Automated Approach to Development and Online Operation of Intelligent Sensors

glibdoadingΤεχνίτη Νοημοσύνη και Ρομποτική

20 Οκτ 2013 (πριν από 4 χρόνια και 8 μήνες)

139 εμφανίσεις

Automated Approach to Development and Online Operation of
Intelligent Sensors

Vasiliki Tzovla and Ashish Mehta

Emerson Process Management


Neural Netw
orks, Soft Sensors, Prediction, Process Identification


In the recent past, neural networks have been used to create intelligent (or soft) sensors to predict key
process variables that cannot be measured on
line, to pre
empt lab analysis delays
and to validate
physical sensors. However, they have had limited application due to two significant drawbacks: first,
traditional neural net development is a fairly complicated task requiring extensive third party “expert”
intervention; and second, due to

their inherent nature, neural nets do not handle changes in process
operation over time very well, often requiring retraining. In this paper we present an approach that
addresses both problems. Neural Nets are represented as a function block in a structu
red system, and by
using state
art techniques and intuitive GUI applications, the commissioning process becomes
fast and easy. Automation of the following: data collection and pre
processing, identification of relevant
process inputs and their dela
ys, network design, and training, enable the process engineer to develop
neural net strategies without the need for rigorous techniques. In online mode, the actual process
variables, for example those obtained as a result of lab analysis, are used as input
s to the neural net for
automatic adaptation of its prediction in response to changes in process. The paper details these and
other features of convenience.


An intelligent sensor, more popularly known as a ‘soft sensor’, is based on the use

of software
techniques to determine the value of a process variable, in contrast to a physical sensor that directly
measures the value of the process variable. These sensors open a whole new world of possibilities and
options that help to circumvent issue
s such as maintenance, cost and online use of physical sensors. A
soft sensor is a highly trained neural network based model that takes real process variables as inputs and
predicts values of the process output variable online [1, 2, 3]. Classical approach
es to the problem of
designing and training a neural network for the actual process conditions involved a sequence of
complex steps which often was demanding for the typical plant engineer / operator. More often than not,
it required a highly trained profe
ssional to create models. These models also had the drawback of not
being able to constantly adapt to drifts in process inputs and other process conditions.

Figure 1: NN block predicting values otherwise obtained from A) Lab Analysis (left), and B) Sampled

Analyzer (right)

Neural networks are essentially non linear function approximators that automatically adjust parameters
known as weights in an iterative procedure of learning the underlying function, a procedure known as
training. Training a soft senso
r model consists of presenting information to the model, comparing the
output to a target value, adjusting network weighting, and repeating the training until an acceptable
output is achieved. What makes soft sensors useful is the ability of the underlying

model to infer in real
time a measurement otherwise available only after significant delays from analyzers or lab tests.

Urged by the industry’s need for development tools, several soft sensor suppliers and control system
manufactures have expended consi
derable resources to create applications to support the process
engineer and the plant operator. The ease, with which a system may be implemented, commissioned and
maintained though, is influenced to a large extent by the user interface supplied by the man
ufacturer and
its capability to integrate with the control system. In many cases, the application and general acceptance
of advanced control and monitoring tools within the process industry has been limited by ease of use
issues. Commercial products have
too often violated some of the very basic principles of good usability.
As a result, typical process engineers and instrument technicians may have difficulty in addressing
neural net based applications, while the plant operators may be faced with increasin
gly complex user
interfaces which provide minimum or no support. This paper explores how some commonly accepted
practices in user interface design have been successfully applied in a neural network (NN) application.
Examples show how this tool makes it ex
tremely easy and intuitive to configure and run an NN strategy,
and how friendly and explanatory interfaces provide the plant operator with the support needed to
increase the lifetime of the NN model. Simplicity, nonetheless, is achieved without any loss o
functionality and sophistication. In particular, the needs of the expert user are addressed without
sacrificing the ease with which a normal user may implement such advanced applications.


The implemented strategy uses
what are commonly referred to as function blocks (in the Fieldbus
control paradigm [4]), wherein each function block is a part (e.g., a subroutine) of an overall control
system and operates in conjunction with other function blocks to implement control and

loops within the process control system. Function blocks typically perform one of an input function
(transmitter, sensor, etc.), a control and analysis function (PID, fuzzy logic, etc.), or an output function
(e.g., a valve). It must however
be noted that the control routines may take any form, including
software, firmware, hardware, etc. Similarly, the control and analysis strategy could be designed and
implemented using conventions other than function blocks, such as ladder logic, sequentia
l function
charts, etc., or using any desired programming language or paradigm. NN development is integrated as
part of the control strategy, obviating the need for off
line mathematical data processing packages, or
development of interfacing layers betwe
en the process control system and the NN application.

