The method for detecting in a controlled apparatus and process the freezing or failure of a sensing process and assembly comprising a sensor generating a sensor output signal and related components converting the sensor output signal into a process signal derived from the sensor output signal, with the sensor being subjected to continuous transient variations due to process noise in the controlled apparatus and process, comprises the steps of: determining in real-time processing mode an estimate of the standard deviation of the process signal, comparing the estimate of the standard deviation of the process signal with at least one predetermined reference value, and producing an unacceptability signal if the comparison results in the determination of unacceptable deviation between the compared values. Further, an apparatus for detecting in a controlled apparatus and process the freezing or failure of a sensing process and assembly is described.

This application is a 371 of PCT/US93/11303 filed Nov. 19, 1993. 
BACKGROUND OF THE INVENTION 
The present invention generally relates to a method and an apparatus for 
detecting the freezing or failure of a sensing process and assembly in a 
controlled apparatus and process. The sensing process and assembly 
comprises a sensor which generates a sensor output signal which is further 
converted to a process signal by the sensing process and assembly, and the 
sensor within the sensing process and assembly is subjected to 
(continuous) transient variations due to process noise in the controlled 
apparatus and process. 
In the case of process control, noise signals can either be produced in the 
controlled apparatus and process (to which measurements are applied) as 
process noise or in the measurement method (which is implemented using an 
extended sensing process and assembly) as measurement noise. In this 
connection the term "noise" is used to signify all forms of interference. 
It is usually not possible to identify the source of noise in a signal 
through the use of conventional analytical techniques on the time discrete 
signals themselves. Usually, noise will demonstrate an oscillatory or 
vibrational characteristic when a series of discrete signals is analyzed 
as a function of time; usually, the overall oscillatory pattern can be 
resolved into a set of sine waves having characteristic frequencies. 
Process noise is produced from transient variations in the controlled 
apparatus and process itself in the regions proximate to the location of 
the measuring sensor. There are a variety of factors that can contribute 
to such variations in the controlled apparatus and process itself such as 
the presence of entrained gas bubbles in liquid, local variations in a 
liquid density as in the case of agitated oil/water mixtures, local 
variations in liquid temperature, turbulence, or the like. 
Measurement noise is produced in each stage of the sensing process and 
assembly. The measurement noise is usually a function of the quality of 
the sensing process and assembly. 
Current methods used to measure process variables such as temperature, 
pressure, and flow inherently yield signals which incorporate both process 
noise and measurement noise in their magnitude. Hence, using a process 
signal corrupted by noise is a long-standing problem of process control 
engineers. Measurement noise is normally the less significant component in 
the total amount of noise present in a given signal when compared to the 
process noise component, and the measurement noise can be reduced further 
by corresponding measures such as grounding of the electrical circuitry or 
the like. The following discussion will, therefore, focus on the process 
noise component and presume that the influence of the measurement noise 
component is essentially negligible in the application of the present 
invention. 
In a controlled apparatus and process where a control unit or set of 
control units assists in the operation of a complicated machine such as a 
nuclear power station, automobile, turbine, chemical manufacturing 
process, or the like, various parameters of the process being controlled 
are monitored to determine the status of the process at any point in time. 
To this end, sensors and transducers are used to generate sensor output 
signals which represent the values of various attributes (such as 
temperature, pressure, flow rate, rpm., or vessel level) characteristic of 
the controlled apparatus and process. 
A sensor is a device which is constructed to respond to a given event, 
occurrence, or state and to produce an accordingly predetermined sensor 
output signal representing the aforementioned state, occurrence, or event 
as an analog or digital signal (or, in a more virtual sense, as a numeric 
or Boolean quantity). The term "sensor" can also include, as a further 
component, a device which might be designated as an interactive measuring 
device which is in physical contact with the controlled apparatus and 
process (a transducer), and this general device will be hereinafter 
considered as being incorporated into the sensor. 
