly

we can conclude that all nine failures were similar in shape and likely share the same root cause.

EF: Symptoms

We present in this post a very simple EF implementation you can start with and expand. The PI analysis

service triggers the event frames automatically from real-time sensor values. When Bearing Fault

overall exceeds its high limit for a proving time period the trigger fires. PI classifies the symptoms (See

the Table below) which is used in visual displays and emails. This list of symptoms can grow as your

teams learn more about their machines. The list is centralized in your PI library and easily propagated to

all deployments. This list can become key intellectual property for your company.

OSIsoft, LLC Jan 2018

EF: Reason Codes

Reason codes allow users to capture what they found to be the root cause for each failure. Drop down

lists are needed for selecting the reason code. These lists serve to teach users what to look for. Like

symptoms, reason lists are always expanding as you learn. This list can grow and become more

intellectual property. The list is centralized in your PI library and easily propagated to all deployments of

health sensors.

EF: Acknowledgement and Comments

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Manual data entry in rotating equipment work is essential. We all contribute our best when we know

that our efforts are seen and used. Diverse teams have good tools for traction to communicate well.

They organize manual data alongside sensor data and engineering data. Comments about what people

see, hear, feel, and smell at the machine are very important. Photos and videos are extremely powerful.

Other files such as shop teardown reports and independent performance reports can all be valuable

evidence when solving difficult problems. Save all manual data in your failure records. Accurate

recollection of the details leads to discovery.

As a manager, engage your users by reading their comments and responding when possible. Operations

managers, staff engineers, operators, vendors and mechanics are all vital in finding root causes of

machine failures. Manual data entry bridges gaps across work crews and across plant sites. The PI

system stores comments along

with timestamp and username to

help you make this happen.

A web-based platform allows users

to contribute wherever they may

be. (Mobile or desktop). At right,

see the standard event frame

panels built into PI Vision. These

require no programming or setup.

Features include time series

overlays in the middle, with

commenting and

acknowledgement controls to the

right.

As you can see in the image,

the comments pane includes

information about emails

sent, who acknowldeged the

event and when. Comments

are saved with timestamp

and user login too. There are

simple controls for attaching

files and viewing them.

These are essential tools for

collaborating effectively.

OSIsoft, LLC Jan 2018

Template Design

Here are some ideas around PI analytics and template design we found helpful when building out this

post.

Number one, I strongly recommend that IIoT health sensors like the ones referred to in this work be

given their own element template. Position health sensors as optional “care providers” that can be

attached to “patients”. This gives you tremendous freedom to evolve the diagnostic algorithms and

then push those improvements with one click to all deployments of that sensor. You should expect to

make many advancements to your machine health analytics as you see more and more results. The

machine health analytics captured in these templates become important intellectual property honed

to your business. This template design will also provide flexibility to move sensors readily to and from

machines with minimal work to do in the PI system.

Let’s look at the actual buildout in more detail. Note the images below illustrate a typical BoP

centrifugal pump. The only existing piece of sensor data we have is from the motor starting equipment

telling us a discrete Running/Stopped status.

We give the motor a Drive End (DE) health

sensor. Large motors will require additional

sensors. We must also give the pump a drive

end health sensor to detect its local bearing

faults. This layout is what we will use in this

post and shown at right.

Now, what does this look like in the PI System.

As you see illustrated, the health sensors are

child elements of the pump and motor. The

pump and motor are the “patients”. Data is

exchanged between patient and care provider.

The list below contains the information provided

by a typical BoP asset to its health sensor care

provider element. Status and LastMaintenance

date are key items involved in the analytics of

the health sensor.

Each health sensor is responsible in turn for providing results back to its patient. These results come

from the forecast calculations and event frame health triggers. The list below contains the health

results that are communicated back up to the patient. To facilitate this exchange of health info back to

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the patient we use a PI Analysis Rollup at the patient element because in we have multiple care

providers on one patient, they will be combined.

