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8Дефекты в ременной передачи вентилятора представлены рисунком 2. Этот вид неисправностей определяется по частотам, кратным частоте
биений ремня, определяемой длиной последнего и диаметрами шкивов. Характерные частоты отмечены - (С).
Рисунок 2 - Характерные частоты (С) при наличии дефектов ременной передачи.
При проведении повторных измерений формируется база данных, позволяющая отслеживать динамику развития повреждения во времени, что дает возможность заблаговременно планировать выведение оборудования в ремонт и существенно сократить затраты, связанные с отказами оборудования, так и снять затраты электроэнергии.
Выводы
1. Как правило, диагностирующие дефекты АД определяются при помощи вибродатчиков и с помощью токовых приборов, регистрирующих величину тока в обмотках электрических машин.
2. Диагнозы по этим дефектам проводятся как частные случаи. Причины особенностей спектральной картины описываются по внешним признакам без связи с внутренними физическими процессами в АД.
3. Использование диагностических спектральных вибропараметров электрических машин выявит на ранних стадиях работы дефекты и позволит в дальнейшем разработать свои диагностические правила для анализа дефектов состояния АД по вибропараметрам.
Список литературы
1. Баркова Н.А. Вибрационная диагностика машин и оборудования [Текст] / Н.А.Баркова, А.А. Борисов, А.А. Борисов - СПб: Северо-Западный учебный центр, 2013. - 152с.
2. Барков А.В. Вибрационная диагностика электротехнических машин в установившихся режимах работы [Текст] / А.В. Барков, Н.А. Баркова, А.А. Борисов - СПб: Северо-Западный учебный центр, 2006. - 36с.
3. Гольдин А.С. Вибрация роторных машин [Текст] / Гольдин А.С. - М.: Машиностроение, 1999. - 344с.
4. Зусман Г.В. Вибродиагностика [Текст] / Г.В. Зусман, А.В. Барков - М.: Издательский дом «Спектр». - 2011- 215с.
5. Неразрушающий контроль. Справочник / Ф.Я. Балицкий, А.В. Барков, Н.А. Баркова и др. Вибродиагностика, - М.: Машиностроение. - 2005, Том 7 - 829с.
Trembovetskyi M. P.
Head of the Department of Energy efficient technologies Doctor of Technical Sciences, Associated Professor State university of Telecommunications
Zaika V. F.
Head of the Department of Telecommunication systems and networks Doctor of Technical Sciences, Associated Professor State university of Telecommunications Zhebka V. V.
Associate Professor of the Department of Software engineering
PhD in technical sciences State university of Telecommunications Ivanichenko E. V.
Senior Teacher of the Department of Energy efficient technologies
State university of Telecommunications
THE EFFECT OF NON-GAUSSIAN INTERFERENCE ON THE QUALITY OF RECEIVING OF DISCRETE MESSAGES AND CHARACTERISTICS OF THEIR SUPPRESSION IN MEMORY
CHANNELS
The paper presents the analysis of the models and probability characteristics of the additive interference in communication channels. It showed that the most convenient way for both the approximation of real interference and the synthesis of de-modulation algorithms are the quasi-deterministic models of a phenomenological type for impulse noise and quasi-harmonic ones for concentrated interference. It is shown that at present there is no clear limit between concentrated and impulse interference, and the majority of non-Gaussian interference can be considered as intermediate-type interference, which can be represented as radio pulses with high-frequency coverage. They can be approximated by the models of these types.
Key words: communication channel, non-Gaussian interference, impulse interference, fluctuation interference, concentrated interference, interference prevention.
The problem-setting:
While receiving the discrete messages the indicator of quality is the mean probability of receiving an error symbol. It is difficult to get the general expressions for error probability during reception with complex interference taking into account the variety of interference parameters and their non-Gaussian statistics.
The analysis of current approaches to solve the problem of concentrated and impulse interference suppression showed that in this case linear methods are practically not applied. The effectiveness of non-linear methods fails while changing non-Gaussian characteristics such as amplitude and spectrum length. The implementation of the majority of non-linear processing methods is complicated.
