Rotor-bearing System Research Articles

Joint extraction-based multivariate chirp mode decomposition and its application to fault diagnosis of rotating machinery under variable speed conditions

Vibration signals from rotating machinery during variable speed conditions exhibits non-stationary characteristics, posing a challenge for fault diagnosis. When a machine fails, the vibration signal of any component connected to the bearing carries valuable information about the equipment’s status. Processing multichannel variable speed signals at the same time avoids losing part of information and improves fault diagnosis. Therefore, this study put up a novel approach called joint extraction-based multivariate chirp mode decomposition (JMCMD) to analyze multichannel signals of rotating machinery with varying speeds. The approach comprises the joint extraction framework and the multichannel signal instantaneous frequency initialization (MSIFI). First, JMCMD employs a joint component extraction framework, which can precisely and simultaneously extract complicated characteristic components close to each other in multiple channels. Second, this study extends the parameterized demodulation (PD) technique to multiple channels to find the initialized instantaneous frequency of multichannel signals. Through the joint extraction strategy’s integration with MSIFI, JMCMD is capable of efficaciously extracting all significant characteristic components of multichannel signals. Additionally, the high-resolution time–frequency representation (TFR) constructed from JMCMD’s output can clearly reveal the time-varying fault characteristic frequency of the rotor-bearing system. Simulation analysis and actual cases demonstrate that the suggested approach is an effective way to identify fault feature components, making it highly applicable in signal decomposition and fault detection.

Series solution of the Reynolds equation in finite-length tilting-pad journal bearing including pad deformation

This paper applies the variable separation method to the hydrodynamic pressure calculation of finite-length tilting pad journal bearings under Reynolds boundary conditions, and a series solution of the Reynolds equation is obtained based on the Sturm-Liouville theory, which considers pad deformation. The thermal effect is also taken into account. A comparison of the present solutions is made with the results obtained by other applied methods, as well as past and recent published data, and good agreement is noted. The steady-state and dynamic performance of a four-pad tilting pad journal bearing are investigated by the load capacity, pressure distribution, friction force, lubricant effective viscosity, journal center velocity, journal center orbits, and phase portrait under the proposed series solution of the Reynolds equation. This study contributes to quickly evaluating the nonlinear dynamic performance of a tilting pad journal bearing rotor system and supporting the nonlinear dynamic design of the system.

A rotor bearing system fault diagnosis method based on FSASCA-VMD and GraphSAGE-SA

Abstract In complex environments, signals from rotor bearing systems can easily be drowned out by strong noise, leading to low fault diagnosis accuracy. In order to reduce noise and improve diagnostic accuracy, this paper proposes a rotor bearing system fault diagnosis method based on FSASCA-VMD (Future Search Algorithm based on Sine Cosine Algorithm-Variational Mode Decomposition) and GraphSAGE-SA (Graph SAmple and aggreGatE-Self Attention). First, the FSASCA optimization algorithm is used to determine two parameters in VMD. The best combination found is then input into VMD to decompose the original signal. Then, filter the IMF (Intrinsic Mode Function) components with high correlation to the original signal using cumulative percentage kurtosis. Reconstruct the filtered IMF components to complete signal denoising. Finally, this paper utilizes graph theory and time series correlation to uncover the hidden relationships within the data. Utilizing the Euclidean distance as the edge weight between nodes, a graph model is established based on the topological structure of the PathGraph, and a GraphSAGE-SA fault diagnosis framework is constructed. Utilizing a self-attention mechanism to aggregate nodes enhances the weight allocation of important information. The experimental results indicate that the method proposed in this paper can effectively remove most of the noise in the signal while preserving a significant amount of useful information. In rotor bearing system fault diagnosis, the diagnostic accuracies of this method for simulated and laboratory signals are 98.33% and 98.56%, respectively, surpassing those of other methods.

