Maintworld Magazine 4/2020

ASSET MANAGEMENT
opposite. These artificially-constrained
systems may become prone to unpredictable
black swans. Indeed, observing
normality, maintenance engineers tend to
believe that everything is fine. However,
environments with “artificial normality”
eventually experience massive blow-ups,
catching everyone by surprise, and undoing
years of failure-free maintenance. The
longer it takes for the blow-up to occur,
the greater the resulting harm. If anything
had indicated the need for protection,
maintainers would obviously have taken
preventive or protective actions, stopping
the black swan or limiting its impact.
It is unfortunate that we cannot develop
convincing methods to infer the
likelihood of a black swan from statisticalinductive
methods (those based on the
observation of the past) and combing this
with statistical deductive methods (based
on known valid laws and principles) to
derive the likelihood of a future event
based on the findings. This is especially
problematic in maintenance. Arguably,
Industrial AI has the potential to change
all this.
Industry 4.0 and Black Swans
In the technology industry, every new
mobile App, computer program, algorithm,
machine learning construct, etc. is
advertised as revolutionary and destined
to change the world. However, black swans
still exist and are highly impactful, especially
in a connected world. Industry 4.0
technologies must learn to handle them.
The knowledge cycle refers to the
frameworks or models used by organizations
to develop and implement strategies,
including in maintenance. The knowledge
cycle or the knowledge management cycle
(or knowledge life cycle) is "a process of
transforming information into knowledge
within an organization which explains
how knowledge is captured, processed, and
distributed in an organization." Today’s
organizations must deal with increasingly
complex problems. The rapid changes
in the economy and a highly competitive
market lead to uncertainty, making it important
to predict possible outcomes or
events to remain operational. In addition,
there is a need to develop knowledge life
cycle strategies to recognize the possibility
of an unlikely critical situation – this, of
course, is not easy, but organizations have
access to immense knowledge. This knowledge
should be managed systematically
to identify and eliminate unpredictable
events or reduce the consequences.
The Industrial AI learning framework
aimed to turn black swans in maintenance
to white swans is shown in Figure 1. As
the figure shows, it is a mixture of various
conventional knowledge cycle models.
This integrated knowledge cycle model
suggests a way to find black swan events,
create a strategy to prevent them, and to
incorporate that strategy. The framework
covers two major areas of knowledge:
known and unknown. The conventional
cycle steps of known knowledge include
knowledge capture and creation, knowledge
dissemination, knowledge acquisition
and application, knowledge base
updating. Black swan events are an example
of unknown knowledge. The cycles of
known and unknown knowledge move at
the same pace and merge at the end with
the main goal of recognizing a black swan
and resisting it. The resulting white swan
or new known knowledge allows the exploration
of previously unknown areas.
Black Swan and
Anomaly Detection
In statistical terms, a black swan corresponds
to the disproportionate contribution
of a few observations to the overall
picture. In maintenance, a few observations
can constitute the normality, the
information provided by outliers may be
missed, and the resulting reduced data
set of failure modes will neglect the total.
Even a simple underestimation of the required
sample size can cause a black swan.
Maintenance engineers use stochastic
processes and such tools as reliability
estimation to predict the behaviour of assets,
but the excessive application of the
“law of large numbers” is not advisable.
Simply stated, the law of large numbers
indicates that the properties of a sample
will converge to a well-known shape after
a large number of observations. Although
bigger datasets of faults lead to greater
accuracy and less uncertainty when predictive
maintenance is performed, the
speed of convergence (or lack of it) is not
known from the outset.
Outliers are considered by modelers
in risk management, but they cannot
capture off-model risks. Unfortunately,
in maintenance engineering and asset
management, the largest losses incurred
or narrowly avoided by maintainers are
completely outside traditional risk management
models.
Predictability of Black Swans
Given the subjectivity of human decision-making,
incorporating the use of
AI modelling as a tool could positively
impact outcomes and support maintenance
expertise. Arguably, using datadriven
approaches increases objectivity,
equity, and fairness. Machine learning
can quickly compile historical data and
create a risk map to assist with decisions.
In addition, using a predictive model that
has a learning component can account
for variations in different subpopulations
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