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FEATURES OF THE FORMATION OF THE PROCESS OF CLASSIFYING THE CONDITION OF A TECHNICAL FACILITY BASED ON THE ANALYSIS OF POINTS IN THE TIME SERIES OF THE PARAMETER
S.I. Klevtsov47-572025-10-01Abstract ▼Assessment of the operability of a technical facility in real time is important for the stable and trouble-free operation of the facility during its operation. Previously, a classification model for the rate of parameter change was proposed based on specialized point cloud processing of a time series segment without trend extraction. However, some proposals, for example, related to the non-inclusion of some points of the series in the model construction procedure, were not sufficiently justified and are an unobvious attempt to get rid of abnormal values of the time series. Some stages of the model implementation, for example, building an ellipse on a transformed point cloud, require a detailed representation, which is important for further model training and classification. In the article, as part of the preliminary data preparation, a procedure is proposed for detecting and screening out abnormal values of the time series of a parameter based on a modification of the Irwin method. In addition, an updated scheme for evaluating the values of the criterion in the classification model for the condition of a technical facility parameter is presented. The ellipse compression ratio is used as the evaluation criterion, which is based on a cloud of scatter plot points cut out by a sliding time window from the time series of the parameter. An iterative ellipse construction procedure has been developed for this purpose. The new procedure provides a more informed and accurate assessment of the criterion. Thus, a modified model has been built that will allow real-time assessment of the occurrence of an emergency situation at an early stage of its development.
The evaluation procedure can be implemented as part of the hardware and software of the monitoring system of a technical facility -
SELECTING FEATURES OF THE MODEL TRANSFORMATION CHARACTERISTICS FOR AN INTELLIGENT PHYSICAL QUANTITY SENSOR
S. I. Klevtsov2021-11-14Abstract ▼The paper discusses the issues of choosing the type and parameters of the model of the transformation
characteristic of an intelligent sensor of physical quantities using the example of a pressure
sensor. The transformation characteristic of an intelligent sensor is a mathematical, algorithmic
and software for calculating a physical quantity based on electrical signals that come from the measuring
channels of the sensor. The model of the conversion characteristic should be adapted to the
configuration of the conversion function of the sensor's sensitive element and the behavior of this
function under the influence of external destabilizing factors. The paper considers various models of
the conversion characteristics, identifies the features of their application, advantages and disadvantages,
attainable levels of approximation error of the real characteristic, which affect the final
measurement accuracy of the smart sensor. Smart sensors are used for measuring physical quantities
in various technical systems and the requirements for measurement accuracy in real-life tasks are
different. The measurement accuracy is largely determined by the degree of approximation of the real
characteristics of the sensor by its mathematical model. The more complex the model, the more difficult
it is to implement in the sensor, and the higher the measurement cost. Therefore, it is important
to control the conversion characteristic approximation error in order to use the sensor efficiently. To
control the approximation error of the transformation characteristic of an intelligent pressure sensor,
it is proposed to use the method of multi-segment spatial approximation, and use models of linear or
nonlinear spatial elements as segments. The basic mathematical expressions, the error control
scheme are determined. The results of modeling are presented, which show the possibility and advantages
of using the method for the formation of spatial models of the transformation characteristics,
which are adaptive to changes in the real transformation function of the sensor, take into account
the influence of external factors on the measurement results. In addition, the method allows you
to modify the current spatial approximation model by changing the types of local spatial elements
and, thus, to control the measurement error -
SELECTION OF THE SENSOR CONVERSION CHARACTERISTIC MODEL FOR CONTROLLING THE ERROR IN THE MEASUREMENT OF PHYSICAL QUANTITIES
S.I. Klevtsov2022-08-09Abstract ▼On the example of a pressure sensor, the problem of selecting a model and parameters of
the conversion function of a microprocessor sensor is considered. The conversion function is
based on a mathematical model that associates the electrical signal coming from the sensor's
measuring transducer with the value of a physical quantity. The model of the conversion function
of a microprocessor sensor must repeat the real spatial dependence of the electrical signal on the
measured value and take into account the influence of external factors, such as temperature. Microprocessor
sensors are used to measure the parameters of an object with a given accuracy. The
main contribution to the measurement error is made by the inaccuracy of the approximation of the