The purpose of the paper
Considering the fact that compensation-evaluation method is mainly used to control the concentrated interference within channels with symbol-to-symbol interference, the aim of the paper is to study one of these methods of suppressing single pulse of concentrated interference in mono channel system and demonstrate that different non-inertial non-linear transformations are the most effective way to suppress non-Gaussian interference while transmitting the discrete messages.
Basic research material
While receiving the discrete messages the quality indicator is the mean probability of receiving an error symbol. It is difficult to get the general expressions for error probability during reception with complex interference taking into account the variety of interference parameters and their non-Gaussian statistics.
In early researches on the assessment of noise stability within channels with complex interference patterns many assumptions were made, for example, idealized models of concentrated interference. Concentrated interference often approximated by Gaussian models and impulse interference were represented by the series of delta-impulses. Later other researches on the evaluation of noise stability within channels with non-Gaussian interference emerged. The most general results in this field were given by Klovskii D.D. in the research [1], where transmission in multi-path radio channels under influence of cumulative interference had been studied. Different researchers [2, 3] developed many methods that allow obtaining approximate formulas for evaluation and calculation of the error probability. They are carefully reviewed [4]. They also state, that, in case of concentrated interference not
only the type of decisive schemes but the base signal is also significant.
To carry out the analysis of interference immunity it is reasonable to compare interference immunity indicators of the reception of the system, which is optimal at the action of fluctuational Gaussean interference and focused interference [5]. To facilitate the analysis of the comparison lets consider the example of a binary system with an active pause (coherent approach) realized either on compatible filters or on the multipliers. Sup-posably, the system uses contrary or orthogonal signals, in case of fadings, they are distributed according to the Rayleigh law. Differences in realization of such system are not substantial from the point of view of potential interference immunity. Depending on the circumstances of signal distribution and concentration of interference, it is appropriate to discuss the following important separate examples:
1) non-fading signal and non-fading interference. This situation is not infrequent when a radio station is located at a short distance;
2) non-fading signal and fading interference;
3) fading signal and non-fading interference;
4) fading signal and fading interference.
The paper presents dependency graphs of an average error probability with values p 6 [0,5; 10-5] from the ratio signal-interference h2 at the absence and action of a single focused interference. At p = 10-4 and FCT = 2 energy loss makes around 4 decibel. At reducing p till 10-7this value grows three times. They also show, that interference probability in the circumstances of concentrated interference activity depends not only on h2, but also on the index of mutual difference of signal and interference. For signals and interferences with approximately similar spectrums this index can be calculated according to the formula:
g
h2 2 _ ""
h2 '
(1)
where h2 — is a ratio of the energy of focused interference to the spectral density of white Gaussean interference.
Apparently, the smaller is the FCT signal base, the worse is interference immunity of the system at other equal circumstances. Interference immunity gets the maximum in case of 1) comparison with all others. Obviously, the existence of Rayleigh fading in interference is much more undesirable for reception than at the concentrated interference with constant intensity. Systems, using the opposite signals demonstrate higher (though not much) interference immunity than systems
with orthogonal signals. It is true under the circumstances of Gaussean interference action. The situation changes much, if the following equation is true:
h2
, (2)
where p - is a certain invariable that depends on the parameters of signal and interference that is placed in the range 1 << p « FCT
In this case the probability of error for systems with orthogonal and opposite signals are the same. However, at the development of this system, it is reasonable to use the system of opposite signals.
When focused interference has quite a big capacity and (1) is true, energy losses for all the cases in this system are intolerably growing. In practice such situations are common. Therefore, the necessity to develop methods to counteract such focused interferences appears.
In the majority of cases the analysis and synthesis of the demodulation algorithms are counted with due regard to toleration that aftereffect is absent in the channel. Unfortunately, it is impossible to build such a channel in practice. The reasons for aftereffect or the channel memory may be described by the nonhomogeniety of the propagation medium, existence of reactional elements, reflections which follow echo-signals. The outcome of the activity of these factors is the channel reflection diffusion in time in comparison to the influence, which in case of transmitting the discrete messages using the sequential procedure leads to overlap of signal elements which correspond to message symbols: inter-symbol interference.