The NN block is implemented as a drag
drop function block capable of execution in a controller of
a scalable process control system [5]. As a soft sensor, the NN function block has one output, the

process variable being predicted. The process variables that are expected to influence the output are
‘soft’ wired as block inputs from anywhere within the existing control system. Extensibility of the
number of input parameters inherently handles the con
troller memory and performance requirements for
widely varying multivariable processes. Properties such as the range and engineering units are
automatically inherited from those of the referenced process variables. For creating the NN model,
known set of i
nput and output data has to be presented for training purposes. Input values are obtained
via the ‘soft’ wired referenced variables. Connecting the process variable to be predicted to the NN
function block provides the output sample values. Later this conn
ection is used for online adaptation of
the predicted value. Typically this may take one of the following configurations:

Lab Analyses: For cases where the output is available only through lab test and measurements, the
NN block is used in conjunction with

a Lab Entry (LE) block as shown in Figure 1A. Since process
lab analysis values are not available in real time, in addition to the sample value, an additional
parameter shown as DELAY in the figure is also provided to the NN block through the LE block.
is parameter denotes the time delay between grabbing the sample and obtaining its measured
value. When online, the NN block provides a real
time continuous output prediction, including for
times in between lab grab sample results.

Continuous Measurement: W
hen used along with analyzers for continuous measurement, cross
validation or backup, the analyzer output is fed to the NN as shown in Figure 1B, where an Analog
Input (AI) block is used for the analyzer. In this case, the delay parameter of the NN block r
the response time of the analyzer measurement.

Once configured, the control module (configured control/analysis strategy comprising the NN and
interconnecting blocks such as LE) is downloaded to the controlling device. In this manner, the NN
omes an integrated part of the existing control system with no additional requirements for any third
party interfacing and integration layers. Furthermore, the configured process inputs, the output, and the
measurement delay parameter are automatically ass
igned to the control systems’ historian for archiving.
A user configurable sampling rate provides flexibility for handling processes with different response
times as well as optimizes memory requirements of the historian.


This section d
etails the methodology and environment for the plant engineer to generate an NN model in
an easy and intuitive fashion, with little or no understanding of its mathematical complexities.

Data Collection/Retrieval:

To create an accurate NN soft sensor it i
s imperative that the data used to
generate the model be representative of the normal operation of the plant. Morrison et al [1] elucidate
some of the key issues in data handling such as inspection of data, removal of bad data, handling of
missing data, de
fining data ranges and removing outliers (data values outside established control limits),
and ensuring that there is sufficient variation in data over the desired region of operation. For the NN

Figure 2: Trend Display of NN block data

block, as noted a
bove, data is already stored in the historian, and so available to any application that may
connect to it. However, it is possible that some quality historical data is available in other external
sources and so retrieval of data from file (in standard file

formats) is also provided. The application
directly connects to the integrated control system, retrieves the data from the historian or other identified
source and displays the process variables in a user
friendly graphical trend format, with features to
zoom, scroll and pan the trends for accurately defining the data to be selected. The data to be used for
identification can be marked by dragging bars along the time scale of the trend, as well as deselecting
regions of data that are unsuitable for
training. If data is found to be missing, rather than interpolating,
the user is provided with a visual indication of data slices that should be excluded. Figure 2 graphically
shows these features.

The model a neural net creates is based entirely and only

on the data it is trained on. That in turn implies
that the data should be limited to valid operating ranges. The application automatically applies statistical
methods such as use of mean and standard deviation to define the range of usable data. Users ca
n also
graphically mark the outlier limits as indicated in the Figure 2. At the same time, expert users can view
the results of pre
processing, including outlier limits, quantity of data analyzed, mean and standard
deviation of distribution, etc., and take

appropriate action. Once data to be used is marked out by the
user, the application processes it, automatically taking into account the information provided on outliers,
ranges, bad and missing data. Another feature of convenience is automation of the tim
e shifting of
output values. As seen earlier, generally there is a time delay between the generation of the sample and
availability of its measured value. There being temporal relationships between process variables, the
data needs to be realigned so that
the values are time coincident. Conventionally, users have to manually
shift the data based on time stamping, or use an average value for the time shift when individual sample
information is not available. Instead, using the delay parameter each output sam
ple value is transparently
shifted by its applicable delay to form the data set. The well
conditioned data after this vital pre
processing step is used in all subsequent operations.