Effect of balls’ quantities, preload and raceway diameters on rotation accuracy and vibration of rigid rotor-bearing system based on nonlinear dynamic model

The rotation accuracy and vibration of the bearing-rotor system are important evaluation indicators for the working accuracy of machine tools. In this work, an innovative nonlinear dynamic model of the ball bearing-rotor system is first developed by considering the slip, spin and gyroscopic effects of ball and the whirl motion of cage. Secondly, this developed model is validated through experimental methods. On this basis, the coordination of balls' quantities and preload is studied to obtain the desired rotation accuracy and vibration of rotor. The effect of raceway diameters on the rotation accuracy and vibration of rotor is investigated. The influence mechanism of the odd and even number of balls in the left and right bearings on the rotation accuracy and vibration is revealed. The analyzed results illustrate that the reasonable contact load of the ball by adjusting the preload of the bearing-rotor system or the balls’ quantity can prompt good rotation accuracy and low vibration of rotor, here, the maximum contact loads of 65 N should be adopted in the left and right bearings to select the preload. Additionally, odd or even balls assembled in the left and right bearings synchronously are conducive to obtaining a favorable trajectory and rotating accuracy of rotor.

Experimental study on operating characteristics of rotor-bearing system lubricated by gallium-based liquid metal

PurposeThe purpose of this paper is to explore the operating characteristics of gallium-based liquid metals (GLMs) by directly adding them as lubricants in real mechanical equipment.Design/methodology/approachThis paper conducts an analysis of the rotor-bearing system under GLM lubrication using a constructed test rig, focusing on vibration signals, surface characteristics of the friction pair, contact resistance and temperature rise features.FindingsThe study reveals that GLM can effectively improve the lubrication condition of the tribo-pair, leading to a more stable vibration signal in the system. Surface analysis demonstrates that GLM can protect the sample surface from wear, and phase separation occurs during the experimental process. Test results of contact resistance indicate that, in addition to enhancing the interfacial conductivity, GLM also generates a fluid dynamic pressure effect. The high thermal conductivity and anti-wear effects of GLM can reduce the temperature rise of the tribo-pair, but precautions should be taken to prevent oxidation and the loss of its fluidity.Originality/valueThe overall operating characteristics of the rotor-bearing system under GLM lubrication were investigated to provide new ideas for the lubrication of the rotor-bearing system.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0067/

Nonlinear vibration and stability analysis of a dual-disk rotor-bearing system under multiple frequency excitations

Nonlinear vibration and stability analysis of a dual-disk rotor-bearing system under multiple frequency excitations

Multivariate iterative nonlinear chirp mode decomposition: principles and applications

Abstract The rotor-bearing system (RBS) of a hydroelectric unit includes multiple components such as the rotor, rotor shaft, coupling, turbine, and main shaft. There is a strong coupling relationship between the vibration signals of multiple bearing sections. At the same time, the vibration signal of the RBS contains rich frequency components, which can effectively reflect the operating state of the shaft system of the unit. Therefore, synchronous signal analysis across multiple rotor bearing sections is required to completely grasp the operating status of the unit. In this study, the Multivariate Iterative Nonlinear Chirp Mode Decomposition (MINCMD) is proposed to analyze signals on multiple sections simultaneously. MINCMD follows the step-by-step extraction idea of Adaptive Multivariate Chirp Mode Decomposition (AMCMD), but it adopts a new algorithm framework by modifying the objective function. Furthermore, AMCMD does not converge well without a good initial instantaneous frequency (IF). Therefore, a method based on parameterized demodulation (PD) is introduced in MINCMD to solve the multi-channel frequency initialization problem. The effectiveness of the proposed algorithm is verified through a simulation test and real data from hydroelectric units.

An Integrated Dimension Theory and Modulation Signal Bispectrum Technique for Analyzing Bearing Fault in Industrial Fibrizer

Abstract This study investigates the dynamics of roller bearings by utilizing the dimension theory technique to diagnose the bearing clearance faults of revolving machines. The generation of local defects in rotating machines is closely related to the clearance behaviour of the rotor-bearing system. A dynamic model of bearing with dimension theory by matrix method (DTMM) is developed for characteristics of bearing clearance considering the influence of local defects on the inner and outer bearings races. The characteristics of bearing internal radial clearance considering the impact of the defect on the bearing are analyzed. An experimental study has been performed under various operational conditions. The noisy signal is subsequently eliminated using the Modulation Signal Bispectrum (MSB). The efficiency and reliability of the stated approach are evaluated using a specialized bearing test and a run-to-failure fibrizer test. As a result, this technology offers a significant opportunity to execute more cost-effective maintenance work to eliminate the breakdown of machinery.