In space-time channels spatial diffusion also appears. That introduces new additional difficulties at demodulation, as additive interferences also act in the channel, they are often non-Gaussean. The search for such methods of demodulation was first accomplished by Nyquist and Shannon. To decrease the effect of the mentioned factors a range of methods is developed to date, based on space, frequency or correlation channel rays diversity. Klovskii D.D. carried out the basic study of the models and research on discrete messages transmission on radio channels with inter-symbol interference [1].
However, the analysis of the majority of methods shows that the emphasis is made on the inter-symbol interference, and white Gaussean interference is considered as an additive model. In practice, when neither central frequency nor the width of the focused interference spectrum is known, application of whitening and barrier filters leads to the total destruction of part of the information. This is applied both to single-channel systems without frequency excessiveness and to multichannel systems without frequency dubbing.
Evaluation-compensation methods are mainly used in counteracting concentrated interferences in the channels with inter-symbol interference. It is also accepted, that in the analyzing interval only one interferences is in action.
Let us consider, as it is shown in [6], one of these methods of suppressing single impulse of concentrated interference for single-channel system. Supposably, the only thing known about concentrated interference is
that it exists and the indicators of the central frequency and the width of the concentrated interference spectrum u(t) in the mixture z(t) = s(t) + u(t) on the analyzed interval are [0; 7] unknown. As the result of preliminary evaluation, estimated average indicator of interference u(t) is foreseen. In the compensatory part of the method it is extracted from the mixture.
zp(t) = z(t) — u(t) = s(t) + e(t), (3)
where e(t) = u(t) — u(t) is the prediction error. The error at demodulation will be minimal at the condition, that :
M[e2} < M[u2}, (4)
where M{...} — is mathematical expectation.
In the evaluating part of the predicting method is based on the interference approximated by the actual interval of complex harmonic oscillations with random frequency, phase and amplitude. If the timing is equal for all analyzed intervals then interference parameters are applied on the following interval. In other case the evaluation is done using the recurrence relation
WO)
where (~) denotes the combination of Hilbert.
We assume that parameters of real concentrated interference change in time resulting in extrapolation error. It is important, for example, that the spectrum width of concentrated interference doesn't depend on time. After the calculation of correlation function the mean square deviation e2 can be obtained:
e2 = M[(uk(t) — uk(t))2} = 2B(0) — 2B(T)cosuT — 2B(T)sinwT = 2(B0(0) —
cosp(t)), (6),
where B0 and (p —: curve and phase of correlation function.
It demonstrates that to obtain (3) it is necessary to follow the inequation (7)
Bom>B-M. (7)
If the interference is introduced by quasi-determined model type (8) then the prediction may be more accurate. It is necessary to take into account the change of concentrated interference module and phase shift that differentiate the preceding formula uk-1(t) from
T^k(t):
(8)
ii(t) = ai(t, e^, .....
where at (t) — certain determined function that characterizes the structure of i- interference on the duration of the elementary periodic signal; — random parameters of i-interference with known density distribution or unknown at the time of reception. The examples of such parameters can be the phase, temporary shift relative to the impulse start, etc.
While analyzing the influence of impulse interference current on channel with dispersion, in many cases the total interference is approximated by the model of Gaussian white noise; in this case the demodulation is carried out by the optimal scheme with the most consistent evaluation for example, by the Viterbi algorithm (7). The Klovskii-Mykolaiev algorithm is quite similar to it by its characteristics but is simpler in implementation.
Alternatively, the impulse interference current is represented by the model which is generalization for the channels with dispersion [6] :
HO = ^^ Yik Aikgi(t - tik)exp(-Jcpik), (9) i k
where yik - input indicator , k - impulse interference of I direction y = 1 if the interference occurred, 0 in other case ); Aik - amplitude , tik - moment of impulse occurrence.
The dispersion ok k impulse of total additive interference (impulse interference + Gaussian white noise) is calculated with known quantity yik depending on demodulator realization the suppression is carried out by adding robust blocks that carry out multiplica-i
tion by ak = -2 instead of multiplication we can make
rejection of k - count on the input of the decisive scheme. Generally, this method can also be called evaluation-compensation one.
