Identification Of Technical Condition Research Articles

Method of Non-Invasive determination of wheel rim technical condition using vibration measurement and artificial neural network

Some vehicle damages can be related to the wheels' functional state, defined by rim and tire conditions. The technical condition of the rim is not simple to identify as it is related to the state of material structure. So far, little information has been presented about measurement systems and methods dedicated to rim technical condition identification that are acceptable by car service stations. Thus, the new measurement system and method are proposed in this paper. The identification process uses forced vibration spectra measurement and data processing with neural network use. For this purpose, the existing wheel balancer has been integrated with the computer coupled to a shaft actuator, vibration exciter, and accelerometer head. The developed software provides automation of measurement procedure and results classification, while the algorithm of data processing enables the proper detection of the three states of rim: new, used, and fit for further use, and not usable.

Method of identification of technical condition of equipped train equipment

Due to the fact that the equipment of modern electric trains is functionally and technologically complicated, the relevance of creating airborne systems for predictive monitoring of the technical condition of trains to identify their actual and predicted technical condition is increasing. At present, it has not been possible to build automatic on-board systems for predictive monitoring of the technical condition of trains. One of the possible solutions to this problem can be considered the creation of on-board systems, the identification of the technical condition of equipment in which is carried out using neural network technologies. The article proposes a methodology for identifying the technical condition of electric train equipment using artificial neural network technologies, which allows real-time detection of the occurrence and development of malfunctions of electric train equipment with the display of information on the display in the driver’s cab. Taking into account the specifics of the problem being solved, the choice of a multilayer architecture of a direct distribution neural network is justified. All layers of the neural network are completely interconnected, while the number of neurons of the input and output layers of the network is determined, equal to the number of controlled parameters of the technical condition of the electric train and the number of its possible technical conditions, respectively. As a function of activation of network neurons, a logistic function was selected. A heuristic approach is used to train an artificial neural network.

The Use of Acoustic Emission Elastic Waves for Diagnosing High Pressure Mud Pumps Used on Drilling Rigs

Although mud pumps are vital components of a drilling rig, their failures are frequent. The identification of technical condition of these high-pressure piston pumps is difficult. There are no reliable criteria for the assessment of mud pump condition. In this paper, faults of the pump valve module are identified by means of acoustic emission (AE) signals. The characteristics of these signals are extracted by wavelet packet signal processing. This method has been verified by experiments conducted on a NOV (National Oilwell Varco) -made triplex 14-P-220 mud pump (mounted in the drillship). The results show that the wavelet packet signal processing method can effectively extract the frequency band energy eigenvalues of the signals. Besides, some operational problems associated with high pressure piston mud pumps are presented. A non-invasive method for diagnosing the technical condition of such pumps is being developed at the Maritime University of Szczecin.

Identyfikacja stanu technicznego konstrukcji wsporczej napowietrznej linii elektroenergetycznej

Technical diagnostics of the overhead power line supporting structures is very important from the point of view of reliability and the national energy security. The paper presents a method of identification of technical condition of power line supporting structures. The technique is based on testing the correlation between the change of stress in the system and the change of modal parameters caused by damage. Presented approach is comprehensive and accounts for a number of problems related to the diagnostics of large-size objects of complex geometry. The basic advantage of the developed identification algorithm of the overhead power line supporting structures technical condition is low sensitivity to measurement errors, which is very important in the context of exploitational diagnostics

Vibration‐Based Damage Identification and Condition Monitoring of Metro Trains: Warsaw Metro Case Study

The work concerns the monitoring of the technical condition of metro trains. For this purpose, a vibration monitoring system installed in buildings in the vicinity of the underground is used. Based on measured vibrations buildings, the identification of technical condition in metro trains is conducted. The results of measurements from the system are also used to assess the impact of vibrations on people staying in buildings through the WODL ratio. Exceeding the assumed levels of the WODL ratio is the basis for repairing trains by turning wheels. Statistical analysis of the data was implemented and trends for particular time periods of measurements were shown. With the entered monitoring system and the introduced WODL index thresholds, it is possible to better plan the costs associated with the repair and replacement of wheels of rolling stock, where there are large values of radial runout. According to the introduced monitoring procedure, the number of complaints from residents decreased and the comfort of their lives improved.

Methodology and Results of Analytical Identification of Technical Condition of Technogenic Facilities

The article covers the major aspects of the pressing issue of technical condition of technogenic facilities (TGF). Theoretical substantiation and presentation of three-tier methodology of analytical identification has been provided, which unlike the known approaches is based on aggregated models. Using the diagnostic data gathered for power-plant boiler we obtained numerical aggregated, correlative, analytical models of its technical condition (TC) and assessed its operating performance with account taken of analytical identification.

Identification of Aircraft Gas Turbine Engines Temperature Condition

Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made. Keywords—Identification of a technical condition, aviation gas turbine engine, fuzzy logic and neural networks.

Complex Condition Monitoring System of Aircraft Gas Turbine Engine

Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients’ changes are analysed. Researches of skewness and kurtosis coefficients values’ changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes’ dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values’ changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made. Keywords—aviation gas turbine engine, technical condition, fuzzy logic, neural networks, fuzzy statistics

Diagnosing technical condition of gas turbine engines by building a binary tree

In the paper the problem of binary trees using for identification of aircraft gas turbine engines technical condition is considered. The results of the method using for aircraft gas turbine engines technical condition identification are given.