Within the context of process control, the sensor output signal generated 
by the sensor then is transferred into a process signal which is the 
representation of the sensor output signal as a value useful in a process 
control decision program wherein a controlled apparatus (such as a 
manufacturing plant, aircraft, ship, or other such apparatus controlled by 
a real-time process control computer) is implemented through use of the 
process control decision program. A number of well-known techniques are 
utilized in the course of the transfer of a sensor output signal to a 
process signal, each being appropriate to the particular signal; some 
common examples of these techniques include analog to digital conversion, 
linearization, measurement via a Wheatstone bridge (as in the case of some 
weigh cells and resistance temperature devices), multiplexing, or receipt 
and interpretation of a serial data message from the sensor if the sensor 
outputs a serial data message as its sensor output signal. The 
implementation of these techniques can be achieved through a combination 
of process computer integral sensor output conditioning software means 
executing, in a preferred embodiment, in the computer processing unit 
which is executing the process control decision program. 
A further consideration of the background of the invention is that the 
detection schema relating to a sensing process and assembly, the 
associated controller, and the process signal are all further associated 
with a process control computer which implements and executes certain of 
the aspects of the present invention within the context of a mode of 
operation generally known as real-time processing. Real-time processing is 
generally defined as a method of processing in which an event causes a 
given reaction within an actual time limit and wherein computer actions 
are specifically controlled within the context of and by external 
conditions and actual times. As an associated clarification in the realm 
of process control, real-time processing relates to the performance of 
associated process control logical, decision, and quantitative operations 
intrinsic to a process control decision program functioning as part of a 
controlled apparatus and process wherein the process control decision 
program is periodically executed with fairly high frequency usually having 
a period of between 20 ms and 2 seconds, although other time periods could 
be also utilized and some operations might be performed on an integer 
multiple of the primary process control decision program execution period 
for purposes related to either tuning, sensitivity, or efficient resource 
utilization. 
In a continuously controlled process, parameters such as temperature, flow, 
voltage, current, rpm., height, or the like, may be monitored to determine 
when the process is out of tolerance. 
Natural phenomena, such as noise, are usually not uniformly distributed. 
Instead, they are more likely to follow a normal distribution also known 
as Gaussian Distribution which has well-known bell-shaped distribution 
properties. Random numbers generated by this distribution tend to cluster 
about the mean or average value of the entire group. As values depart from 
the mean, they occur less frequently. 
This means that, in a statistical context, process signals derived and 
resulting from the sensing process and assembly show a random variation in 
their values. If these process signals consistently show a normal 
distribution within predetermined limits, the controlled apparatus and 
process is considered stable and under control by a controller. 
In the course of operation of the controlled apparatus and process, the 
sensor or the sensor output signal from the sensor can fail, can be 
terminated, or might be distorted due to a disruptive event or situation 
such as a cable break, short-circuit, freezup, or any other kind of 
failure, so that the sensor output signal and its further associated 
process signal is not representative of the status of the process being 
controlled and can have detrimental effects in the process control 
operation. The terms freezup or freezing refer to the unwanted 
introduction of some mechanical, electrical, or fluidic disruptive element 
into the sensor mechanism or sensor signal transmission mechanism which 
results in the sensor output signal or its associated process signal being 
either less dynamically responsive or not dynamically responsive in the 
predetermined manner for which the sensing process and assembly was 
designed to generate the process signal indicating the event, occurrence, 
or state associated with the controlled apparatus and process. 
Examples of phenomena associated with the cause of freezing include short 
circuits, radio-frequency interference, fluidic phase changes (such as 
solidification of liquid tar into solid tar in a tube or pipe connecting 
the sensor to the controlled apparatus and process), polymerization of a 
monomer into a more viscous fluid in a tube or pipe connecting the sensor 
to the controlled apparatus and process, or agglomeration of solid 
particles in a tube or pipe connecting the sensor to the controlled 
apparatus and process. 
It is therefore an object of the invention to provide an economical and 
straightforward method or apparatus for determining the presence of 
freezing or failure phenomena in either the operation of a sensor which is 
generating a sensor output signal or in the associated comprehensive 
sensing process and assembly by examining the process signal derived from 
the sensor output signal in a controlled apparatus and process. 
SUMMARY OF THE INVENTION 
According to the invention, the method for detecting probable freezing or 
failure phenomena in the operation of a sensor which is generating a 
sensor output signal or process signal derived from the sensor output 
signal in a controlled apparatus and process comprises the steps of 
determining in real-time processing mode an estimate of the standard 
deviation of the process signal, comparing the estimate of the standard 
deviation of the process signal with at least one predetermined reference 
value, and producing an unacceptability signal if the comparison results 
in the determination of unacceptable deviation between the compared 
values. 