For example, let’s say we add three more health sensors to our pump. The rollup for pump

LifeExpectancy would simply be the minimum of the LifeExpectancy reported by all care provider

children. This means as you move sensors around in the field, you need only move the sensor in PI. The

templates know how to attach themselves for data flow to/from the patient. This is super easy to

configure in the PI server. See below the configuration.

The wireless vibration health sensor template attributes are shown below. Categories help organize into

groups and traits are used on several attributes to provide depth and organization. The best way to

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review this template is to download it into your PI system and explore it.

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Enumeration Sets

Enumeration sets are code lists you can build in PI system to describe behavior or status in the form of a

discrete list. Two enumeration sets were built for this demonstration. These sets were mentioned

earlier in the event frame section. These sets are included in the PI AF kit that comes with this post.

Necessary Visualization

Good visualization design is critical so that users can read these new data streams without effort. You

want your staff on their feet talking to people and spending time with the machines in your process.

You don’t want them at their desk building queries. Web-based based user interfaces are important to

allow folks to use smart phones and tablets in addition to desktops.

Dashboards - It is important to shape data and make it fall in front of the user at the right time. A

“collection” in PI Vision is effective at making smart dashboards. We can draw a dashboard panel for

one asset, specify an asset query criteria and then PI Vision will continually populate the screen with

duplicate panels for all assets that meet our criteria.

One critical dashboard we will need here is a collection of all unhealthy machines. See the image below.

In this dashboard we show the name, life expectancy [in days], run status, and health status of the

machine. We highlight sensor data using yellow and red colors if hi limits are reached. The trend

provides a quick glance but can be clicked to expand. A detailed table is provided with the remaining

useful metadata.

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You might find it useful to have high level dashboards with very brief summaries for all machines with

health sensors. We have only four machines in our build here, but imagine this display dynamically

finding and displaying hundreds of machines.

Another technique to query and report by exception in PI Vision is the asset comparison table. Here we

see a more dense view of results across several machines. An example is shown below. Again the use of

yellow and red coloring is used to highlight unhealthy machines throughout the grid. In this example

three machines are running, two are unhealthy and both of those machines are showing early stage

bearing failure. Imagine this table looking at hundreds of motors in a plant sorted by health status.

Machines that need attention rise to the top. That tremendous boost in visibility we talked about

getting from wireless smart devices comes out here.

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Detailed Display - A detailed display is always useful to give the data full context. Here we try to put

everything we know. This screen is useful for training purposes. The content we show here is typical for

a BoP pump asset in a chemical plant. Typically the only process signal for this pump is Status [Running

or Stopped]. So, we add health sensors and metadata. We use the banner for overall summary and we

use several panels below to show historical and realtime data. These displays promote “emotional

discovery” of trends and relationships that will lead to finding root causes of failure.

Visit our YouTube online course content for PI Vision for more details about visualization.

XvY Plotting - Provide an XY plot of bearing fault vs overall velocity as part of your standard visual

template for each sensor. Plotting XY is a simple way to help users recognize relationships. In the

example prodvided, you see gE plotted against Velocity (In/Sec Pk) over the time period of a poor health

event. In this case a cluster of gE readings at low velocity are meaningful. Later, increasing velocity

becomes well corellated with gE. This provides a signature that is unique and useful in understanding

the physical root cause inside the machine.

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Recommended

Metadata

Much like run counters, there are

more key pieces of metadata that

should be kept near. The mfr and

model of the asset along with rated

size and performance from the

Engineering design. This includes

direct links to engineering drawings.

The same is true for maintenance

and reliability data in the Centralized

Maint Info Sys (CMMS). Provide links

so that information is easily

discovered. Another idea is to keep

the replacement cost for each asset

in front of the user to help in

decision making. The physical

location of the asset is useful to

avoid mistakes. The criticality is

helpful to harmonize everybody on

what is important and help make

decisions faster and cleaner.

These are only a start for one basic

class of pump. You will have

templates for many types of rotating

equipment, each with unique

metadata. Metadata fields are easily

expanded in the PI system. PI AF and

PI Vision are excellent aggregators of

metadata as shown here. You can

expect to always be improving the

metadata for rotating equipment

assets.