real transformation function by its model. The need to achieve the optimal level of parameter
measurement error in the system, taking into account the complexity and cost of measurements,
requires the control of the sensor error. For this purpose, various models and methods of approximation
are presented. For efficient error control, a method of multi-segment spatial approximation
based on models of linear or non-linear spatial elements is proposed. The error control procedure
is formulated. The procedure for using the model of multi-segment spatial approximation
of the transformation characteristic for pressure calculations taking into account the influence of
temperature is based on the combined use of linear and non-linear spatial elements within the
same model. The segment type selection procedure should begin with an assessment of the possibility
of using a linear spatial element first, and if it is impossible to meet the accuracy requirements,
an analysis of the use of a non-linear element. The method allows you to change the types
and configuration of spatial elements and in this way influence the measurement error. The advantages
of this approach are confirmed by the simulation results. -
CLASSIFICATION OF THE DEGREE OF PARAMETER CHANGE IN REAL TIME BASED ON TIME SERIES POINT CLOUD ANALYSIS
S.I. Klevtsov2024-10-08Abstract ▼The task of building a model for assessing the performance of a technical object has many applications
in the field of controlling various hazardous situations. The need for advanced monitoring of the
technical object state to prevent and control the course of abnormal situations in order to eliminate them
with minimal consequences makes the statement and fulfillment of this task relevant and timely. To perform
the assessment of the state of the technical object it is advisable to use simple models that allow to
obtain the result in real time without significant load on the microcontroller control system. The paper
considers the construction of a model for classifying the dynamics of change in the parameter of a technical
object, which will allow you to predict the change in its state in the process of assessing the degree
of serviceability of the object. The data reflecting the change of parameters in real time and presented in
the form of time series of parameter values are used. The change of the object parameter in time is fixed
with the help of a time window, which moves along the time series, cutting out of the set of initial data a
subset with an unchanged number of time samples. To classify the dynamics of parameter variation, it is proposed to use a representation of the time window points in the form of a Poincaré plot, which is actually
a special type of repetition plot or a type of scatter plot. The ellipse compression factor (ellipticity) is
used as a criterion, which encompasses the point cloud formed during the construction of the scatter diagram,
for the time series of the technical parameter. A methodology for training and using the model,
including the formation of classes of states of the dynamics of the object parameter and the calculation of
criteria, is developed. The model has been tested. The model provides the realization of procedures for
real-time detection of the possibility of an abnormal situation at an early stage of its development with the
help of a microprocessor module located at the lower level of the object monitoring system. -
USING THE NORMALIZED RANGE METHOD TO ASSESS THE IDENTITY OF TEST CYCLES
S.I. Klevtsov2024-01-05Abstract ▼The measurement accuracy of a microprocessor-based physical measurement sensor is
measured to the extent determined by its conversion characteristic, which is constructed from data
obtained from various calibration tests. Characteristics of the sensor converter, on which the
measurement accuracy depends, within a certain degree of approximation of the characteristics of
the sensor converter. Sensor calibration tests are carried out in accordance with the test procedure.
In the process of fulfilling obligations, errors arise due to exceptions of individual loops
made on each other. Therefore, small deviations from the conduction scheme can lead to a decrease
in the quality of the conversion characteristics and a decrease in the metrological characteristics
of the sensor. It is important that the results of several test cycles under constant environmental
conditions are independent of each other. The article presents a method for determiningthe quality of the results of calibration tests of a microprocessor pressure sensor. The method
allows you to evaluate previous test cycles for violation of the conditions of their conduct. An artificial
time series constructed using test data is analyzed. For construction, a specialized procedure
was implemented for connecting individual cycles into a single structure, similar to a time series.
A separate time series was constructed for each constant temperature value. Since the resulting
time series is a linear function, its Hurst exponent should be close to one. In this case, the series is
trend-resistant, and experimental cycles check independence and calculate a single linear trend
with minor deviations from it. If during the test any conditions for their conduct were violated, for
example, the conditions for transition from one temperature regime to a procedure, then based on
the results of the current test regime, the temperature conditions of the cycle regime will be observed.