Selected Data
region (end)

boundary for
an input

Data marked
for exclusion

Trended process
variables information

Selected Data
region (start)


Figure 3: Variation of correlation sensitivity values with input delays
for A) automatically calculated
delay (left), and B) Manually (by process expert) entered delay

Model Input Configuration:

One of the key problems in NN based process prediction is the lack of
a priori

information on the variables that have an in
fluence on the output. The trained NN
would be more accurate if only variables that affect the output are included. It is also well known that
process outputs depend on input variables over time, and so inputs at certain delays rather than their
ous values should be used. In fact it is the experience of the authors that the fundamental
problem encountered by most intelligent sensor applications is the determination of time delays for
inputs. Usually, this involves trial and error techniques with
multiple passes of an algorithm that
iteratively reduces the range of user specified possible delays, thereby relying heavily on hard to acquire
solid process knowledge. On the contrary, the approach presented here requires the user to provide only
the app
roximate process response time, a value and concept that most plant engineers/operators are very
comfortable with. The NN application then employs a two
stage technique to automatically determine
the appropriate input delays and the relative importance of
the configured inputs. Algorithmic details are
provided in [6]. Briefly, the cross
correlation coefficients between the output and the time shifted input
is calculated for all input delays up to the response time. Peaks in these values are parsed as defini
indicators of strong dependence of the output on the input at that delay. Figure 3A shows such a cross
correlation plot for an input up to a delay horizon of 100 seconds, with the maximum occurring at 24
seconds, the identified delay for this input. In
all subsequent processing, the data corresponding to this
input is realigned in accordance with this delay.

The application then computes the sensitivity of the output to the input at the determined delay. While
the delay is calculated considering input
s individually, sensitivity analysis effectively compares the
influence of the inputs on the output. In some sense it ranks the inputs in order of importance. It is not
uncommon to find some variables that have significantly more or less influence than ori
anticipated. Furthermore, usually only a few of the originally configured variables are key to the
prediction of the output. Sensitivity values are expressed such that their sum total is one (1). The average
sensitivity (mathematically, average sen
sitivity = 1/number of configured inputs) is a good indicator for
comparison, and is used to eliminate inputs that are not significant. Figure 3A also plots a bar for the
sensitivity value at the delay, and numerically compares it with the average. A simpl
e yet complete
overview of the participation of all the inputs configured in the NN block is provided in Figure 4, which
visually displays the individual as well as the average sensitivities. The length of the bar corresponds to
the sensitivity of the part
icular input and the dashed line represents the average sensitivity. Four inputs

Correlation at
different delays

Sensitivity at
delay with max
correlation va

Sensitivity at
specified delay

Figure 4: Overall view of Configured Inputs Participation

have been eliminated as irrelevant to the output prediction using this information. The delay and
sensitivity info
rmation can also be viewed in a tabular manner. The detailed information for each input
is navigable in a Windows like format from the left pane of the Figure 4.

Expert users can tweak the input delays and the inclusion/exclusion of inputs based on their

of the process. Both the graphical and tabular (numerical) displays provide editing capability. The
sensitivities are then recomputed with the updated information. This is illustrated in Figure 3B, where
the identified delay is manually changed
to 70 seconds. As expected there is a reduction in the
sensitivity value, in fact it is smaller than the average in this case. In this manner the NN application
determines the most suitable input
output configuration while allowing users to embed their pro


Based on the delay and sensitivity analysis of the previous step, data is realigned to form the
training set. At this stage the process variable values are maintained in engineering units. Some variables
like temperature, for exa
mple, may have a range of a thousand degrees, others like composition have a
range of a few units. In a multivariable model, incremental changes from the two variables should be
equalized so that learning is not biased to the EU range. Uniform scaling via
normalization of input and
output values equalizes the importance of inputs. Conventional neural net training requires deep
understanding of the theory and behavior of these nonlinear engines, right from determining how many
hidden nodes to use, to establi
shing the convergence criteria that will stop training. Recognizing that this
is knowledge that only NN experts have, not the plant engineer who has to use the NN, the training
procedure has been greatly simplified while maintaining sound underlying core t
echnology as described
in detail in [6].

