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Influence of hanging load on induction machine torque and stator current in hoisting winch system
Abstract
The aim of this paper is to analyze theoretically and experimentally the stator current and the load torque of a three-phase induction machine in a hoisting winch system in order to show the influence of hanging load at different levels in motoring modes. In this paper, the results of hanging load are verified in time and time-frequency domains and it has been observed that in case of fast changing of the load, it introduces a non-stationary oscillation during a short time. This leads producing a low frequency component in both the output torque and the stator current spectrograms with different levels of hanging load.
To control the distance between the side scan sonar and the seabed, a permanent magnet synchronous machine(PMSM) servo system is designed, and the overall structure of the system and the design scheme of each part are presented. The maximum torque per ampere (MTPA) vector control method is adopted to enhance the power factor and dynamic performances of the system, and based on MTPA vector control method, according to the harmful effects of the dynamic load in the servo control of sonar, the sliding mode variable structure (SMVS) vector control method is proposed, and the robustness of the servo system is effectively improved. The mechanism of the MTPA and SMVS vector control method is demonstrated in detail, and their effectiveness is verified. © 2018, Editorial Board of Journal of Northwestern Polytechnical University. All right reserved.
This paper studies the detection of small torque oscillations in induction motor drives during speed transients by stator current analysis. Small torque oscillations can be the consequence of mechanical faults. Speed transients result in time-varying supply frequencies that prevent the classical current spectrum analysis. The proposed solution is time-frequency signal analysis. This work particularly deals with the extraction of fault indicators that could be used in a permanent and automatic condition monitoring system. Three different fault indicators based on instantaneous frequency estimation and the Wigner Distribution are presented. Their performances are tested on experimental signals from speed transients under different load conditions.
Signal processing can be found in many applications and its primary goal is to provide underlying information on specific problems for the purpose of decision making. Traditional signal processing approaches assume the stationarity of signals, which in practice is not often satisfied. Hence, time or frequency descriptions alone are insufficient to provide comprehensive information about such signals. On the contrary, time–frequency analysis is more suitable for nonstationary signals. Therefore, this paper provides a status report of feature based signal processing in the time–frequency domain through an overview of recent contributions. The feature considered here is energy concentration. The paper provides an analysis of several classes of feature extractors, i.e., time–frequency representations, and feature classifiers. The results of the literature review indicate that time–frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications. The expectation is that further research and applications of these algorithms will flourish in the near future.
Mechanical conditions, such as load unbalance and shaft misalignment in induction motors can be detected by monitoring specific components in the stator current frequency spectrum. Previous work tested these mechanical conditions for the cases of no-load and full-load conditions in small induction motors, assuming that the effect of load on the detection scheme between these two conditions is linear. This paper examines the effect of changing the load of the motor on detecting these mechanical conditions in both the current and vibration spectra. The paper shows that the spectral components associated with mechanical conditions do not always linearly change as the load varies. It shows that other factors, such as mechanical resonance in the coupled systems, affect these spectral components. Experimental results for testing different types of mechanical faults, with the load changing from no-load to full-load, are shown.
This paper examines the detection of mechanical faults in induction motors by an original use of stator current time-frequency analysis. Mechanical faults lead generally to periodic load torque oscillations. The influence of the torque oscillations on the induction motor stator current is studied using an analytical approach. The mechanical fault results in a sinusoidal phase modulation of the stator current, which is equivalent to a time-varying frequency. Based on these assumptions, several signal processing methods suitable for stator current signature analysis are discussed: classical spectral analysis, instantaneous frequency estimation, and the Wigner distribution. Experimental and simulation results validate the theoretical approach in steady-state operating conditions
The aim of this study is to analyze both stator current and output torque spectra of a three-phase squirrel-cage induction machine in a hoisting winch system. These experiments are performed to show the influence of the shaft misalignment at different load and speed levels in motoring and braking modes. In this paper, it is observed that the influence of the shaft misalignment in the stator current and torque harmonics can be damped by increasing the load level for both motoring and braking modes.
The aim of this paper is to analyze theoretically and experimentally the stator current of a three phase induction machine and load torque signals in a hoisting winch system, in order to show how they are influenced by the wire rope faulty condition. When the wire rope is subjected to axial and torsional loads, the outer strands can separate from the core in permanent way and make the rope useless. The term birdcaging is used to describe this type of phenomenon giving a permanent appearance of a wire rope forced into compression. In a hoisting winch system the running rope is bent over drum and pulley and it is torsionally stressed by the fleet angle between them. In this paper, the effect of the fleet angle is observed in the stator current and load torque, in order to detect the wire rope birdcaging. This study is performed on a 22 kW squirrel-cage induction machine and presents an original non invasive way to detect wire rope fault condition, by using the stator current of the electrical machine.
This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.