To determine such scales, a procedure is proposed for comparing the Hurst exponent of a
time series, which presents data from an unreliable test cycle, with a range of acceptable results.
If the Hurst exponent meets the established limits, the test results can be used to construct a highquality
calibration characteristic. Otherwise, the results of the analyzed cycle describe the test
cycle conditions and recommend repeated test cycles. -
THRESHOLD ASSESSMENT OF THE STATE OF A TECHNICAL OBJECT BASED ON SEGMENTATION AND IDENTIFICATION OF THE CONTROLLED PARAMETER MODEL
S.I. Klevtsov2023-08-14Abstract ▼To fix the jumps in the average value, a detection method based on the segmentation of the signal
under study based on the formation of cumulative sums using the Page-Hinckley criterion is proposed.
The use of the Page–Hinckley likelihood criterion makes it possible to detect abrupt changes
in the average value of the controlled object parameter in real time under noisy conditions. When
using the method, it is assumed that the signal is described by a time series of values of the signal
under study. From this series, it is possible to single out separate successive sections, which can be
considered as some signal models limited in time. The method is based on the use of criterion statistics,
on the basis of which two or three models estimated from different parts of the signal are compared,
which makes it possible to detect abrupt changes in the model parameters. The method assumes
that a piecewise constant signal with additive noise is considered. At arbitrary moments of
time, there are jumps in the average value of this signal. Jumps in the average value of the signal can
be different in sign (fixed on different sides of the time axis) and significantly exceed the original
value in absolute value. The average value of the signal is a constant value close to zero. But a situation
is possible when a repeated jump will be made from a level different from the average value
close to zero, both in the direction of increasing and decreasing the average value of the signal and
changing the signal polarity (the sign of the signal values). A criterion has been chosen that allows
minimizing the delay time in detecting a jump in the average value of the recorded signal with a minimum
of false alarms. In this case, segmentation of the signal under study is used based on the formation
of cumulative sums using the Page-Hinckley criterion. The use of the Page–Hinckley likelihood
criterion makes it possible to detect abrupt changes in the average value of the controlled object
parameter in real time under noisy conditions. -
DETERMINING THE NATURE OF PARAMETER CHANGES BASED ON THE ANALYSIS OF DYNAMICS RELATIVE TO THE SHAPE OF ITS VALUES SET IN REAL TIME
S. I. Klevtsov2020-10-11Abstract ▼One of the important tasks of monitoring technical objects is the prevention of emergency
situations. This task is associated with the implementation of a reliable and adequate assessment
of the health of the object. The assessment of the object’s health is based on an analysis of the
behavior of its controlled parameters in real time. Only then it will be relevant. A method for determining
the nature of a parameter change based on an analysis of a sequence of special spatial
graphical forms called Poincare graphs is proposed. The selected parameter should largely determine
the operability of the controlled object. Charts are formed on the basis of the time series
of the controlled parameter. A time window is selected that cuts the specified number of parameter
values. A graph is plotted for each step of moving the window along the time series of the parameter.
The transformation of the form of a given type is analyzed, which is superimposed on the totality of
parameter values presented in the form of a graph. By changing the form parameters, a conclusion is
drawn on the nature of the parameter changes. The paper shows the possibility of using Poincare
graphs to track changes in the state of a technical object in real time. This takes into account the
peculiarities of information retrieval from sensors. The assessment is implemented using a microprocessor
module included in the monitoring system. The structure of a generalized one-factor model is
also proposed, which tracks the change in the state of an object based on an analysis of Poincare
graphs. The option of assessing the state of the object by comparing the characteristics of the graph
with the criteria is given. The criteria are obtained after preliminary processing of a large array of
data on the behavior of the controlled parameter. Each criterion value is associated with an expert
assessment that determines the state of the object. The assessment allows you to determine the degree
of operability of the facility and implement the necessary actions in case of danger.
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