by Kevin Clark, Director of Global Service and Alliances, Fluke Corporation
As companies begin leveraging the Internet of Things (Io T) and Software-as-a-Service (SaaS) systems such as Computerized Maintenance Management Software
(CMMS), there is greater opportunity to take control of operations, quality, and safety. This is critical for manufacturers and facilities, as a common challenge in the industry is
converting data into information and insight useful for plant
personnel. In fact, the ability to connect assets and feed
information into a central system gives industrial plants the
power to turn data into powerful insights and automatically
take corrective, preventive or predictive action.
Io T is the inter-networking of physical devices, vehicles
(also referred to as “connected devices” and “smart devices”), buildings, and other items, embedded with electronics, software, sensors, actuators, and network connectivity
that enable these objects to collect and exchange data.
Cloud-based systems and Io T enable companies to buy
best-of-breed solutions without turning the IT department
upside down, or needing to analyze complex data. Embracing
data-driven aspects of Io T such as machine learning, analytics and mobility can revolutionize maintenance processes
within a plant.
Maintenance Machine Learning
Machine learning is a type of artificial intelligence that
provides computers with the ability to learn patterns and
trends without specific programming. With these advanced
algorithms comes the possibility to stack information associated with multiple variables on top of one another. For
example, rather than monitoring a single variable such as
difference in reservoir pressure, machine learning via a
CMMS can take into account a number of factors important
to overall maintenance strategy.
Data is recorded, contextualized and visualized via CMMS
reports and dashboards, allowing technicians in the field to
monitor equipment performance of wells and other equipment to make data-driven decisions to improve operations.
Predictive maintenance software and condition monitoring
tools can also identify and avoid small scale and catastrophic
failures. Unplanned production stoppages and rate loss can
have an enormous impact on the productivity and profitability
Turning Ordinary Data into
with IoT & CMMS
of process plants.
For example, in chemical processing reactors often have
mixers with agitator blades. The agitator runs at different
speeds depending upon the specific product being manufactured and the liquid level in the reactor. Excessive vibration
during mixing can cause a number of problems leading to
higher maintenance costs and significant production losses.
The same is true for other types of rotating machinery.
With a CMMS, users can define acceptable boundaries
for equipment vibration, import readings, graph results ,
and automatically trigger an email or generate a work order
when boundaries are exceeded. The empirical data provided
within eMaint can help improve asset reliability and boost
Collecting analytics involves reviewing historical data to
identify potential trends, to analyze the effects of certain decisions or to evaluate the performance of a piece of equipment.
With Io T, analytics on assets, labor and work management
are readily available. More specifically, through advanced
analytics, teams are able to interpret data from multiple
sources such as ERP, EHS, quality systems, MES, and asset
This analysis provides a deep and wide perspective, exposing conditions that historically required data engineers
and expensive testing equipment. Teams can prioritize their
workload, avoiding failures and optimize preventive maintenance schedules. Integrated data streams—such as power