In a preferred embodiment, this method provides straightforward detection 
of freezing (or failure) phenomena either (1) in the operation of a sensor 
(which is generating a sensor output signal) or (2) in the operation of 
related components (in the overall sensing assembly) which convert the 
sensor output signal into a process signal when the method executes in the 
same real-time computer processor which stores the process signal for use 
by a process control decision program wherein a controlled apparatus (such 
as a manufacturing plant, aircraft, ship, or other such apparatus 
controlled by a real-time process control computer) is implemented through 
the use of the process control decision program. 
Further embodiments of the method according to the invention are described 
in the claims. 
The apparatus for detecting according to the invention is characterized by 
a means for determining in real-time processing mode an estimate of the 
standard deviation of a process signal, a means for comparing the estimate 
of the standard deviation of the process signal with at least one 
predetermined reference value, and a means for producing an 
unacceptability signal if the comparison results in a determination of 
unacceptable deviation between the compared values. 
Further embodiments of the apparatus for detecting according to the 
invention are described in the claims. 
It is obvious that the invention has a very wide field of use and that the 
method and apparatus for detecting the freezing or failure of a sensing 
process and assembly, a sensor associated with the sensing process and 
assembly, the sensor output signal generated by the sensor, or the further 
derived process signal generated by the sensor in a controlled apparatus 
and process has applicability in other fields using real-time data or 
frequently updated data and is not restricted to only those fields in 
which traditional types of sensors or transducers are used. 
It will also be understood that the invention can be applied to a very 
large number of different kinds of sensors or transducers. 
The above and further objects and advantages of the invention may be better 
understood by referring to the following detailed description, which 
should be read in conjunction with the accompanying drawings in which:

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
FIG. 1 shows, in simplified block diagram form, a controlled apparatus and 
process 1 with an associated and integrally connected sensor 2, said 
sensor being further comprised of a transducer 3 and a sensor output 
signal transmitting system 4. The transducer 3 and the sensor output 
signal transmitting system 4 interact to generate a sensor output signal 5 
according to methods which are well known in the art. The sensor output 
signal 5 is transferred to a sensor output signal receiving hardware 6 
through a communication method which could be either based on electrical, 
optical, or radio-frequency means as is generally known in the art. The 
sensor output signal receiving hardware 6 which is attached to a process 
control computer 7 and a sensor signal conditioning software 8 executing 
in the process control computer 7 further interact to generate a process 
signal 9. 
The process signal x.sub.k 9 can then be referenced during the execution of 
a process control decision program 10 executing in the process control 
computer 7. The process control decision program 10 will then generate a 
controller signal 11 in the process control computer 7. Controller signal 
11 further interacts with a controller signal conditioning software 12 
executing in the process control computer 7 and a closely associated 
controller signal sending hardware 13 which is attached to the process 
control computer 7 to generate a controller output signal 14 which is 
transmitted through a communication method (which could be either based on 
electrical, optical, or radio frequency means as is generally known in the 
art) to a controlling device 15 comprised of a controller signal receiving 
system 16 and a closely coupled field control device 17 affecting the 
controlled apparatus and process 1. 
The sequence of occurrences outlined in FIG. 1 occur, in a preferred 
embodiment of the invention, with fairly high frequency usually having a 
period of between 20 milliseconds and 2 seconds, although other time 
periods could also be utilized. The process control computer 7 is the 
operative physical apparatus which dynamically executes the associated 
process control logical, decision, and quantitative operations intrinsic 
to the elements of FIG. 1. 
FIG. 2 shows, in a simplified block diagram form, a case of a commonly 
referenced process dynamics and control block diagram control loop which 
can be virtually associated with the control components of FIG. 1 
comprising a controller 21, a controlled apparatus and process 22 
(corresponding to the controlled apparatus and process 1) and a sensing 
process and assembly 23. 
The sensing process and assembly 23 of FIG. 2 comprises the transducer 3, 
the sensor output signal transmitting system 4, the sensor output signal 
5, the sensor output signal receiving hardware 6, and the sensor signal 
conditioning software 8 of FIG. 1 when interpreted in the process control 
and dynamics context. The controller 21 is logically resident within the 
process control decision program 10 of FIG. 1 and further comprises the 
elements of FIG. 1 which are designated as the controller signal 11, the 
controller signal conditioning software 12, the controller signal sending 
hardware 13, the controller output signal 14, the controller signal 
receiving system 16, and the field control device 17. 