The paper presents only general characteristics of the most common methods of interference suppression in channels with symbol-to-symbol interference. They do not consider the joint action of non-Gaussian impulse and concentrated interference. The state-of-the-art developments in the field of synthesis of adaptive equalizers and methods of channel characteristics equalization allow decreasing the impact of symbol-to-
symbol interference. The findings [7] show that these developments are very effective, especially in the cases of minor symbol-to-symbol interference. After the compensation of symbol-to-symbol interference, separate interference suppression can be achieved.
Literature:
1. Кловский Д.Д. Передача дискретных сообщений по радиоканалам / Д.Д. Кловский. - М.: Радио и связь, 1982. - 304 с.
2. Middleton D. Statistical-Physical Models of Electromagnetic Interference. - IEEE Trans., 1977, v. EMC-19, N 3, pp. 106-127.
3. Bello P.A., Esposito R. A New Method for Calculating Probabilities of Error Due to Impulsive Noise. - IEEE Trans. 1969, v. Com-17, N 3, p.368-378.
4. Коржик В.П. Расчет помехоустойчивости систем связи дискретных сообщений: Справочник / В.П. Коржик, Л.М. Финк, К.П. Щелкунов: Под ред. Л.М. Финка. - М.: Радио и связь, 1981. - 232 с.
5. Сикарев А.А. оптимальный прием дискретных сообщений / А.А.Сикарев, А.И.Фалько. - М.: Связь, 1978. - 328 с.
6. Николаев Б.И. Последовательная передача дискретных сообщений по непрерывным каналам с памятью /Б.И. Николаев. - М.: Радио и связь, 1988.
- 264 с.
7. Прокис Дж. Цифровая связь // Пер. с англ.
- М.: Радио и связь, 2000. - 800 с.
Ishmukhametov B.Kh.
Postgraduate of Ufa State Petroleum Technological University Ишмухаметов Булат Ханифович
Аспирант Уфимского государственного нефтяного технического университета
SUCKER ROD PUMP FOR WATERED OIL PRODUCTION СКВАЖИННЫЙ ШТАНГОВЫЙ НАСОС ДЛЯ ДОБЫЧИ ОБВОДНЕННОЙ НЕФТИ
АННОТАЦИЯ
В статье представлены результаты разработки штангового насоса для добычи обводненной нефти. Показано, что нанесение микрорельефа в форме кольцевых канавок, позволяет увеличить гидравлические сопротивления течению жидкости в кольцевом зазоре.
ABSTRACT
The article presents the results of the development of a sucker-rod pump for the production of watered oil. It is shown that the application of the microrelief in the form of annular grooves, allows increasing hydraulic resistance to the flow of liquid in the annular gap.
Ключевые слова: штанговый насос, плунжерная пара, регулярный микрорельеф, подача насоса, утечки в зазоре, канавки, добыча нефти.
Key words: sucker rod pump, plunger pair, regular microrelief, pump capacity, gap leakage, groove, oil production.
Утечки жидкости через плунжерную пару в значительной мере влияют на коэффициент подачи штангового насоса. Рост утечек связан с износом плунжерной пары в процессе эксплуатации. При наличии механических примесей процессы износа интенсифицируются, поэтому задача защиты насоса от механических примесей является актуальной.
Патентный анализ конструкций штанговых насосов для добычи обводненной нефти содержащей механические примеси показал, что существует множество конструкций штанговых насосов.
Известен скважинный штанговый насос, содержащий рабочую пару плунжер-цилиндр с установленными в них соответственно нагнетательным и всасывающим клапанами, фильтр механических примесей. Последний связан с приемом насоса и снабжен снизу емкостью предварительного накопления механических примесей. Фильтр представляет собой концентрически расположенные трубы, сообщающие забой скважины с приемом насоса. Емкость предварительного накопления мехприме-сей выполнена в виде продолжения внешней трубы фильтра и снабжена подпружиненным клапаном,