Figure 5: Error vs Epoch for automatic training of NN with incremental number of hidden nodes

A suitable NN architecture is determined by training with an incremental number of hidden nodes.
Network weights are

randomly initialized to small values, and for cross
validation purposes, data is
automatically split into training and test sets. Training and test error are computed at each pass of data
(epoch) through the network. Figure 5 shows the training progress
wherein for each hidden node
number, the best combination of error (test and train) is determined. Spikes in error values correspond to
initialization of the network when another hidden node is added. For clarity, the normalized values
are converted bac
k to engineering units and displayed along with the minimum error epoch number in
the adjoining table. The test set is used to prevent the NN from overfitting the training data as the goal of
the network is to learn to predict and not to memorize the train
ing data. This dual approach establishes
the most suitable network design by picking the optimal number of hidden neurons and corresponding
weights, which is four in Figure 5 even though the NN was trained with 1
5 hidden nodes. [6] also
describes several
enhancements that help realize a fast learning robust NN.

It must be noted here, that a single button click automates the last two steps of delay/sensitivity analysis
and training for the normal user. In numerous experiments, the automatically calculat
ed parameters have
proven to generate satisfactory models. As for sensitivity analysis, the knowledgeable expert has the
flexibility to modify training parameters such as minimum / maximum number of hidden neurons,
percent of data split between test and tr
ain sets, random vs sequential split of test and train data, etc.

Once training is over, the weight parameters of the NN are de
normalized so that scaling the process
variables is not needed when the model is online. At this stage an intelligent sensor c
apable of predicting
the process variable of interest is available. Since the model may not be satisfactory, it is not directly
downloaded into the controlling device. Download has to be initiated by the operator, but is merely a
click away as the NN funct
ion block has been configured to run a downloaded model. Another desired
functionality is the capability to compare and switch models generated over different data sets and / or
with different parameters. Therefore, the process models for a particular NN
block are automatically
saved on creation, and remain in the database of the control system until they are explicitly deleted.
Figure 4 for example, has three separate models for the same NN block, any of which may be put online.
Pertinent details of the m
odel can also be printed out for comparison / recording purposes.


Once a model is developed, it is validated by applying real input data to it and comparing
its output with the actual process output. Even though a test set is used during tr
aining, verification
should always be carried out if additional data is available. The validation / verification data set must be

Figure 6: Verification A) Actual and Predicted vs Sample, and B) Actual vs Predicted (right)

different from the trainin
g and test data sets but should represent the data region the NN is expected to
operate on. Figure 6 shows an example of model validation results. Graphical comparison as well as the
root mean square error information is available. If the root mean square
error per scan is not satisfactory,
the model should not be put online. The Actual vs Predicted plot at times is very useful in determining
the modes of the process operation. Clusters indicate regions of operation, and so, multiple clusters
might imply va
riations in plant operation such as those due to seasonal changes. From Figure 6B, it
appears that the process has two regions of operation, the actual value plots in Figure 6A also
corroborate this. For this example, it seems that the data for region II i
s limited, and that may mean re
training with more data from that region. In some situations, separate models for the different modes
would achieve better results.


Once a suitable NN model is created, simply downloading to the controlle
r integrates it into the existing
control strategy. The model becomes part of the NN block and operates with mode and status
information similar to other blocks. In the online mode, real process variables act as inputs to the NN
model that generates a pred
icted output. This value may be used by an operator or in control to correct
for changing conditions. Since the inputs are soft wired as references to the NN block, a clean and
simple interface is maintained. The excluded inputs are not part of the model a
nd online prediction, but
they need not be removed for possible later use in re
training. Maintaining inputs as references allows
such flexibility while minimizing confusion on the inputs excluded/included in a particular model. The
same NN block, therefor
e, may have models with different inputs being used (depending on changing
process conditions) as long as they are originally configured as input references. It also need not change
if the input configuration of the model changes over time. The following f
eatures add to the lifetime and
capability of the online NN model.