The process control computer 7 of FIG. 1 is associated with the controller 
21, the sensing process and assembly 23, and a combining operator 24, 
shown in FIG. 2, as the operative physical apparatus which dynamically 
executes the associated process control logical, decision, and 
quantitative operations intrinsic to the elements of FIGS. 1, 2, and 3. 
A reference input signal r is supplied to the combining operator 24 which 
can be an adder where either the reference input signal r or the process 
signal x.sub.k from the sensing process and assembly 23 is negated and 
supplied to the combining operator 24. The output from the combining 
operator 24 is supplied as error signal e to the controller 21 which 
produces a manipulated input m to the controlled apparatus and process 22 
as an output from the controller 21. Note that manipulated input m (FIG. 
2) to the controlled apparatus and process 22 is not the controller output 
signal 14 (FIG. 1), but it is the interfacing modification of the 
environment of the controlled apparatus and process 1 caused by the field 
control device 17 (for instance, the alteration of the effective inside 
cross sectional opening in a pipe caused by the partial closing of an 
analog control valve). The controlled apparatus and process 22 can be any 
mechanical or chemical process or system such as a reactor, a container to 
be filled, a thyristor, a motor, or the like. The terms "controlled 
apparatus and process", "controlled environment" or "controlled system" 
should also include situations where a human being might be part of the 
control loop. In this regard, FIG. 1 depicts the preferred fully automatic 
scenario, but should not exclude partially automated approaches. 
The process attribute x to be measured and indicated by the sensing process 
and assembly 23 can be any intrinsic phenomena associated with the 
controlled apparatus and process 22 such as temperature, flow, voltage, 
current, rpm., height, or the like. 
The invention starts from the idea that the process attribute x should have 
a random variation in magnitude over a period of time, and that, 
therefore, the sensing process and assembly 23 should output corresponding 
variations in the magnitude of process signal x.sub.k if sensing process 
and assembly 23 is working properly. As can be understood by one skilled 
in the art of process control, inaccurate correspondence between the 
process attribute x and the process signal x.sub.k in either magnitude or 
dynamic fidelity can diminish the appropriateness of the controller 21 to 
effectively operate the controlled apparatus and process 22 unless the 
controller 21 has a means or mechanism to effectively detect and 
compensate for inaccurate correspondence between the process attribute x 
and the process signal x.sub.k. The present invention is a means and 
mechanism to detect such an inaccurate correspondence, and the 
compensation for the situation is generally understood in the art once the 
status indicating inaccurate correspondence between the process attribute 
x and the process signal x.sub.k is identified. 
The sensing process and assembly 23 outputs the process signal x.sub.k to 
create a signal which indicates an analog value within the context of a 
predetermined measuring range which is limited or bounded by a negative 
full scale (NFS) value and a positive full scale (PFS) value. 
One possible approach which can be used for detecting the amount of 
variation as a function of time in the process signal x.sub.k is to 
determine in real-time processing mode by the process control computer 7 
in FIG. 1 an estimate of the standard deviation .delta..sub.k of the 
process signal x.sub.k and by setting a first predetermined value LL as a 
low limit value of the estimate of the standard deviation of the process 
signal and a second predetermined value Hi as a high limit value of the 
estimate of the standard deviation of the process signal. These various 
determinations are implemented within the controller 21 which is, as 
stated above, logically resident with process control decision program 10, 
which is being executed by the process control computer 7. If this 
estimate of the standard deviation is below the low limit value ii or 
above the high limit value HL, a freezing or failure of the sensor has 
occurred. The determination of an estimate of the standard deviation 
.delta..sub.k will be further described later. 
Now, the detailed operation enabling the determinations of the estimate of 
the standard deviation and of the status of a particular signal at any 
given time is described by referring to FIG. 3 showing in a functional 
diagram (flow chart) the evaluation of the process signal x.sub.k with 
respect to its status regarding freezing or failure using the detection 
schema subpart of the controller 21 which is a critical aspect of the 
present invention. 
The detection schema is used to evaluate the process signal x.sub.k which, 
in the preferred embodiment, is updated in the process control computer 7 
at a sample interval (or period) T.sub.s which can be one second or any 
other time depending on the steadiness of the process, the measuring 
devices, and other circumstances. This sampled process signal x.sub.k, 
with k being the period index of the discrete time in the process control 
computer 7, is one of a set of process signals ultimately supplied to the 
process control computer 7 as a series of time-discrete values 
representing a particular attribute of the controlled apparatus and 
process 1 for use and reference by the process control decision program 
10. At the beginning of the execution of the process control decision 
program 10, following startup of the process control computer 7, the 
period index k is set equal to zero; as a practical matter, k may be 
adjusted or rolled over periodically to accommodate the limitations of 
available registers In the process control computer 7 using methods 
understood in the art. 
