Introduction
Human beings have come a long way
in a relatively short period of time, it was not so long ago that we were
actually keeping track of maintenance actions on a log sheet. Inventors see the world in a different way,
not just as it is, but what it could be.
Often times innovative ideas come from not just thinking outside of the
box, but also what we can do with the box.
The sky is not the limit, instead, the sky is our playground. Throughout history, we humans have been
exceeding the status quo and pushing the limits, from the wheel and plow, to
vaccines and medicine, satellites, smartphones, and even unmanned airplanes. So, what is the next step how will science
and technology continue to evolve and revolutionize the world as we know it
today? To answer this question, we must
consider how inventors become innovative, what motivates them, and where do
they get their ideas from. Gaming ideas
usually come from errors or unwanted accidents that have occurred, including
predictive maintenance.
History of Predictive Maintenance
The history of predictive maintenance began in the early
1950s, and it was primarily developed by the Japanese (Roser, 2021). Back then maintaining your equipment and
machinery was essential to your business.
Often times maintenance was more reactive than it was proactive, the
motto was simply if something breaks then fix it.
After World War II the Japanese economy and industrial
infrastructure was in bad shape. At that
time the Japanese leaders began to adopt American philosophies about quality
management and control. This led to
Total Quality Management and was introduced by Dr. W Edwards Deming during a
lecture in Japan. More lean principles
were introduced and there was more of a widespread emphasis placed on automated
equipment, tools, processes, and procedures.
As we moved to a more digital platform that greatly increased the efficiency
of production, this also brought along many unforeseen challenges. As skilled workers diminished, there were not
enough technicians that could maintain the machines, and this led to costly
breakdowns. This contradicted their new
paradigm to produce high-quality products, you cannot achieve high-quality
products with poorly maintained equipment (Cumming, 1950). This spearheaded predictive preventative
maintenance, which is the concept of daily maintenance. This consist of properly managing and
planning maintenance activities to keep the equipment in good operational
condition. The intent is to avoid
failures and unwanted accidents by engaging in periodic inspections, routine
maintenance, and prevention of deterioration.
Toyota was one of the first companies to introduce preventative
maintenance, by introducing automation, then by increasing maintenance technicians,
and also by implementing preventative maintenance techniques (Roser, 2021). This helped to advance technology as we know
it today, these combined techniques and practices with earlier lean principles
is what helped to lead to unprecedented levels of efficiency and success.
Predictive Maintenance Today
The intent of predictive maintenance
is to improve maintenance efficiency and effectiveness, see figure 1. Technological advancements were made to
ensure that the maintenance actions were carried out, but also performed in
such a way that it is cost-effective. It
was important to factor in proper maintenance planning and scheduling, while
also ensuring that lean maintenance principles were followed (Wireman, 2004). Unwanted accidents and
catastrophic events led to predictive maintenance solutions and failure
reporting systems that are supported by machine learning.
Figure 1. Predictive Maintenance and Its Role in Improving Efficiency
Companies have now turned to predictive maintenance to avoid these severe
economic losses and to increase system reliability. With the use of machine learning, companies
can implement predictive maintenance techniques. Data has been developed and collected, and
preventative maintenance solutions have been tested in a simulated environment,
and the results demonstrate that preventative maintenance technology is able to
predict machine states and failures with high accuracy (Paolanti et al, 2018).
There are also economic forces that are driving predictive
maintenance because it helps to drive cost efficiency. Predictive maintenance can drive substantial
savings by increasing production, performance, and quality. This will also lead to cost savings with
reduced maintenance actions because innovative technology now allows us to
predict failures before they happen.
Before predictive maintenance existed, there were several accidents that transpired. Machinery and complex systems like aircraft were inspected based on handwritten planned-out maintenance schedules, which were determined by the manufacturer based on the life expectancy of parts studies. In this case, some parts of the aircraft were required to be disassembled so that those sub-parts could also be inspected and analyzed to ensure proper maintenance. There was always a risk associated with the inspection activity because it could actually introduce failures if the inspector failed to put the sub-parts back together correctly. So this required a new approach that required that maintenance actions actually be in sync with the current condition of the equipment and that it should be carried out based on the equipment’s actual usage patterns. Based on the usage patterns, data could be collected, and machine learning could be implemented to predict preventative maintenance actions that should be taken in order to prevent unintended accidents. This was the true beginning of innovation, which led to the use of probability statistics to determine inspection cycle strategies which is ultimately the foundation for predictive maintenance, which is a driving technological force.
Figure 2. Predictive Maintenance Infrastructure
Cumming, W. (1950). The Origin of Preventive Maintenance And What
It Includes,"
SAE Technical Paper. https://doi.org/10.4271/500032
Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., & Loncarski, J. (2018, July).
Machine learning approach for predictive maintenance in industry 4.0. In 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) (pp. 1- 6). IEEE.
Roser, C. (2021). A Brief History of Maintenance. Retrieved on September 4, 2022
from https://www.allaboutlean.com/maintenance-history/
Wireman, T. (2004). Total productive maintenance. Industrial
Press Inc. Global
Education US. https://coloradotech.vitalsource.com/books/9781119713197
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