R. Divita, ITT
Goulds Pumps, Singapore
has ever watched a medical drama knows the story. By the end of the hour, the
exasperated doctor pores over charts to find the nugget of information they are
looking for—the information in the patient’s medical history that will help
them discover their ailment.
What if the
doctor could not access the patient’s medical history? Without this important
background, the chances of identifying the cause of the illness and ending the
episode at the 60-min mark would be slim to none.
in many cases, this scenario applies to monitoring the health and condition of
machines in process industries. Often, facilities hesitate to invest in
monitoring rotating equipment until the machine fails. By that time, there is
no background information, such as vibration or temperature data, to help site
engineers diagnose the machine’s failure.
implementing a remote monitoring solution on healthy equipment, users can set a
baseline of machine health to gain essential insights when the machine begins
The way it
has always been done.
The risks associated with pump leakages are significant in process facilities
that handle materials like hydrocarbons or liquefied natural gas (LNG). To
prevent leaks, facilities like these typically do maintenance on pumps at
standard intervals (e.g., every 2 yr). In many cases, the pump is shut down as
soon as a leak appears.
this leads to an unnecessary drain on resources. The lack of insight into pump repairs
may lead to healthy pumps being shut down for maintenance, while inefficiently
operating pumps are neglected until it is too late. When healthy pumps are
repaired prematurely, the facility wastes time and money.
when unhealthy pumps are not promptly addressed, they can fail, resulting in
further production loss and safety concerns, which is the bane of any pumping
facility. To unlock access to a plethora of important machine health data
(e.g., temperature and vibration), many facilities have begun to turn to remote
machine health monitoring.
health monitoring. Remote
monitoring enables process facilities to practice predictive maintenance and
solve potential problems before they become major issues. Plants can prevent
surprise equipment failure and extend machine life by integrating a remote
monitoring system with suitable sensors and data logging functionality.
many new sensor systems can provide an Industrial Internet of Things (IIoT)
asset intelligence platform that can utilize automated diagnostics to securely
monitor and assess—locally or remotely—the health of machines. Overall vibration,
vibration spectrums, temperature and pressure data can be analyzed to detect
and resolve issues that might otherwise lead to equipment breakdowns.
facilities that elect to use remote monitoring begin by identifying bad actors
or machines that have chronic issues and consistently fail; sensors are
attached to these machines to identify any anomalies that must be addressed.
this strategy does have one flaw. Remember the scenario where the physician
cannot determine the patient’s health issues because they cannot review the
medical history? Imagine the machine is the patient, and the medical history is
all the data of the machine’s past performance, including vibration and
temperature. If it is not understood what the machine used to look like, how
and when it will fail cannot be accurately determined.
monitoring bad actor equipment when it is already experiencing issues, IIoT
sensors often simultaneously highlight a range of machine problems. The user
then finds it difficult to decide when to act, as no data is available to
contextualize the issue. The facility is essentially starting at square one. Therefore,
it is crucial to begin monitoring when the equipment is healthy.
the baseline for success. Installing remote machine health monitoring sensors on healthy equipment identifies and tracks changes over time. This includes general trends and
vibration spectrum changes. A good monitoring system will incorporate automated
diagnostics of the vibration spectrums to ensure that higher volumes of data
are successfully managed and reviewed. The longer the facility waits to begin
this process, the more data the operation is missing from the equipment’s health
if sensors are installed on five new pumps in a LNG facility, a health baseline
for these machines is set. These pumps’ vibration, temperature and pressure
data can be instantly recorded, identifying how they appear when healthy (FIG.
later, before the overall vibration has increased, this continuous collection
of data compared with the initial baseline may show that one of the pumps is
experiencing a slight increase in bearing fault frequencies. In addition, the
sensor is set to routinely capture vibration spectrum data, which shows the
degradation speed over time. The operations team can set the sensor alarm to a
level just above the known trend to receive early notification of the next
issue—in this case, a stage 4 bearing defect. Over time, alarms can be
re-adjusted to higher thresholds.
vibration steadily increases in the following months, the user can determine
when to shut down the machine for repairs, ensuring no further damage to other
rotating components and allowing them to minimize lost production time and save
with the data collected from the healthy machine’s operation through its repair
stage, the user has a picture of the machine’s estimated lifespan. This enables
them to practice predictive maintenance, establish more accurate maintenance
budgets and forecast workloads without unplanned events. For example, if the
pump reaches a Stage 3 bearing defect, maintenance can be scheduled when it
begins to approach the known threshold again.
predictive maintenance means less equipment failure, reduced machine downtime
and a decreased drain on resources. However, to get started, facilities must
implement a remote machine health monitoring system to monitor trends, gather
data and identify when machines are approaching the danger zone. There is no
better time than now. HP
DIVITA is the Manager of monitoring and control for ITT PRO Services’ Asia
Pacific region. Divita is based in Singapore and has been with ITT for 15 yr,
having joined the company in 2007 as a mechanical engineer. He oversees the
technical application of ITT Monitoring and Control solutions throughout the
APAC region, including applying IIoT solutions to pumps and other rotating
equipment, conducting site audits, leading energy studies and utilizing RCA
support and practical solutions to lower the total cost of ownership for
clients. Divita works with clients in oil and gas, power, mining and pharmaceuticals.
He earned a BS degree in mechanical engineering from Curtin University in