Maintworld Magazine 3/2021

"It is human if you don't know something"
I ended up with the same feeling after a couple of rounds of
AI Dungeon. First you are blown away, but then you realize
the model will not get the pump or turbocharger fixed. Every
random person with some brains and a search engine eventually
can formulate an answer on the first question without
understanding how a pump works. For the second example, it
is clear you need to step away from design failure modes and
have a good understanding of the operating conditions before
you can make a thorough fault diagnosis.
In fact, you cannot blame GPT-3 for being wrong, because
it was trained with knowledge from books and not with any
language that has meaning in a mining facility or a production
floor. Reading the internet is simply not enough to understand
how physical assets behave in the real world.
What is in it for the industry?
"Reading the internet is not enough. We should start
reading the factory"
General models are a great idea but if we really want to
democratize the use of language models for industrial companies,
they should start reading the factory. Therefore, we
should build out a Common Crawl for the industry: a large
corpus with domain-specific jargon, abbreviations, misspellings,
synonyms and word associations that we can typically
find in maintenance records, operator and quality logs, warranty
claims, asset datasheets, product manuals, etc. Databases
on a company level containing thousands or millions
of records are not large enough to learn the true semantics
of industrial language. The effort to build a corpus must be
industry wide.
A great example of a such an effort can be found in healthcare,
where libraries with medical lexicon are used in combination
with NLP to extract relevant clinical information
from unstructured data found in electronic health records.
It is a small step to apply a similar methodology to maintenance
logs, where the most valuable information is often
stored in free text fields, such as the symptoms and root
causes associated with failure events or other problems, and
the physical actions taken to repair components, machines,
or subsystems. This data is collected for every machine over
their entire lifespan. Structuring this human knowledge in
meaningful features and linking it with streaming process
data opens a new door for building prescriptive maintenance
applications where recommendations are based on what
works well within the particular context of a factory, and not
on what works well in general (like we find in design failure
mode libraries).
The road ahead
If we can tap into the hidden potential of all this unstructured
information and truly understand it, then we can link the ears
and eyes of the production floor with the hard facts and measurements
derived from historical and streaming sensor data.
Combining NLP with machine learning (ML) makes it possible
to build real Human-In-The-Loop applications where
actionable insights are gained from human language to support
operators, technicians, and engineers in their day to day
jobs. In addition, the knowledge they have can be used to give
feedback – in their own words – on those prescriptions, improving
the ML system for the next challenges that arise.