1Monitoring the Health
of Plant Machinery
Health monitoring or condition monitoring has
been used for many years on machines and plant
where the cost of an outage is high. It allows failures
to be anticipated and maintenance or repairs to be
scheduled for the least loss of production, as well
as avoiding unnecessary periodic maintenance.
It can be as simple as a person touring the plant at
regular intervals with portable instruments such as a
thermal imaging camera and vibration analyzer, or it
might be permanently installed so that data can be
gathered remotely over a long period, with the data
analyzed off-line and trends identified.
With the increasing use of intelligent devices on
machinery and plant, which can be networked
and their data gathered remotely at low cost, the
possibility for plant health monitoring is increasing
rapidly. There is plenty of information available
on the web giving ideas and offering products
for monitoring.
Here we will be looking at a range of techniques
which take advantage of the special position of
the variable speed drive in the machine to access
further useful data.
The techniques applied need to use low-cost sensors
and to be reasonably non-invasive, to avoid high
installation costs and the risk of damage from the
installation process. Simple sensors, such as thermal
probes and accelerometers, can be attached to
accessible parts and give a wealth of data.
For example, an accelerometer can often easily be
mounted on a bearing support or machine housing to
measure radial vibration from a rotating machine, and
it can detect defects which cause unbalanced forces,
such as broken rotating parts, cracked shafts and
misaligned couplings.
A simple amplitude measurement can give general
warning of potentially damaging defects, whilst
a deeper frequency analysis may be able to focus
attention on parts, especially if there are different
rotational speeds involved as in gearbox or belt drives.
2The variable speed drive is in a unique position in a
machine, since it usually provides the motive power. It is an
intelligent device which is closely coupled to the working
parts of a machine through the electric motor. It contains
information which it uses to do its job reliably, but which
can be accessed and analyzed at easily. In other words, it
can be used as an extra set of sensors, at virtually no cost.
To begin with, the drive has its own internal sensors for
various internal temperatures and the motor current, which
are provided by the manufacturer to prevent damage to
the drive or motor due to abnormal conditions. It may
also have a motor temperature sensor connected.
This data is available as drive parameters and can be
accessed periodically to give a warning if it is approaching
a limit, and to analyze trends.
In a closed-loop control system such as a servo drive,
the drive contains data regarding the control variables.
It is quite common, for example, to monitor the following
error in a position control loop and to raise a flag if the
error exceeds a threshold – this could indicate malfunction
such as increased stiffness (impending seizure, obstruction
or damage) or backlash (from wear).
It is a small step to move from a simple alarm threshold
to monitoring the trend of the smoothed data and alerting
the user to a developing situation which might result in
future failure.
For following error there must be at least a shaft transducer
fitted, which tends to be the case in precision motion
control applications. In all applications however, the drive
also has access to a special measurement which is difficult
to obtain by external instrumentation – the motor torque.
To measure motor torque conventionally using a
transducer is most commonly done by installing a strain
gauge or load cell in the fixing of the motor housing. This
requires a special motor mounting if it is to give a sensible
measurement of torque, and the measurement is affected
by the moment of inertia of the heavy motor frame which
reduces the sensitivity to the higher frequencies.
Even more difficult is to measure the actual dynamic
shaft torque, since this requires a rotating strain gauge
to be fixed to the shaft, with telemetry to pass the data to
the fixed side. This is an expensive operation, and is done
rarely even for a special test. It is unlikely to be a permanent
installation.
The drive however has internal data for the torque-
producing current in the motor, which is a good proxy
for the shaft torque, available at no cost! The data is even
available when the motor itself is inaccessible, whether
deep inside a machine or under water or in a hazardous
area. The accuracy of the torque measurement is best in
a fully closed-loop system, but even in a simple open-loop
drive the torque data is good enough for many purposes
except at the lowest speeds.
Once we appreciate that torque data is available in the
drive at virtually no cost, as well as the corresponding
speed data, we can enter a new realm for machine and
plant monitoring. The following is a range of possibilities
which we have encountered at Control Techniques.
Readers may have new ideas for types of machines –
it takes detailed knowledge of the machine to invent
new methods for using the torque data which is released
by the drive.
Machine or plant health monitoring using a variable speed drive
3Simple limits for average
or peak torqued
The real-time torque data can be smoothed to give a
running average value when the drive is active, or the
peak value can be captured on a timescale chosen to suit
the application, this could be anything from milliseconds
to days depending on the process. An alarm can be
generated if the value moves outside of an expected
range (i.e. it exceeds an expected value or, less commonly,
falls below an expected value).
Trend of torque
The same torque data can be logged and analyzed for
trend over time or against any other variable,
with alarms set to indicate an unhealthy trend.