Coupled modal characteristics of a bladed rotor bearing system under aerodynamic and rotor excitations: Modelling and numerical study

Bladed Rotor Bearing System is considered in the coupled vibration analysis among blades and the rotor system. The governing equations of motion are derived based on Lagrange’s equation, which accounts for the coupled vibrational modes. The model is discretised based on the Galerkin finite element method. A comparative analysis on the influence of uniform and pre-twisted blade configurations on the BRBS and on individual blades is carried out. Free vibration analysis for various blade geometry configurations is studied and results show that natural frequencies of the blades are reduced in the presence of pre-twist whereas the same is increased with double-taper. The Campbell diagram revealed the presence of frequency veering among the blades and rotor loci of natural frequencies for a specific blade configuration. The influence of aerodynamic and external excitations on the blades and the rotor, respectively, are considered in the forced response analysis, where the resulting effects of both the excitations on the individual blades and the BRBS for different blade configurations are studied.

Finite element simulation of iron losses for an Active Magnetic Bearing–Rotor system constructed of Hiperco laminations

Iron losses appear in Active Magnetic Bearings (AMBs) mostly because of the rotor's movement, but also because of the fluctuation of the control current in the stator's coils. They can be divided into three categories: the hysteresis losses, the eddy current losses and the excess losses and while they depend significantly on the rotating speed and the magnetic flux density applied on the poles, the most contributing factor is the magnetic material used for the core. In this paper, a 2-D Finite Element Method transient model is used to simulate the rotational motion of the shaft inside the AMB and calculate the iron losses that occur due to the alternating magnetic flux inside the rotor, as well as the mechanical load capacity on the vertical direction of the AMB for each case. A simulation is carried out, at first, for a constant control current value and a speed range of 0–30,000 rpm, followed by a second one, for constant rotational speed and control current values 0–0.5 A. Geometry remains the same for all simulations. When it comes to the materials selected for the stator and the rotor, the cases of Hiperco 27, Hiperco 50 and Hiperco 50 HS laminations are tested. The iron losses of the three alloys are compared to the losses of 3 % silicon-iron. The results show that the three iron cobalt alloys have significantly lower losses than the silicon iron for the same AMB size and rotor's speeds. Hiperco 50 has the lowest loss among the three Hiperco alloys, while Hiperco 50 HS provides slightly higher mechanical load capacity under the same operating conditions.

An investigation of dynamic characteristics in rotor-bearing system with combined bearing and coupling defects

An investigation of dynamic characteristics in rotor-bearing system with combined bearing and coupling defects

Study on the Vibration Characteristics of the Helical Gear-Rotor-Bearing Coupling System of a Wind Turbine with Composite Faults

As the core component of the wind turbine generation gearbox, the gear-rotor-bearing transmission system typically operates in harsh environments, inevitably leading to the occurrence of composite faults in the system, which exacerbates system vibration. Therefore, it is necessary to study the vibration characteristics of wind turbine helical gear-rotor-bearing transmission systems with composite faults. This paper uses an improved energy method to calculate the theoretical time-varying mesh stiffness of a helical gear with a root crack failure. On the premise of considering the time-varying meshing stiffness of the faulty helical gear, the gear eccentric fault, and the nonlinear support force of the faulty bearing, a multi-degree-of-freedom helical gear-rotor-bearing transmission system with compound faults was established by using the lumped parameter method. The dynamic model of the system was solved based on the Runge–Kutta method, and the vibration response of the system under healthy conditions, single faults with gear eccentricity, single faults with tooth root cracks, and coupled bearing composite faults were simulated and analyzed. The results show that the simulation results based on KISSsoft software 2018 version verify the effectiveness of the improved energy method; the existence of single faults and composite faults will cause the fault characteristics in the time domain and frequency domain responses. In this paper, the influence of a single fault and a complex fault on the time domain and frequency domain of the system is mainly discovered through the fault study of the helical rotor-bearing system, and the influence of the fault degree on the vibration of the gear motion system is discussed. The greater the degree of the fault, the more vibration of the system occurs; accordingly, when the system is under the coupling of tooth root crack and bearing fault, there is a significant difference compared with the healthy system and the single fault system. The system vibration has obvious time domain and frequency domain signal characteristics, including periodic pulse impacts caused by gear faults and time domain impact caused by bearing. The fault characteristic frequencies can also be found in the frequency domain. In this paper, the fault study of a helical gear of wind turbine generation provides a reference for the theoretical analysis of the vibration characteristics of the helical gear-rotor-bearing system under various fault conditions, lays a solid foundation for the simulation and subsequent diagnosis of the composite fault signal of the system, and provides help for the fault diagnosis of wind turbine gearboxes in the future.

