Efficient Plant April 2019 - 14

feature | operating strategies
1.4-million readings and caught 13 defects. In
comparison, APS' old walkaround program would have only collected 5,000
readings, most of which wouldn't
have corresponded with the key
operating hours.
With ML, millions of data
points can be run through
advanced algorithms that are
constantly improving and
creating better prediction
models. It would be physically
impossible for a human analyst
to process the quantity of data
that AI, ML, and IIoT advancements create. "Of course," Khushraj
emphasized, "more data doesn't mean
more insights unless something is done
with it."

ML AS AN ASSISTANT
According to Khushraj, the best PdM programs view ML as an
assistant to in-house reliability staff. Rather than replacing engineers or technicians, it enables them to focus only on the assets that
need attention, obtaining more data to make better diagnoses, with
real-time alerts.
"ML," he said, "is perfectly suited for the mundane tasks that
humans aren't suited for, as in finding patterns in millions of lines
of data. Humans are far superior when it comes to determining the
meaning of the data."
Experienced reliability personnel can add their knowledge to train
the models on failure modes. Instead of the system only detecting an
anomaly, it could alert personnel of specific problems. "Today," Khushraj stated, "there are still no systems that can accurately tell you all
of this information. The knowledge of the experienced individual is
critical in understanding what to do about the potential problem. In
short, humans need ML and ML needs humans."

INCREASE YOUR VALUE
With routine data analyses increasingly performed by algorithms,
Khushraj said reliability personnel can apply their knowledge to
preventing failures. As an example, he described a recurring pump
failure due to undersizing for the application. "The underlying root
cause or 'why,'" he said, "can't be captured by an algorithm."
Eliminating mundane tasks for certain personnel has other
advantages. Among them, it can help shift plant maintenance efforts

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from reactive to predictive to prescriptive.
Similarly, an ML-based system can take
into account parameters that have not
traditionally been monitored, such
as simultaneously examining
operations and condition data.

GETTING STARTED
Instead of completely overhauling their existing PdM
programs, he encourages
personnel to look for areas
that can be improved, i.e.,
assets that are not currently
being monitored or ones where
failure is happening in between
routes. Use the algorithms in tandem with existing analyst knowledge
so you can validate that it is catching the
same failures a human would catch, without
giving false positives. This will help to reassure
everyone that it will work.
"If you don't have data scientists on staff," he advised, "find a
provider offering pre-built applications that don't require specialist
skills." Keep in mind, though, that a general-purpose ML system
capable of building algorithms for any application, i.e., operations,
maintenance, finance, will require more data-science knowledge than
one built for a specific problem.
"Identify the problem you are trying to solve," Khushraj said,
"and select a few companies to pilot, testing the algorithms against
each other to see which one performs the best, and is the easiest to
configure and use."

THE FUTURE IS NOW
The bottom line for Khushraj is that change is going to come,
whether we like it or not. Organizations (and workforces) that fear
advanced technologies (or are slow to adopt them) will be replaced
by others that view technology as a tool to help improve human
efficiency. "Embrace the opportunity of having an assistant that can
help sort through repetitive tasks, allowing you to do the things you
love," he urged. "The smarter the assistant, the better you will be at
your job." EP
Petasense (petasense.com) offers an end-to-end solution with a wireless
vibration sensor, cloud software, and analytics that supports asset
reliability and PdM.

APRIL 2019

http://www.petasense.com http://www.EFFICIENTPLANTMAG.COM

Efficient Plant April 2019

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