A System for Measuring the Active Power of the Asynchronous Motor of a Lathe Electric Drive

Kuzovkin, V. A.; Филатов, В. В.; Porvatov, A. N.; Porvatova, A. N.

doi:10.1007/s11018-014-0430-3

Meas Tech

2014

DOI: 10.1007/s11018-014-0430-3

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A System for Measuring the Active Power of the Asynchronous Motor of a Lathe Electric Drive

V. A. Kuzovkin

В. В. Филатов

A. N. Porvatov

et al.

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Cited by 3 publications

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“…The uncertainty in the estimate of the desired factor may increase with the number of vertices of the polyhedron defining the uncertainty region. Therefore, before connecting the new (n + 1)st observation channel to account for an additional perturbing factor x n+1 , it is necessary to compare the uncertainty U n+1 (x 1 ) to the uncertainty obtained when working with n channels and calculated according to the formula, analogous to (3): (9) where u 1 (x n+1 ) is the additional uncertainty estimate for x 1 due to variations in factor x n+1 ; U n (x 1 ) is the uncertainty of the estimate for x 1 when using n observation channels with fixed factor x n+1 .…”

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confidence: 99%

Estimation of Uncertainty in Solving Multi-Parameter Diagnostic Problems

Kozochkin

Porvatov

2015

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We present a methodology of performing a preliminary analysis when introducing multi-parametric diagnostics and examples of its application. We clarify the sources of error in determining the current value of a diagnostic parameter.An important task of diagnostic systems (DS) integrated with automated process equipment is continuous monitoring of the state of cutting tools [1][2][3][4][5]. The basis of all DS is the dependence of the parameters of the controlled signals on the state of the cutting edges. Data for DS comes from functional parameters (power, vibration, temperature, etc.) of the cutting process, which depend not only on the condition of the cutting edges, but also on other changing conditions [4, 6-10]. In the initial preparation, the values of the controlled parameters in all phases of processing are stored in the DS (this is a preliminary training of the DS), but it does not fully solve the problem, because some of the factors are random [10][11][12]. Such factors include random variation in acceptance and hardness of the workpiece surface, and also the shape of the wear pad of the cutting tool, which cannot be taken into account in the data characterizing the amount of wear, for example, on the back side. Variable hardness occurs even within a single workpiece and is determined by the prior technology of manufacture.Therefore, in addition to the desired factor (tool status), during the cutting process there may occur a number of perturbing random factors, which affect the value of the controlled parameters that can trigger a false alarm or acceptance of defectives by the DS. Since there can be many controlled parameters, the problem is resolved by forming a system of equations that is adequate for the number of variable factors, i.e., transitioning from one-parameter to multi-parameter diagnostics [13][14][15][16]. However, such a transition does not improve the situation for assessment of tool wear. For simplicity, consider the example of two variables.Suppose there are two linearly independent functions of two unknown arguments. Theoretically, when functions (diagnostic parameters) take specific values, the unknown arguments (perturbing factors, state parameters) can be found. Likewise a higher order system of equations can be formed with multiple arguments. The difficulty is that in most cases of implementing diagnostic procedures, the connection between diagnostic parameters and perturbing factors is not functional, but random. In this case, changes in the perturbing factors (wear, hardness, etc.) which lead to changes in indirect (diagnostic) parameters are not deterministic, as in functional connections, but random, reflected, for example, by their mathematical expectations. The knowledge only of the mathematical expectations contradicts the metrological requirements to indicate the uncertainty of the estimates obtained in the form of confidence intervals, which contain the true value with a desired probability, for example 95%.

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Estimation of Uncertainty in Solving Multi-Parameter Diagnostic Problems

Kozochkin

Porvatov

2015

Meas Tech

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“…Questions of diagnostics of the most important components of the technological equipment responsible for the machining accuracy also arise. Damage to these components during operation can lead to hazardous situations or breakdown of the technological cycle [9][10][11].The development of instrumentation and computational techniques in recent years has enabled partial monitoring and diagnostics by means of systems for monitoring machines and equipment based on information technology [5].Most adaptive control and monitoring systems on the market today react to a change in the cutting conditions through electromagnetic signals from the force part of an electrical drive and automatically adjust the feed to the maximum allowable level for each operation [12]. When necessary, these systems stop the machine because of overload or detected tool failure.…”

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confidence: 99%

“…Most adaptive control and monitoring systems on the market today react to a change in the cutting conditions through electromagnetic signals from the force part of an electrical drive and automatically adjust the feed to the maximum allowable level for each operation [12]. When necessary, these systems stop the machine because of overload or detected tool failure.…”

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confidence: 99%

Information-Measurement and Control Systems for Force and Vibroacoustic Parameters

2015

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Problems relating to the creation of information-measurement and control systems based on monitoring acoustic emission and electromagnetic effects are discussed. Experimental data are presented on the prospects for and limits on the use of these effects for high-speed machining of structural materials.Automatic maintenance of machine tool equipment with minimal operator interference is impossible without extensive use of inbuilt means of measurement [1-3] which gather and process information on the quality of the technological process and work as part of a diagnostic system. The functions of operational diagnostics include evaluating the state of mechanisms and the work process, as well as determining the location and causes of breakdowns, but also making decisions on the liquidation of the consequences of failures. The choice of equipment (probes) for operational diagnostics of a cutting process depends mainly on the following factors [4]: informativeness adequate to the situation in the cutting zone, as well as simplicity and reliability of installation on a machine.At present, the most widespread diagnostic systems are those based on information-measurement systems for force and vibroacoustic (VA) parameters. The devices for measuring force during cutting can be divided into two classes: those that are electromechanical (dynamometers) and those based on electromagnetic effects in an electrical drive (current and voltage sensors). Measurement devices employing electromagnetic effects in an electrical drive with the aid of current and voltage sensors are substantially cheaper and easier to use, so they are widely used in industry for setting up so-called adaptive control [5].Vibroacoustic signals during cutting and abrasion have a complicated structure that differs from that of the forces that appear during cutting. For example, when the contact area increases during abrasion, the amplitude of the oscillations may increase sharply in some stages, and decrease or not change at all during other stages [6].A correct evaluation of the state of the cutting process is diffi cult owing to the complexity of the process, for which there is no unifi ed theory, and the structure of its dynamics [7]. There is reason to state that the process of cutting materials is self-oscillatory [6,8]. Questions of diagnostics of the most important components of the technological equipment responsible for the machining accuracy also arise. Damage to these components during operation can lead to hazardous situations or breakdown of the technological cycle [9][10][11].The development of instrumentation and computational techniques in recent years has enabled partial monitoring and diagnostics by means of systems for monitoring machines and equipment based on information technology [5].Most adaptive control and monitoring systems on the market today react to a change in the cutting conditions through electromagnetic signals from the force part of an electrical drive and automatically adjust the feed to the maximum allowable level for each o...

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Application of morphological filtration in the problem of diagnostics of the cutting tool surface condition

Masterenko

Skoptsov

2019

Izmer. Tekhn.

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A System for Measuring the Active Power of the Asynchronous Motor of a Lathe Electric Drive

Cited by 3 publications

References 1 publication

Estimation of Uncertainty in Solving Multi-Parameter Diagnostic Problems

Estimation of Uncertainty in Solving Multi-Parameter Diagnostic Problems

Information-Measurement and Control Systems for Force and Vibroacoustic Parameters

Application of morphological filtration in the problem of diagnostics of the cutting tool surface condition

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