Healthy Rotor Research Articles

Research on vibration signal decomposition of cracked rotor-bearing system with double-disk based on CEEMDAN-CWT

In the actual working process, the source of vibration signal is not only the rotor itself, so the detected vibration signal will become complicated. This complex signal makes it difficult to accurately measure the existence of crack. In this paper, a novel method, which includes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and continuous wavelet transform (CWT), is proposed to analyze the cracked rotor-rolling bearing system. The CEEMDAN-CWT successfully separates the vibration signal of the rotor itself from the original signal and provides results similar to the simulation signal. At the speed below 2000 rpm, the 2X frequency difference between cracked rotor and healthy rotor in CEEMDAN-CWT spectrum is about 1, while the difference of FFT spectrum of original signal is about 0.6, which shows the superiority of the novel method in extracting rotor vibration signals from complex vibration signals.

Physics-informed Neural Network for system identification of rotors

The condition of the rotor system remains difficult to assess due to system nonlinearity and nosiy measurements. To deal with the problem, we proposed a hierarchical physics-informed neural network (HPINN) to discover the ordinary differential equation (ODE) of a healthy/faulty rotor system from noise measurements and then assess the machine condition based on the discovered ODE. Specifically, the ODE of a healthy rotor system is first stably identified from noise measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODE, the extra fault terms in the ODE of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified by the data set collected on the circulating water test bench, showing the potential for practical applications.

Adaptive data-driven fault-tolerant control strategy for optimal power extraction in presence of broken rotor bars in wind turbine

Broken rotor bars of a Squirrel Cage Induction Generator (SCIG) impact significantly the quality and quantity of produced electrical energy from Wind Energy Conversion Systems (WECS) because: (1) they change the characteristics of SCIG entailing the invalidity of the designed Maximum Power Point Trucking (MPPT) control and pitch angle control, (2) they increase mechanical and thermal stresses, as well as the harmonic content in stator currents. Therefore, this paper proposes an efficient fault tolerant control strategy in order to mitigate the aforementioned consequences. This strategy is based on a hybrid (mechanic-electric) fault tolerant control (FTC) allowing obtaining optimal power extraction in presence of broken rotor bars. A failure (one or two broken bars) is detected in early stage based on the use of a data-driven (machine learning) approach. The latter uses a discriminative feature space (frequency and magnitude of stator harmonic currents) in order to represent and separate the normal operation mode from faulty modes (broken bars). Simulations using MATLAB demonstrated a nominal current in healthy rotor bars in wind turbine operation zone-3 and new optimal power extraction despite one or two broken bars.

Interpolated reference model based fault tolerant sliding mode control for an octorotor

In this paper, a quasi-linear parameter varying sliding mode control allocation law is proposed for the fault tolerant control of an octorotor. In the event of rotor faults/failures, an allocation law redistributes the control effort among the remaining healthy rotors. The sliding mode control law is designed to guarantee asymptotic tracking of a reference model which is tuned on-line, through an interpolated feedback gain, to ensure that the control signals remain within their saturation limits. A method for designing the parameterised feedback gain is proposed which is shown to maximise a defined stability criteria whilst preventing undesirable performance characteristics in the reference model. The proposed scheme is tested on a non-linear octorotor model in the presence of severe rotor failures and uncertainty/disturbances.

Classification of a cracked-rotor system during start-up using Deep learning based on convolutional neural networks

This article addresses an improvement of a classification procedure on cracked rotors through Deep learning based on convolutional neural networks (CNNs). At first, a cracked rotor-bearing system is modeled by the finite element method (FEM), then throughout its start-up, the related time-domain responses are calculated numerically. In the following, as a pre-processing stage, continuous wavelet transform (CWT) and Short-time Fourier transform (STFT) are applied on the three various health conditions, i.e. without crack, shallow-cracked, and relatively deep-cracked shafts. The plots of CWT’s coefficients and STFT’s in these various classes are used as the input dataset in Deep learning based on CNNs and the three classes are introduced as the output. AlexNet with 25 layers is employed as the network. The results of the testing phase demonstrated that not only this expanded method has a reasonable capacity in the classification of cracked and healthy rotors, but it also can classify cracked rotors with different crack depths with a negligible error.

A Novel Algorithm to Detect Broken Bars in Induction Motors

A new algorithm is proposed in order to detect and quantify partially broken bars in induction motors during start-up without load. In the qualification process, no threshold is used. It uses the principle of the harmonic generated by the broken bar in the stator current, it should vary with the slip to confirm the failure and provide more security in the diagnosis. A severity index is also proposed, based on the maximum peaks of the Teager energy operator of the Gaussian filter applied in the stator current signal. Experimental data were used to validate the algorithm, comparing rotors manufactured with one partially bar, one failed bar, and two completely failed bars, arranged in a variety of ways. The results show that the algorithm qualifies correctly the faulty bar, even for a partially broken bar. In the quantification phase, the severity index of the fault shows the higher sensibility in comparison to the state-of-the-art. Its value for a 3 HP motor is: 8.837 × 10−10 for a healthy rotor, 2.553 × 10−8 for a partially broken bar, and 4.058 × 10−7 for one broken bar.