Automatic Correction:

The process output stream, predicted using the Neural Network and measured
upstream conditions, is automatically corrected for error introduced by unmeasured disturba
nces and
measurement drift [7]. This correction factor is calculated based on a continuous measurement or
sampled measurement of the stream provided by an analyzer or lab analysis of a grab sample, the
configurations of Figure 1. Two approaches have been u
sed to calculate the correction factor that must
be applied to the NN prediction. Both are based on calculation of the predicted error using the time
coincident difference between the uncorrected predicted value and the corresponding measurement
value. Dep
ending on the source of error a bias or gain (ratio) change in the predicted value is applied. To

egion II

Region I

Figure 7: Automatic Correction mechanism

avoid making corrections based on noise or short term variations in the process,
the calculated correction
factor i
s limited and heavily filtered e.g. equal to 2X the response horizon for a change in a process
input. During those times when a new process output measurement is not available, the last filtered
correction factor is maintained. An indication is provided if

the correction factor is at the limit value.
The correction can be turned on or off, and the filter and limit values are configurable online, providing
added flexibility as process conditions change. Figure 7 shows how the automatic adaptation mechanism
orks in conjunction with the NN model prediction. This correction eliminates the need for re
the NN in the case of drifts and unmeasured disturbances and greatly enhances the lifetime of a running
model. Of course if the process undergoes signific
ant changes, a new model should be created.

Future Prediction:

In typical NN applications, the process is characterized by large delay times. The
plant operator needs to wait for the delay time to observe whether change in the input achieves the
effect on the output. In many cases, this may result in out
spec product and significant losses
in terms of time, money and effort before corrective action can be taken. The NN block provides a
FUTURE parameter (Figure 1,7), that provides the ability to

predict outputs in the future. It is calculated
by setting the delays associated with the inputs to zero, i.e., assuming the process is at steady state with
the current set of input values, and predicting the neural net output. This ability to predict the

output in
the future allows the user to perform “what
if” analysis and make real
time corrections for input

Range Handling:

Unlike first principles based parametric models, neural nets are approximators that
attempt to form the best mapping bet
ween the I/O data seen during training. In essence, this implies that
due to their nonlinear nature, they will not do a good job of prediction over regions not seen during
training. In a real process plant environment this can cause very poor results as pr
ocess variables tend to
change over time. The training data may have been limited and so certain regions of plant operation, for
example seasonal changes, were not included. Again taking advantage of the coupling between the NN
function block and modeling
application engine, the outlier limits that were either automatically
calculated or defined by the process expert, are invoked in the online mode. The user is informed if input
ranges are being violated while clamping them to the outlier limits and calcula
ting the predicted output.
Heuristics based on the number of such range violations and the relative importance of inputs with
values beyond the control limits determine whether the prediction is considered uncertain or bad. In the
normal operation, all val
ues are within the training range and the predicted output has a good status.
Operators can make use of this status information when applying the predicted output elsewhere in the


The design and implementation of a new and simplified

approach to the commissioning and
development of neural net based intelligent sensors has been presented. By embedding the NN function
block in the process control system, and automating the model development, benefits of neural net
techniques can be easi
ly applied to a variety of processes without the overheads incurred in traditional
implementations. An intuitive and user friendly GUI minimizes the engineering and implementation
effort while maintaining the underlying NN technology. Several enhancements
to the online operation of
the model increase its lifetime and maintainability even in the presence of unmeasured disturbances and
process drift. This approach is instrumental in the development of the next generation of easy to use,
integrated NN applicat
ions implemented in a scalable process control system [5].



Morrison, S. and Qin, J., “Neural Networks for Process Prediction”, Proceedings of the 50

ISA conference, pp 443
450, New Orleans, 1994.


Ganseamoorthi, S., Colclazier, J., and

Tzovla, V., “Automatic Creation of Neural Nets for use in
Process Control Applications”, in Proceedings of ISA Expo/2000, 21
24 August 2000, New Orleans,


Fisher Rosemount Systems. “Installing and Using the Intelligent Sensor Toolkit”. User Manual for
the ISTK on PROVOX Instrumentation, 1997.


US Patent Application, Fisher File No: 59
11211, Blevins, T., Wojsznis, W., Tzovla, V. and Thiele,
D., “Integrated Advanced Control Blocks in Process Control Systems”.



Home Page:


Mehta, A., Ganesamoorthi S. and Wojsznis, W., “Identification of Process Dynamics and Prediction
using Feedforward Neural Networks”, submitted for ISA Expo/2001, 10
13 September, 2001,
Houston, TX.


US Patent Application, Fisher File No: 59
11243, Blevins,

T., Tzovla, V., Wojsznis, W.,
Ganesamoorthi, S. and Mehta, A., “Adaptive Process Prediction”.