Simple correlations of average
torque with speed
In many processes the torque is strongly dependent on
the speed, in a well-defined pattern. For example, a fan
or pump driving fluid through a fixed duct, pipe or loop,
or a network of them, will have a well-defined torque/
speed curve. Any significant deviation from the normal
curve indicates a change which might represent a
problem.
Some examples are:
Low torque:
• Broken drive belt or other coupling
• Loss of fluid in pump
• Obstruction to flow, e.g. blocked filter or screen
(for an impeller type of pump or fan, could apply also
to conveyor etc.)
• Build-up of deposits on fan or pump rotor
• Cavitation in a pump due to air ingress, swirl or
other faults (also causes pulsations – see below)
High torque:
• Seizure of rotor or other parts
(partial or total)
• Obstruction to flow
(positive displacement type of pump)
• Major leakage
(impeller type of pump or fan)
A torque/speed profile can be established outside
of which an alarm state is generated, for example as
shown in Figure 1: The torque data needs to be subject
to sufficient low-pass filtering or averaging to prevent
dynamic effects (acceleration torque) or normal pulsations
from generating false alarms. Other variables may have an
impact, for example a variable delivery pressure of a fluid,
so tolerance bands must be set wide enough to prevent
false alarms from this cause.
Speed (%)
Expected torque
Lower alarm limit
Upper alarm limit
120.00
100.00
100.00
80.00
80.00
60.00
60.00
40.00
40.00
20.00
20.00
0.00
0.00
140.00
Torque (%)
120.00
100.00
80.00
60.00
40.00
20.00
0.00
0.00 50.00
Speed (%)
Torque (%)
Figure 2: Expected torque for fan with two circuits with dampers
100.00
Expected torque - both open
Expected torque - damper 1 open
Expected torque - damper 2 open
Speed (%)
Expected torque
Lower alarm limit
Upper alarm limit
120.00
100.00
100.00
80.00
80.00
60.00
60.00
40.00
40.00
20.00
20.00
0.00
0.00
140.00
Torque (%)
120.00
100.00
80.00
60.00
40.00
20.00
0.00
0.00 50.00
Speed (%)
Torque (%)
Figure 2: Expected torque for fan with two circuits with dampers
100.00
Expected torq e - both op n
Expected torque - damper 1 open
Expected torque - damper 2 open
4Multi-variable correlations
In more complex processes the torque will depend on
several variables, which might or might not be available to
the drive. For example, consider a fan driving air through
a system of ducts, some of which have damper controls
to vary the local air flow. The torque/speed curve then
depends on the positions of the dampers.
If data is available regarding the damper state, or the
pressure drop over the dampers, then a multi-variable
correlation may be possible to allow for this. Figure 2 gives
a simple illustration of the case with two duct branches
with dampers.
Another possibility is to use the measured torque and
speed values to deduce the flow and pressure at the
pump or fan from their characteristic curves, which
could then be compared with a measured value from a
transducer. Any discrepancy could mean either that the
pump or fan is defective or the transducer is defective.
Figure 2: Expected torque for fan with two circuit with dampers
To
rq
ue
(%
)
Speed (%)
Dynamic analysis of torque
The torque data in the drive has a wide bandwidth and
can in principle be used for dynamic analysis. It is quite
common for the torque bandwidth to be in the order
of 1 kHz or more, although it might not be possible to
access and analyze the data at such a high rate – the
data communications channel typically limits the data
access to about a 250 ms sample interval.
The torque data relates to the electrical torque in the
motor, which is transmitted to the output shaft but
influenced by the inertia of the motor rotor and the
effective stiffness of the motor control algorithm.
These form a low-pass filter whose characteristics
might not be known.
In a fully closed-loop system it is possible to deduce the
transfer function and obtain accurate shaft torque data,
so that for example high-frequency torque reversals can
be detected. However the measurement does not need
to be precisely calibrated in order for comparisons or
trend analysis to be successful.
In practice pulsations with frequencies in the region of
100 – 500 Hz have been usefully monitored from motor
electrical torque data.