Nonlinear Control of Quadrotor for Fault Tolerance: A Total Failure of One Actuator

This paper deals with the problem of a quadrotor experiencing a total failure of one actuator. First, a nonlinear mathematical model for the faulty quadrotor is derived with three control inputs and six outputs, which includes the translational and rotational dynamics. Because of the limited inputs, the controllability of the yaw state is sacrificed, and the control inputs are reallocated to the other three healthy rotors. Second, a nonlinear controller is designed based on the proposed model. This presented controller includes two subcontrollers: 1) a roll angle, pitch angle, and altitude subcontroller and 2) a horizontal position subcontroller. In the controller design, the Moore–Penrose pseudoinverse of the coefficient matrix is applied to overcome the singularity of the roll angle. Third, the stability of both of the designed subcontrollers is verified by the Lyapunov stability theorem, and the convergence of the designed subcontrollers is analyzed. Finally, simulation results are provided to verify the effectiveness of the proposed model and the designed controller.

ANN-Based Pattern Recognition for Induction Motor Broken Rotor Bar Monitoring under Supply Frequency Regulation

The requisite of direct-on-line (DOL) starting for various applications in underground mines subjects the rotor bars of heavy-duty squirrel cage induction motors (SCIMs) to severe stresses, resulting in sustained fault in the rotor bars, unlike the applications where mostly reduced voltage starting is preferred. Furthermore, SCIMs working in underground mines are also affected by unforeseen frequency fluctuations. Hence, the paper proposes a discrete wavelet transform (DWT)-based broken rotor bar detection scheme using the stator current analysis of SCIM when subjected to a frequency regulation (±4% of 50 Hz supply) in steady-state, as prevalent in underground mines. In this regard, the level-seven detailed coefficient obtained by the DWT-based multi-resolution analysis of stator current corresponding to the healthy rotor is compared with that of the faulty rotor to extract the necessary features to identify the fault. Further implementation of the proposed scheme is done using artificial neural network (ANN)-based pattern recognition techniques, wherein both feed-forward backdrops and cascaded forward backdrop type ANNs have been used for fault pinpointing based on the feature extraction results obtained from DWT. The scheme is developed and analysed in MATLAB/Simulink using 5.5 kW, 415 V, 50 Hz SCIM, which is further validated using the LabVIEW-based real-time implementation.

Experimental Comparison of Response for Healthy and Cracked Overhung Rotor System

Online diagnosis of the rotor system has been of great aid for possible detection of crack. In this paper, experimental vibration response of unbalance cracked overhung rotor system with an open crack is compared to the response of a healthy unbalance overhung rotor system for possible indication of the crack presence, due to change in the dynamic response of the cracked rotor system. The experimental modal analysis results have been compared with the finite element results to validate the accuracy of the experimental setup, followed by the comparison of the spectral response for healthy and cracked rotor system. The change in the dynamic behaviour of the cracked rotor system is studied through the waterfall plot and the colour map plot. A noticeable increase of 27.77% in response amplitude for crack overhung rotor system compared to that of healthy overhung rotor system is observed with unbalance provided to disc. The changes seen in both plots can be utilized for the indication of crack presence in the rotor system.

An analytical model for squirrel cage induction machine with broken rotor bars derived based on the multiple coupled circuit theory and the winding function approach

SummaryIn this paper, a squirrel cage induction machine (SCIM) with broken rotor bars is modeled by using the multiple coupled circuit theory and the winding function approach. This analytical model includes all the spatial harmonic components of air gap magnetomotive force and is applicable to the study of transient machine behaviors. Equivalent circuits that are consistent with the topologies of healthy and faulty cage rotors are developed. The equivalent circuit for the healthy rotor of a SCIM having an integer number of rotor bars per pole is simplified further by exploiting the symmetry of the winding functions of the stator. In the equivalent circuit for a healthy rotor, inserting an independent current source in the bar that breaks in the damaged rotor, whose current is the negative of the current through the same bar, and replacing all the voltage sources in the rest of the bars with short circuits result in an equivalent circuit that can be employed to solve for the deviations in rotor mesh currents caused by bar breakage. A simulation is carried out in MATLAB/Simulink for a 2.2 kW, three‐phase, four‐pole SCIM with 28 rotor bars and a laboratory test rig is built. The effectiveness of the proposed model was validated by the good agreement of the simulation results with the experimental data.