Be sure to learn and utilize layered

templates (called derived templates

in PI) in your buildout of AF. In the templates that come with this post you will see we use an asset top-

level parent template. This template contains attributes that apply all assets regardless of class. The

Manufacturer attribute for example appears in the asset top-level. Rated Head on the other hand is

specific to the centrifugal pump class.

For more detail visit our YouTube Learning channel here…

https://www.youtube.com/watch?v=dCx5_Aw5x24

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Notifications

Well-designed emails and text messages are super important for rotating equipment work. Good

messaging reduces labor and empowers people . Bad messaging can sour the whole experience .

1. Messages should be triggered automatically based on analytics.

2. Messages should be sent quickly to help first responders capture key evidence. It is important

that everybody know about the issue ASAP.

3. Allowing many people to be near the details via messages helps solve hard problems.

4. Both real-time and historical data surrounding the issue should be provided in messages.

5. Content should be accurate and targeted for what a person on their feet will need. Don’t

expect users to do any searching.

6. If it’s not in PI now, it probably can be.

7. Several users must have authority to configure messages so that edits are done swiftly.

8. Messages should include static text such as work instructions, troubleshooting tips.

9. Messages should include a path for the user to easily respond to the event with feedback

(comments) and see the latest real time status on smartphone, tablet, or desktop.

10. Centralize message formats as part of the health sensor template.

11. Don’t be shy about including information in the message.

12. Thoroughly test your messages before scaling up to more machines. Start with one machine

and a small group of users. Trigger and test as many scenarios as possible.

13. Work to get candid feedback on what is not missing or unnecessary.

14. When you see that the design is well accepted then scale up to remaining machines.

In the end, message content represents key intellectual property that you can hone to your business and

share across a large enterprise. Expect message content to continually evolve as you learn more.

Visit our YouTube channel on Notifications for more details on how to extend event frames into email

messages and web services.

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Sample Verbose Email - Shown below is a sample of a verbose email message that can be generated

using PI notifications for machine health. The idea here is to include all data surrounding the machine

that may be useful. This can be instructions, realtime data, historical data, metadata, or just about

anything. With this mail it allows the user to stay on their feet and be fully informed. The subject line

includes the name of the asset and the key triggering data value. The body of the email contains sensor

values, forecast, maintenance data, engr links and a description of the triggering mechanism behind the

email.

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Sample SMS Text Message – At right find a

sample SMS text message generated by PI

Notifications. The wording is critical since

we have a limited number of characters.

We need to be sure it includes the asset

name, the symptom, and the data value(s)

triggering the event.

Final Thoughts

I hope this post is helpful in your work. The

depth we cover in this post is only

scratching the surface of what is possible for

machine learning in this setting. What I

found in doing this post is that machine

learning algorithms can be broken down

into small, very manageable equations.

Then when these analyses are gathered up

and presented to a user they create insight.

You might even say the result is greater than

the sum of the parts. I was pleased to see

how well these algorithms went into place

in a PI server. I hope your experience will be

the same.

Please feel free to download the templates

used here and push forward in your own

unique setting. We look forward to hearing

what you find.

Should you need support please don’t

hesitate to contact OSIsoft.

OSIsoft, LLC Jan 2018

Appendix Data Sources

The raw sensor data used in this white paper was generated by the PI Ramp Soak Interface. This

interface generates patterns of data movement using prescribed time periods, amplitude and signal

noise. The amplitudes of sensor data are based on actual machine failure data. The time periods and

reoccurrence however in these data were accelerated greatly to save time in proving out the analytics.

Therefore the elapsed time results and forecasted timestamps are much more frequent than real

machine data.

References

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http://www.maintenance.org/topic/causes-of-unbalance-in-rotating-machinery

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http://www.skf.com/us/products/condition-monitoring/surveillance-systems/on-line-systems/wireless-

systems/wireless-machine-condition-sensor/index.html

Rethink Overall Vibration Monitoring, By Trent Phillips, CMRP, CRL, Novelis, 2016

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https://www.efficientplantmag.com/2016/08/rethink-overall-vibration-monitoring/