Self-Calibration Method of Displacement Sensor in AMB-Rotor System Based on Magnetic Bearing Current Control

Displacement sensors are the key components of the active magnetic bearing rotor (AMB-rotor) system to realize the closed-loop control of the rotor displacement. The aging and failure of the displacement sensor components will lead to the changes of sensor sensitivity and zero point, which can directly affect the suspension performance of the rotor and even cause the instability of the AMB-rotor system. Therefore, it is significant to calibrate the sensor regularly. This paper proposes a self calibration method based on the detection and control of the magnetic bearing current without additional calibration equipment and hardware circuits. In addition, a new control structure of the AMB-rotor system is proposed to achieve efficient calibration, which can control the rotor displacement based on current following control method. Finally, experimental results verify the effectiveness of the proposed method on the displacement sensor calibration.

Sliding model control of active magnetic bearing rotor system based on state observer

This paper addresses a novel sliding mode control based on state observer for active magnetic bearing rotor system. Firstly, the state-space model of a radial AMB rotor system is established with considering unbalance disturbance and gyro effect for a vertical flywheel energy storage system. Then a sliding mode function and switching surface are constructed based on an observer. Meanwhile, a separation and decoupling strategy based on Finsler’s lemma is proposed. Through this method, the constraint relationship between the controller gain, active magnetic bearing matrices and the Lyapunov variables is eliminated. After that a method for chattering reduction in the sliding-mode controller is raised. Relied on these techniques, new sufficient conditions for the stability of AMB rotor system are given in the framework of linear matrix inequalities. Finally, the effectiveness of the proposed sliding mode controller is validated on the experimental platform of the flywheel energy storage system.

Experimental Research on Dynamic Characteristics of a Multi-Disc Rotor System Supported by Aerostatic Bearings

Gas bearings have the advantages of small friction loss, wide applicable speed range, no pollution, etc., and have important application prospects in micro and small high-speed rotating machinery. However, due to its compressibility and low viscosity, its dynamic stability in high-speed rotating machinery is the key to constraining its development. The experimental study of shaft system dynamics is the main means to explore the mechanism of rotor behavior. On the test platform of dynamic characteristics of multi-disc rotor system supported by aerostatic bearings, experimental research on the nonlinear dynamic characteristics of a rotor system was carried out, and nonlinear vibration test and analysis methods, such as axial orbits, bifurcation diagrams, and spectral characteristics, were adopted, and vibration phenomena, including the critical rotational speed accumulating energy and low-frequency accumulating energy, were presented and the vibration characteristics of bearing fracture faults were presented. The bearing supply pressure and rubber damping pad were introduced as a method to suppress the low-frequency vibration of the aerostatic bearing rotor system, and its vibration-reduction effect was verified by experiments. The above results can provide technical support for vibration control and fault diagnosis of rotor systems supported by aerostatic bearings.

The LPST-Net: A new deep interval health monitoring and prediction framework for bearing-rotor systems under complex operating conditions

Accurate and science-based prediction of bearing performance degradation has been a principal concern and a critical challenge issue in the sector of Prognostics and Health Management in the industry as the solution to promote the engineering system's reliability, availability, and maintainability. Deep Learning (DL) methods are currently a hotspot in Prognostics and Health Management. Nonetheless, with complex operating conditions, existing forecast models still inevitably suffer from three fatal flaws. Firstly, dynamic modern industrial systems and harsh operational environments make the degradation data of the bearing-rotor system highly stochastic and nonlinear. Secondly, in practice, the bearing-rotor system failure process will be governed by complex failure dynamics and degradation mechanisms, resulting in degradation data characterized by intense temporal order. Thirdly, deep learning models use a multi-layer or cellular design containing numerous weights and biases, generating more computational overhead. To fill this research gap, a new deep interval health monitoring and prediction framework named Lightweight Probabilistic Spatiotemporal (LPST-Net) is proposed, which is integrated with the concepts of lightweight and interval prediction and is capable of state monitoring and Remaining Useful Life (RUL) prediction for bearing-rotor systems under complex operating conditions. Mainly inspired by the improvement of the Gate Recurrent Unit (GRU), this paper designs a time series variable prediction algorithm and derives a new formulation named Weight Diminish Recurrent Unit (WDRU). It dramatically reduces the training parameters of the proposed LPST-Net framework and improves the convergence speed while ensuring prediction accuracy. The degradation data are obtained under 2 actual and complex operating conditions of bearing-rotor system unbalance and high temperature. The three metrics show that the proposed LPST-Net framework can achieve high-precision point prediction, suitable prediction intervals, and reliable probabilistic prediction results. It is also verified that the proposed LPST-Net framework has superior performance and more practical application value compared with seven mainstream methods, such as (b)Squeeze-WDRU-GPR-Net.