International Journal of Prognostics and Health Management, ISSN 2153-2648, 2015 007 Effectiveness of vibration monitoring in the health assessment of induction motor

Induction motors are widely used prime movers for rotating machinery in most of the industrial applications. Reliability of the induction motors plays a significant role in reduction in downtime of the machinery. Proper diagnostic procedures are to be followed to assess the condition of the motor. Based on the literature, it is found that the vibration analysis is mostly used for diagnosis of rotating systems. However, industrial experiences reveal that potential motor failures have been observed very frequently even with proper vibration based condition monitoring. Therefore, this paper focuses on further investigations of whether the vibration monitoring stand alone can properly diagnose the presence of faults such as eccentricity in an induction motor or any other alternative technique should be employed in addition to the vibration monitoring for better diagnosis. In this paper, experimental investigations have been carried out to monitor the changes in the vibration and current spectrum of an eccentric motor in its decoupled and coupled state with a healthy rotor system.

Analysis of stator and rotor currents and torque of induction machine with rotor-bar faults

The paper analyzes a case of the induction motor in which one or more rotor bars in the rotor cage are broken. The method of space vectors and symmetrical components of instantaneous values of currents and voltages is used in the analysis. The solution obtained makes it possible to determine the currents in each of the healthy rotor bars, the stator currents, and the electromagnetic torque of the machine for any number and arbitrary combination of broken rotor bars. The calculations are made for an induction motor with 17 rotor bars. The theoretical results are compared with experimental measurements.

Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors.

Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.

Feedforward Rotor Fault Compensation

When damaged, it is not unusual to observe nonlinear responses in previously linear structures. Specifically, in rotating machinery context, unbalance, misalignment, bearing, and shaft damage occur during operation, and their responses arise as a combination of multiple modes and harmonics. These vibration effects are undesirable and, besides compromising the system performance, can propagate or generate further faults. In practice, the machine cannot be simply stopped for repairs, and a method for provisory attenuating such problems is desirable. The fault effects can be approximated by a set of loads distributed along the healthy rotor linear model approximation. Intuitively, one can also estimate counteractive forces to mitigate the fault symptoms. Therefore, this work proposes a simple feedforward approach to detect and compensate fault effects at multiple harmonics in rotors. The compensating force is calculated to minimize the least square modal difference between healthy and faulty rotor. A test rig is operated in distinct conditions to demonstrate and validate the method highlighting different fault symptoms (unbalance, misalignment, and shaft bow).

Advanced Fault-tolerant Control Strategy of Wind Turbine Based on Squirrel Cage Induction Generator with Rotor Bar Defects

Several research works in Wind Energy Conversion Systems (WECS) based on Squirrel Cage Induction Generator (SCIG) are developed for isolated site applications. Where, WECS are usually controlled by the Maximum Power Point Trucking (MPPT) algorithm in order to extract the maximum power from wind with constant operating point of SCIG. However, this WECS based on SCIG generator suffers from the problem of breaking rotor bars, therefore a failure in rotor bars is particularly a challenging task because: 1) it can change the operating point generated by MPPT algorithm, 2) increase mechanical vibrations, 3) affect the operation of gearbox, 4) inject a harmonic current in the grid. Therefore, Fault Tolerant Control (FTC) scheme is necessary to allow an optimum energy production, to reduce the maintenance costs and to increase the WECS availability. In this work, a new fault diagnosis approach based on frequency analysis of stator currents is used to detect the failure in bars of SCIG, then FTC algorithm is developed to adjust the pitch angle in max wind speed zone by determining a new operating point according to the wind speed, which leads to extend the lifetime of WECS. Simulation results using MATLAB SIMULINK showed a nominal current in healthy rotor bars and the lifetime of WECS was increased.

Prognostics Model to Predict Brake Rotor Thickness Variation

Brake rotor thickness variation causes brake torque variation which can lead to brake judder and pulsation, steering wheel oscillations and chassis vibration. In this paper, we have proposed a prognostics methodology to predict the degradation level of brake rotor due to disc thickness variation. Leveraging the time and frequency domain analysis, this model creates health indicators to assess the health of the rotor and predict the rotor thickness variations of 36 micrometers or more. These health indicators that are calculated during braking events include: (i) envelope or variance of the brake master cylinder pressure (MCP); (ii) envelope or variance of the longitudinal acceleration (AX); (iii) the root mean square amplitude of the average order spectrum of the MCP at order one; and (iv) the root mean square amplitude of the average order spectrum of the AX at order one. This paper demonstrates that the above health indicators are significantly larger for a degraded brake rotor due to thickness variation compared to a healthy rotor.