The operation of the detection schema subpart of the controller 21 executes 
in the process control computer 7, and it begins with the "START" step 31. 
The period index k is set equal to the integer 0 in step 32 and the 
process signal x.sub.k for the current time period T.sub.s is read in a 
"READ x.sub.k " step 33 by the detection schema subpart of the controller 
21. 
The process control computer 7 next examines, in step 34, the value of the 
process signal x.sub.k with respect to the value associated with zero 
magnitude for the attribute of the controlled apparatus and process 1. If 
the decision is YES, the status of a cable break monitor is evaluated in a 
"LIVE ZERO?" step 35 to establish whether the presence of an open circuit 
or general discontinuity is indicated (indicating failure or NO regarding 
the "Live Zero Test"). If the result of the "LIVE ZERO?" step 35 is NO, a 
first message in step 36 is output indicating a problem in the integrity 
of the physical components of the sensing process and assembly 23. In this 
regard or in regards to further use of the term, "message being output" 
can be any message, status indicator value output to the database of any 
controller 21 executing in the process control computer 7, alarm, output 
on the printer, red lamp, or the like being output from the detection 
schema to the database referenced by any controller 21 executing in the 
process control computer 7; furthermore, a human being might also receive 
an indication of the message through an appropriate interactive device. 
Following the generation of the output message in step 36, the detection 
schema then proceeds to the end of schema iteration step 37. 
If an open circuit or general discontinuity is not indicated (indicating 
acceptability or YES regarding the "LIVE ZERO?" step 35), further 
evaluations of plausibility are conducted as indicated in a 
"PLAUSIBILITY?" step 38. Examples of these types of evaluations would 
include cross reference to data from other equipment, or correlation of 
the zero magnitude indication to momentum, heat, or mass transfer 
balances. 
A specific scenario illustrative of cross reference to other equipment 
would be comparison of the process signal x.sub.k values from two 
identical sensors 2 dedicated to measuring the same attribute of the 
controlled apparatus and process 1. A specific scenario illustrative of 
the correlation of the zero magnitude indication to momentum, heat, or 
mass transfer balances would be that of a flow meter connected to a stream 
which is being transferred by a pump. If the flow meter indicates a value 
of zero flow in the stream and the amperage from the motor operating the 
pump indicates that the pump is operating under an elevated load, then the 
"PLAUSIBILITY?" step 38 should indicate a value of NO even though the 
"LIVE ZERO?" step 35 has a value of YES since the momentum transfer 
indicated from the amperage reading would indicate the presence of flow 
even though the flow sensor indicates a flow of zero. If the result of the 
"PLAUSIBILITY?" step 38 evaluation is NO, a second message in step 39 is 
output indicating a problem in the plausibility of the sensing process and 
assembly 23. The detection schema then proceeds to the end of schema 
iteration step 37. 
If the value of the process signal x.sub.k is not zero or the result of the 
"PLAUSIBILITY?" step test(s) 38 is YES, the detection schema executing in 
the process control computer 7 next examines in step 40 whether the 
positive full scale PFS magnitude of the value for the process signal 
x.sub.k has been reached or surpassed by the process signal. This could 
mean that a short-circuit has occurred. In step 41 the detection schema 
examines whether the magnitude of the value for the process signal x.sub.k 
has reached or fallen below the negative full scale NFS. This could mean 
that a cable break, open circuit, or general discontinuity has occurred. 
If the determination is YES in either case, an appropriate PFS message in 
step 42 or NFS message in step 42 is output. In either case, the detection 
schema then proceeds to the end of schema iteration step 37. 