Blocks of data can be captured in real time and subjected
to dynamic analysis off line. Analysis may be in the time
domain, for example by calculating the magnitude of
fluctuations (overall torque pulsation or fluctuation, r.m.s.
amplitude with or without time-averaging, peak values
or peak negative values) or in the frequency domain
through a Fourier transform with respect to time or
some other variable such as position. This can then allow
developing changes to be detected, specifically in the
pattern of torque pulsation:
• Excessive torsional overall vibration amplitude,
wide-band or band-limited, e.g. from broken machine
parts or cavitation in pumps
• Excessive peak torques which might result in
mechanical damage or premature wear
• Frequent torque reversals which can cause gear
chatter resulting in premature wear or breakage
Speed (%)
Expected torque
Lower alarm limit
Upper alarm limit
120.00
100.00
100.00
80.00
80.00
60.00
60.00
40.00
40.00
20.00
20.00
0.00
0.00
140.00
Torque (%)
120.00
100.00
80.00
60.00
40.00
20.00
0.00
0.00 50.00
Speed (%)
Torque (%)
Figure 2: Expected torque for fan with two circuits with dampers
100.00
Expected torque - both open
Expected torque - damper 1 open
Expected torque - damper 2 open
Speed (%)
Expected torque
Lower alarm limit
Upper alarm limit
120.00
100.00
100.00
80.00
80.00
60.00
60.00
40.00
40.00
20.00
20.00
0.00
0.00
140.00
Torque (%)
120.00
100.00
80.00
60.00
40.00
20.00
0.00
0.00 50.00
Speed (%)
Torque (%)
Figure 2: Expected torque for fan with two circuits with dampers
100.00
Expected torque - both open
Expected torque - damper 1 open
Expected torque - damper 2 open
5• Frequent torque reversals which can cause gear chatter
resulting in premature wear or breakage
• Torsional resonances, e.g. from loose couplings, resulting
in peaks in the frequency spectrum whose frequency is
independent of speed although they may be enhanced
at certain speeds
• Torsional pulsations, with one or more cycles per
revolution, e.g. from cracked shaft, impeller or gear
tooth damage or other mechanical damage, with the
possibility of tracing the source in a complex machine
from the frequency of the spectral peaks, the speed,
and a knowledge of gearbox or other drive ratios
Dynamic analysis of torque
with speed correlation
In some of the examples given above it is clearly
beneficial to consider the shaft speed in conjunction
with the dynamic analysis of torque, because pulsations
relating to the rotation of the shaft will be at the rotational
frequency (once-per-revolution effects) or a multiple of it
(e.g. a cracked shaft gives twice-per-revolution, impellers
may be at N-per-revolution, gear teeth at N or N1/N2 –
per-revolution).
It can be helpful to generate compound plots
of vibration spectral analysis with speed, which will clearly
differentiate N-per-revolution effects from resonance effects
whose frequency is fixed but might be stimulated only
in certain speed ranges. These are referred to as cascade
plots or waterfall plots, and are widely offered by suppliers
of vibration analysis equipment.
and fs is the sampling frequency. fs needs to be kept
above about 3 times fd to avoid generating confusing
new frequency products within the region of interest.
An added benefit of cascade plots is that alias products
are clearly visible, their frequency falling as the speed
increases whereas with genuine effects the frequency
increases or remains constant.
Caution – sampling rates
and aliasing
Care is needed in systems with rapid torque pulsations.
The torque data is sampled at a rate which might be
restricted by the capability of the drive to store or export
data at the rate it is acquired internally. The sampling
frequency will produce alias errors at frequencies such
as (fs – fd) where fd is the frequency content of the data
Artificial intelligence analysis
In all of the above I have concentrated on
applications where a physical understanding of the
process is used to define an expected behavior, and
the available data is used to compare actual operation
with the expectation. Even if the amplitude scaling is
uncertain, the frequencies are unique and trends can be
identified. The advantage of this approach is that people
involved with the process can understand the data
and work from the information and alarm conditions
generated to develop a diagnosis for the plant.
An alternative is to use some form of machine learning
algorithm to track all the available data and aim
to deduce the patterns of normal and abnormal
behavior. This is a subject of current research.
In Conclusion
The ideas give above are general ones based on a
broad picture of a machine with rotating parts, couplings
and gears, or a pump or a fan. I hope that by pointing
out the special access which the drive gives to some
valuable data, especially the dynamic torque data,
designers of machines will be able to apply these ideas
to their own specific and unique applications.
P.N. WHP-MONITOR 02/18
©2018 Control Techniques a division of Nidec Motor Corporation. The information
contained in this brochure is for guidance only and does not form part of any
contract. The accuracy cannot be guaranteed as Control Techniques has an ongoing
process of development and reserves the right to change the specifications of its
products without notice.
Control Techniques
7078 Shady Oak Road Eden Prairie, MN 55344-3505 USA
Monitoring the Health of Plant Machinery
Health monitoring or condition monitoring has been used for many years on machines and plant where the cost of an outage is high. It allows failures to be anticipated and maintenance or repairs to be scheduled for the least loss of production, as well as avoiding unnecessary periodic maintenance.
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