A gray texture image data-driven intelligent fault diagnosis method of induction motor rotor-bearing system under variable load conditions

Induction motors often operate in variable conditions, and the data distribution of diverse conditions is different. To solve the issue of low accuracy of diagnosis methods caused by large differences in data distribution, an intelligent fault diagnosis method of induction motor rotor-bearing system is proposed. The method combines convolutional neural network with adaptive batch normalization, and the gray texture images transformed from vibration signals are used as the input samples. A total of thirteen conditions including constant and time-varying loads are designed to verify the performance of the method. The average accuracy of the model for thirteen conditions of test set reaches 98.7 %, and the average accuracy for eight unknown conditions of test set is 99.0 %. The proposed method can extract features that are not affected by the variable load conditions, facilitate the transfer from no-load to on-load and known to unknown conditions.

Effects of interference fit variation on dynamic characteristics of full ceramic ball bearing rotor system

Bearing fit is one of the key aspects in determining the performance of FCBB (full ceramic ball bearing), which affects FCBB internal contact condition and support stiffness. In this paper, the interference fit value variations of FCBB induced by the centrifugal displacement and thermal deformation is calculated, the mapping relationship between interference fit and internal clearance is established, and the influence of preload and interference fit value on the dynamic characteristics of FCBB is discussed in detail. Finally, the vibration experiment of FCBB is verified by ABLT-9 bearing life testing machine. The research shows that interference fit value is the main factor affecting internal clearance. The increase of internal clearance will reduce the bearing radial stiffness and increase the non-uniformity of load distribution, while the preload will weaken the influence of internal clearance on radial stiffness and load distribution. The results provide a theoretical basis for improving the FCBB’s service performance and optimizing their assembly parameters.

A Novel Hybrid Approach to the Diagnosis of Simultaneous Imbalance and Shaft Bowing Faults in a Jeffcott Rotor-Bearing System

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalances and shaft bowing in these systems can lead to decreased efficiency and increased vibrations. The early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry over the years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) methods has become a trend, and in the foreseeable future the combination of AI with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as often insufficient fault data are available. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalances and shaft bowing are demonstrated) in a Jeffcott rotor. To overcome the lack of real-world labeled fault datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose imbalance and shaft bowing faults using the simulated and real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-faults, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

An integrated approach for response prediction in large-scale rotor-bearing system with local nonlinear joints based on FRF-based harmonic balance method

In the development and maintenance of gas turbines, the efficient computation to predict the responses of large-scale and complex rotating structures with local nonlinear joints is crucial. In this paper, a novel integrated strategy as a trade-off between time expense and accuracy for response prediction in large scale rotor-bearing system with local nonlinear joints is developed using the frequency response function based harmonic balance method (FRF-based HBM). To tackle the numerical challenges arising from asymmetrical matrices in rotating systems, a new approach is presented to compute the FRF using a spectral decomposition in second-order state space. Furthermore, the analytical mapping of eigen-solution against rotational speed based on Taylor series expansion is developed to address the computational expenses associated with speed-dependent FRF matrices. Then, a model reduction is adopted to mitigate the additional computational costs arising from the high-order unsymmetric matrices. The superior qualities and practical value in engineering of the new strategy are demonstrated on two numerical cases. The first is a one-dimensional rotor-bearing model subjected to the cubic nonlinear force under investigation, this approach shows an exceptionally high level of accuracy in response prediction, when compared to both the Runge-Kutta method and the harmonic balance method (HBM) without any form of model reduction. Another is a large-scale rotor-bearing model with two squeeze film dampers, the computational speed, being thousands of times faster than the HBM method and several times faster than the HBM considering the CMS model reduction, underscores the efficiency of this approach.