If the PFS and NFS tests both generate a result of NO, the period index k 
is increased by the integer value of one in a "k=k+1" step 44. If period 
index k is equal to one (step 45) after the operation of the step 44, the 
prior sampling period process signal x.sub.k-1 from the prior sampling 
period is set equal to process signal x.sub.k (step 46) and, in step 47, 
the deviation d.sub.k of the process signal x.sub.k from the prior 
sampling period process signal x.sub.k-1 is set to zero; and, in step 48, 
the estimate of the standard deviation .delta..sub.k is set to zero (since 
a value of k being the integer one indicates that the detection schema is 
passing through its first iteration). A recency weighting variable N is 
also set to zero in step 49; this recency weighting variable N will be 
further described at a later place in this discussion of FIG. 3. The 
detection schema then proceeds to the end of schema iteration step 37. 
If period index k is not equal to one after the operation of the step 44, 
the deviation d.sub.k of the process signal x.sub.k from the process 
signal x.sub.k-1 from the prior sampling period is calculated according to 
the following formula which is executed in step 50 of the detection 
schema: 
EQU d.sub.k =x.sub.k -x.sub.k-1 (1) 
The recency weighting variable N will now be discussed in preparation for 
the continued discussion of the next part of the detection schema. 
In the preferred embodiment shown in FIG. 3, a critical feature of the 
invention is the ongoing real-time update of the estimate of the standard 
deviation .delta..sub.k. As is well known in the art of statistics, an 
estimate of standard deviation is usually formally based upon a set of 
data values where the number of data values in the set is a discrete 
integer which is used as a part of the calculation of the estimate of the 
statistical standard deviation. In the present invention, an in real-time 
processing mode update of the estimate of the standard deviation 
.delta..sub.k is done to enable sensitivity of the method of the invention 
to recent values of the process signal x.sub.k. However, the recency 
attribute in the estimate of the standard deviation .delta..sub.k is only 
one part of the enablement of sensitivity in the operation of the present 
invention; a second component in the feature of sensitivity is enabled by 
implicitly limiting the impact of the more senescent set of process signal 
x.sub.k values which are effecting the change to the estimate of the 
standard deviation .delta..sub.k and strongly favoring the most recent 
data values of process signal x.sub.k. The reason for this attribute in 
the method derives from and is related to the fundamental negative 
inertial effect on response leverage which accompanies an ever-increasing 
set size of data incorporated into a process dynamics integral, a 
phenomenon which is compensated for in classical control models by the use 
of the proportional component in P (proportional), PI 
(proportional-integral), and PID (proportional-integral-derivative) 
controller designs and by limiting the maximum of the controller integral 
in the PI and PID control models. 
In the context of the present invention, the estimate of the standard 
deviation .delta..sub.k is an estimate of standard deviation which 
strongly "weights" the more recent set of discrete values of process 
signal x.sub.k into the change rate of the estimate of the standard 
deviation .delta..sub.k instead of a classical equal weighting of all 
prior discrete values of process signal x.sub.k into the estimate of 
standard deviation for the set of all values of process signal x.sub.k 
which have been read by the detection schema; in this way, the estimate of 
the standard deviation is usefully sensitive because it incorporates the 
most recent value of process signal x.sub.k while further implicitly 
incorporating the additional limitation of the degree of acceptable 
recency of the rest of the implicit set of collected recent values of 
process signal x.sub.k. The recency weighting variable N in the detection 
schema is used to create this effect in the method. 
In the set of steps designated as 51 in FIG. 3, the value of N is first 
checked with respect to a fixed value of L in a "N&lt;L?" step 52. (The size 
of L is specific to the particular process signal x.sub.k being evaluated 
and is selected to be appropriate for the process dynamics time constants 
related to each unique process signal x.sub.k). If the result of the 
"N&lt;L?" step 52 is YES, then the discrete value of N is permitted to 
increase in step 53 by a value of integer one for the particular iteration 
of the detection schema being implemented. However, if the result is NO, 
then the value of N will be limited to L in all future iterations of the 
detection schema. After determining the value of N, the absolute value 
deviation of the deviation d.sub.k (of the process signal x.sub.k from the 
prior sampling period process signal x.sub.k-1) is established in step 54. 
The next step in the execution of the detection schema is the calculation 
of the estimate of the standard deviation .delta..sub.k of the process 
signal x.sub.k and is determined with the context of the invention in step 
55 by 
##EQU1## 
where: 
the estimate of the standard deviation .delta..sub.k for a period index k 
is calculated based upon the detection schema using the recency weighting 
variable N, the estimate of the standard deviation .delta..sub.k-1 from 
the iteration of the prior period index k-1, and the absolute value 
deviation y.sub.k which was earlier derived from the deviation d.sub.k (of 
the process signal x.sub.k from the prior sampling period process signal 
x.sub.k-1) as previously described. This equation is derived from the well 
known statistical relationship: estimated standard deviation mean range 
divided by d.sub.2, where d.sub.2 is equal to 1.128 for a subgroup size of 
2. The mean range is obtained by an infinite exponentially weighted mean 
series. The choice of the weight depends on the user need in balancing the 
sensitivity and stability of the estimated value. 