In the view of implementing automated solutions for monitoring complex machining processes such as milling, the usage of acoustic emission (AE) in machining is regarded as a promising way for assessing machine tool condition and for in-process detection of workpiece malfunctions. Correlating AE signal events with the occurrence of workpiece surface anomalies (e.g. laps, material drag) can be a powerful method for scrap reduction of expensive components such as those employed in aerospace industry. This paper proposes new methods for supervising cutting processes with multiple teeth cutting simultaneously, i.e. milling, by use of AE signals backed-up by force data. This is done by taking into account signals patterns when one, two or three teeth are cutting simultaneously, situation that often occurs in real milling applications. The research shows for the first time that identification of milling conditions (i.e. cutting with one/two/three teeth) is possible using only AE signal in time–frequency (T–F) domain. Moreover, detection of surface anomalies, such as folded laps that are generated by damaged cutting edges can be successfully identified in various milling conditions. The paper demonstrates that time–frequency analysis of AE signals empowered with advanced processing techniques has great potential to be used in flexible and easily to implement monitoring solutions to enable milling of anomaly-free workpiece surfaces in difficult-to-cut aerospace materials.
The new semi-active variable stiffness tuned mass damper (SAIVS-TMD) system, developed by the authors, is capable of continuously varying its stiffness and retuning its frequency in real time. The tuned mass damper (TMD) system can only be tuned to a fixed frequency, which is the first mode frequency of the building. The SAIVS-TMD system is robust to changes in building stiffness and damping.The short time Fourier transform (STFT) is the most widely used method for studying non-stationary signals. The basic idea of short time Fourier transform is to break up the signal into small time segments and Fourier analyze each time segment to ascertain the frequencies that exist in it. For each different time a different spectrum is obtained and the totality of these spectra is the time–frequency distribution. The new idea proposed in this study is to use STFT to identify the dominant frequency of response, and track its variation as a function of time to retune the SAIVS-TMD.The effectiveness of a SAIVS-TMD controlled by the new STFT algorithm for response reduction of a wind excited tall building is investigated in this study. An analytical model of the tall building with time varying stiffness of SAIVS-TMD is developed. The frequency tuning of the SAIVS-TMD is achieved by a new time frequency algorithm based on STFT. It is shown that the SAIVS-TMD can reduce the structural response, when compared to the uncontrolled case and the case with TMD. SAIVS-TMD is particularly effective in reducing the response when the building stiffness changes; the TMD loses its effectiveness under building stiffness variations. The response reductions due to an active tuned mass damper can also be achieved by SAIVS-TMD—with an order of magnitude less power consumption—with the STFT algorithm.
The power spectrogram has found wide use in diagnostics based on mechanical vibrations. It makes use of the amplitude values of the Short Time Fourier Transform (STFT) but does not consider the information contained in the phase values. In the phase spectrogram, on the other hand, it is precisely the phase values of the STFT that are processed so that they can be presented graphically and are useful. The time–frequency distribution of the phase of a specific component supplies information about the phase modulations around a reference point, which determines both the reference phase and the reference frequency for this component. The frequency spectrogram is calculated from the phase difference between each time slice of the STFT. The frequency spectrogram shows the drift on the instantaneous frequency of each spectral component. These tools have proven to be useful complements to the power spectrogram. In crack detection, for example, they accentuate any frequency drift that occurs in the damped free-decay response of a cantilever beam, which appears to be a function of the crack depth. When identifying a sporadically pulsed vibration source, the ability to show the phase coherence from one impact to the other in time on the spectrogram makes it possible to draw a conclusion about the presence of a stable-vibration source hidden in the background. A dual-channel phase spectrogram is also presented with two application examples.
The aim of this paper is the monitoring of a gearbox by using the stator current signature analysis in the driving induction machines. The detection of mechanical faults using the vibration signal analysis has been well studied before. Recently, some works have been performed in order to observe the vibration components of the mechanical part in the stator current spectrum for an induction machine but without any theoretical detail. This paper proposes a theoretical framework based on torque oscillations due to the characteristic torsional vibrations in a gearbox and the source of mesh and rotating frequency components in the stator current is presented. In order to verify the theoretical development, a test-bed based on a 5.5 kW three-phase squirrel-cage machine connected to a gearbox has been used.
Mechanical faults in induction motors can be detected by monitoring specific components in the stator current frequency spectrum. Previous work tested some mechanical conditions for the cases of no-load and full-load conditions in small induction motors, assuming that the effect of load on the detection scheme between these two conditions is linear. This paper presents an algorithm for detecting mechanical conditions in induction motors under any load condition using spectral analysis of the stator current. The proposed algorithm takes into account the load related abnormalities and resonance by classifying the load-levels into bins and calculating a baseline for each load bin.
This paper shows that mechanical faults in small induction
machines can be detected with adequate sensitivity by merely monitoring
the RMS value of the stator current over a certain frequency range.
Furthermore, a simple technique for mechanical fault detection using
stator current is presented for the case wherein spectral monitoring is
necessary. Experimental results for different types of mechanical
faults, namely, rotor unbalances and shaft misalignments, are
shown