Study on Coupling Fault Dynamics of Sliding Bearing-Rotor System

The phenomenon of oil film oscillation and frequency locked may occur in a healthy rotor system which is supported by sliding bearing. The dynamic behavior of the rotor system with misalignment and rubbing coupling fault supported by sliding bearing is also very complex. To solve the problem of fault diagnosis in this case, a dynamical model of rotor system is proposed in this paper. The short bearing oil film force, the equivalent misalignment moment, and Hertz contact theory are applied to establish the model. For rubbing faults, the Augmented Lagrange method is used to deal with the contact constraints to ensure that the boundary penetration depth is within the specified tolerance range. Furthermore, the dynamic behavior of the faulty rotor system under different rubbing stiffness conditions is analyzed in this paper. Meanwhile, the fault signal is divided into equal-band by the wavelet basis functions to find out the fault frequency band of the rotor system. Finally, the accuracy of the simulation study is verified by measurements obtained from the faulty rotor test platform. The following findings are made in this paper. The rubbing fault is dominant in the coupling fault. With the increasing of the speed, the frequency components of the system are dominated by high frequency. The double frequency is the main fault feature frequency band. It can be seen that the rotor system moves gradually from a quasi-periodic state into chaos due to the Lyapunov exponent. At the same time, due to the effects of misalignment moment and friction force, the phenomenon of oil film instability is partially suppressed. The lagging of the first and second-order oil film oscillations occurs.

Research on singular spectrum decomposition and its application to rotor failure detection

As an important part of rotating machinery, a healthy rotor is critical to ensuring optimal working conditions of the entire system. Considering that the vibration signal of rotor consists of different frequency components when the failure arises, a novel rotor failure detection method based on singular spectrum decomposition (SSD) is presented. The original vibration signal is adaptively decomposed into a number of singular spectrum components (SSCs) by the SSD method. Then, energy separation algorithm (ESA) is adopted to demodulate each singular spectrum component. Finally, the SSD-ESA time-frequency spectrum can be obtained and the fault features contained in the SSD-ESA time-frequency spectrum can be identified to determine the fault types. The effectiveness of SSD for harmonic separation was assessed through tones separation analyses, the results show that SSD is able to separate more harmonic pairs of different amplitude ratios than empirical mode decomposition (EMD). Furthermore, three simulations of multi-component signals were designed to investigate the use of SSD for signal decomposition. The SSD method was then applied to detect signatures caused by rotor oil film whirl in experimental signals and compared to both EMD and ensemble EMD (EEMD). The simulated analysis results reflect that SSD shows superiority to EMD and EEMD in inhibiting mode mixing and extracting the time-varying frequency components. The experimental analysis results demonstrate that the SSD based rotor failure detection method is an alternative method under both constant and variable speed conditions.

Sliding mode fault‐tolerant control of an octorotor using linear parameter varying‐based schemes

This study presents two fault-tolerant control (FTC) schemes for an octorotor UAV. The FTC schemes are based on an linear parameter varying system representation and utilises a combination of sliding-mode ideas and control allocation (CA) in order to take full advantage of the available redundant rotors in the octorotor configuration. A detailed synthesis procedure for the design of the two FTC schemes in the presence of uncertainty, as well as faults/failures, is presented. The first scheme is based on an online CA methodology where knowledge of the rotor effectiveness level has been used to redistribute the control signals to the healthy rotors. The second scheme assumes that this information is not available and uses a fixed CA structure even in the event of faults/failures. Although the synthesis process for the two schemes is different and they use different strategies to redistribute the control signals when faults/failures occur, both schemes involved the same `baseline' (sliding-mode) controller which does not need to be reconfigured. The difference is in the final physical control law where the CA matrix is defined. Simulation results on the full non-linear octorotor model are presented for the two different schemes in the presence of uncertainty, sensor noise as well as faults/failures. The simulation results for various fault/failure scenarios show no visible degradation in state tracking performance, highlighting the potential of the proposed schemes.

Combined rotor fault diagnosis in rotating machinery using empirical mode decomposition

Unbalance, misalignment, partial rub, looseness and bent rotor are one of the most commonly observed faults in rotating machines. These faults cause breakdowns in rotating machinery and create undesired vibrations while operating. In this study, an approach to detect combined fault of unbalance and bent rotors for advance detection of the features of the fault rotors diagnosis is proposed. Empirical mode decomposition (EMD) is used efficiently to decompose the complex vibration signals of rotating machinery into a known number of intrinsic mode functions so that the fault characteristics of the unbalanced and bowed shaft can be examined in the time-frequency Hilbert spectrum. A test bench of Spectra-Quest has been used for performing experiments to illustrate the unbalance and the bent rotor conditions as well as the healthy rotor condition. Analysis of the results shows the usefulness of proposed approach in diagnosing the unbalance and bowed fault of the shaft in rotating machinery.