Thereafter, in step 56 the process signal x.sub.k-1 from the prior sampling 
period is set equal to x.sub.k and in step 57 the estimate of the standard 
deviation .delta..sub.k-1 from the prior sampling period is set equal to 
the estimate of the standard deviation .delta..sub.k in anticipation of 
the coming iteration for the next period index k+1. 
In order to detect the freezing or failure in the sensing process and 
assembly 23, the current estimate of the standard deviation .delta..sub.k 
of the process signal x.sub.k is evaluated for being less than the low 
limit value LL in step 58, and the current estimate of the standard 
deviation .delta..sub.k of the process signal x.sub.k is evaluated for 
being greater than the high limit value in step 59 if the low limit value 
evaluation results in a NO determination from step 58. 
If the estimate of the standard deviation .delta..sub.k is less than the 
low limit value LL and results in a YES from step 58, an evaluation of the 
ability to switch over to a second or backup sensor is performed in step 
60. If a YES result is determined from step 59, the controller 21 will be 
referenced (in a manner not described but obvious to those skilled in the 
art) to the alternate sensor by the output of a fifth message in step 61 
indicating a problem in the integrity of the physical components of the 
sensing process and assembly 23 due to low limit freezing. The detection 
schema then proceeds to the end of schema iteration step 37. 
If an alternate sensor is not available and the result from step 60 is NO, 
the controller 21 will be referenced (in a manner not described but 
obvious to those skilled in the art) through the output of a sixth message 
in step 62 to the need to take corrective action given the existence of 
low limit freezing or failure of the sensing process and assembly 23. The 
detection schema proceeds to the end of schema iteration step 37. 
If the estimate of the standard deviation .delta..sub.k is greater than the 
high limit value HL and results in a YES from step 59, an evaluation of 
the ability to switch over to a second or backup sensor is performed in 
step 63. If a YES result is determined from step 63, the controller 21 
will be referenced (in a manner not described but obvious to those skilled 
in the art) to the alternate sensor by the output of a seventh message in 
step 64 indicating a problem in the integrity of the physical components 
of the sensing process and assembly 23 due to high level deviation 
failure. The detection schema then proceeds to the end of schema iteration 
step 37. 
If an alternate sensor is not available and the result from step 63 is NO, 
the controller 21 will be referenced (in a manner not described but 
obvious to those skilled in the art) through the output of an eighth 
message in step 65 regarding the need to take corrective action given the 
existence of high level deviation failure of the sensing process and 
assembly 23. The detection schema then proceeds to the end of schema 
iteration step 37. 
If the estimate of the standard deviation .delta..sub.k is less than the 
high limit value HL and results in a NO from step 59, the detection schema 
proceeds to the end of schema iteration step 37. 
In the end of schema iteration step 37, the detection schema implicitly or 
explicitly transfers execution of the statements to another subsection of 
either the process control decision program 10 or the controller 21 
(either of which could alternatively either immediately process another 
process signal z.sub.k or continue to the execution of other process 
control decision program 10 statements) while virtually and effectively 
also transferring to a wait and hold step 66 in anticipation of either an 
event or time indicator which will initiate the "READ x.sub.k " step 33 
for the next iteration of the detection schema to detect the freezing or 
failure status of the sensing process and assembly 23 for the next process 
signal x.sub.k where the iteration just described as being appropriate to 
period index k will become appropriate to period index k-1. 
In an alternative embodiment, the operations performed in steps 52 and 53 
can be replaced with the use of a time delay wherein a set of time periods 
k equal to recency weighting variable N are permitted to pass following 
the "Start" step 31 prior to use of any of the messages 36, 39, 42, 43, 
61, 62, 64 or 65 from the detection schema in the process control decision 
program 10, and the value of N is set in step 49 to a non-zero value 
(which will not vary during the course of the use of the schema) 
reflecting the desired sensitivity of the method to freezing phenomena. In 
this case, step 50 would exit to step 54.