A New Method of Early Real-Time Fault Diagnosis for Technical Process

2016 International Siberian Conference on Control and Communications (SIBCON)

2016

The approaches developed and generalized scheme of identification of the technical state of the object based on the analysis of several parameters. Identification is based on the introduction of the median line of the system, forming a local zone states. The scheme forms a platform for the development of specific models and algorithms focused on the identification of technical objects based on data from sensors of physical quantities. To identify the possible states of a model prediction of a physical quantity changes in real time, based on the use of the linear adaptive filter. The filter is based on the model of autoregression. A scheme of performance prediction, which is to implement the procedure, consisting of three stages. Investigations using an adaptive time series to predict the rapidly changing physical quantity based on the sensor data. It is shown that for the identification of the state is necessary to develop models and algorithms that determine the proximity of the integrated current values of the parameters to their median values. It is necessary to take into account the different states of configuration and rank configuration of the integrated degree of intimacy.

“…A gradient with a negative sign indicates the shortest direction to achieve the minimum surface (7).…”

Section: Determination Of In Each Step New Corrected Motion Vector Ofmentioning

confidence: 99%

“…Data can be presented in different ways, but the most used method is that a set of parameters is represented as a vector [7,8]. The components of the vector are the parameter values recorded by the sensors.…”

Section: Introductionmentioning

confidence: 99%

Identification of the state of technical objects based on analyzing a limited set of parameters

2016 International Siberian Conference on Control and Communications (SIBCON)

2016

Application of the Hurst index to evaluate the testing of information gathering system components

2019

ITM Web Conf.

The performance of the technical object is determined based on the evaluation of its parameters. Sensors of physical quantities are used to collect data on the values of the parameters of the controlled object. The performance evaluation of an object depends on the accuracy of the parameter measurement. The measurement accuracy is determined by the sensor conversion characteristic. If the sensor calibration tests are performed correctly, the conversion characteristic will accurately reflect the relationship between the measured parameter and the output electrical signal. A method for assessing the quality of the conversion characteristics of the microprocessor sensor, which is based on the use of Hurst index. The sensor of slowly changing physical quantities is considered. Based on the results of several test cycles obtained at a fixed ambient temperature, a series similar to the time series is formed. The initial series is subjected to additional processing before evaluation. The Hurst exponent is determined for the obtained series. The value of the Hurst index determines the quality of the test results. The possibility of using fractality index to assess the quality of tests is also considered.

Influence of characteristics of time series on short-term forecasting error parameter changes in real time

2018

J. Phys.: Conf. Ser.

Abstract. The impact of physical factors, such as temperature and others, leads to a change in the parameters of the technical object. Monitoring the change of parameters is necessary to prevent a dangerous situation. The control is carried out in real time. To predict the change in the parameter, a time series is used in this paper. Forecasting allows one to determine the possibility of a dangerous change in a parameter before the moment when this change occurs. The control system in this case has more time to prevent a dangerous situation. A simple time series was chosen. In this case, the algorithm is simple. The algorithm is executed in the microprocessor module in the background. The efficiency of using the time series is affected by its characteristics, which must be adjusted. In the work, the influence of these characteristics on the error of prediction of the controlled parameter was studied. This takes into account the behavior of the parameter. The values of the forecast lag are determined. The results of the research, in the case of their use, will improve the efficiency of monitoring the technical object during its operation. IntroductionThe need for the advanced condition monitoring of a technical object to prevent and control the occurrence of emergency situations in order to eliminate them with minimal impact makes the formulation and implementation of the problem of determining the predicted values of the parameters, as well as the projected assessment of the status of the individual parameters of a technical object or a set of real-time, relevant and timely [1][2][3]. Modern development of microprocessor technology allows one to realize this task using microprocessor modules in information systems engineering of complex objects such as active safety systems and vehicle inertial navigation [4]. Estimates implemented in microprocessor modules provide a high dynamic response to the situation. To perform predictive assessment of the technical parameters of the object by means of the microprocessor module, it is advisable to use simple models to get results in real time without a significant load on the microcontroller module. Of interest are the models, which are based on methods of analysis and forecasting of time series [5][6][7]. Numerous methods have been developed on the basis of time-series forecasting models [8][9][10][11][12]. However, these models are quite complex to use microcontroller in the background.This article describes the model and values of the prediction algorithm for rapidly varying physical quantity based on the use of multiple exponential, smoothing its time series [5,6]. Efficiency of the model and the algorithm is assessed based on the vehicle acceleration vector of projections of the change data values measured by the three-axial accelerometer in actual driving conditions.