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Journal of Dynamics, Monitoring and Diagnostics 2024年第1期3卷 49-58页

作者： Khalid M.Almutairi Jyoti K.Sinha Dynamics Laboratory School of EngineeringThe University of ManchesterManchester M139PLUK

In any industry,it is the requirement to know whether the machine is healthy or not to operate machine *** the machine is not healthy then what is the fault in the machine and then finally its *** paper is proposing a 3-Steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance *** Step-1 identifies whether machine is healthy or faulty,then Step-2 detect the type of defect and finally its location in *** method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing *** method uses the optimised vibration parameters and a simple Artificial Neural Network(ANN)-based Machine Learning(ML)model from the earlier *** model is initially developed,tested and validated on an experimental rotating rig operating at a speed above 1st critical *** proposed method and model are then further validated at 2 different operating speeds,one below 1st critical speed and other above 2nd critical *** machine dynamics are expected to be significantly different at these *** highlights the robustness of the proposed 3-Steps method.

关键词： bearing faults fault diagnosis machine learning rotating machines rotor faults vibration analysis

Control of seismic and operational vibrations of rotating machines using semi-active mounts

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Earthquake Engineering and Engineering Vibration 2004年第1期3卷 85-100页

作者： R.Rana T.T.Soong Ove Arup & partners Department of Civil Structural.and Environmental EngineeringState University of New York

A dual isolation problem for rotating machines consists of isolation of housing structures from the machine vibrations and protection of machines during an earthquake to maintain their functionality. Desirable characteristics of machine mounts for the above two purposes can differ significantly due to difference in nature of the excitation and performance criteria in the two situations. In this paper, relevant response quantities are identified that may be used to quantify performance and simplified models of rotating machines are presented using which these relevant response quantities may be calculated. Using random vibration approach with a stationary excitation, it is shown that significant improvement in seismic performance is achievable by proper mount design. Results of shaking table experiments performed with a realistic setup using a centrifugal pump are presented. It is concluded that a solution to this dual isolation problem lies in a semi-active mount capable switching its properties from ‘operation-optimum’ to ‘seismic-optimum’ at the onset of a seismic event.

关键词： rotating machines simple modeling seismic control operational control semi-active control

Orthogonal On-Rotor Sensing Vibrations for Condition Monitoring of rotating machines

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Journal of Dynamics, Monitoring and Diagnostics 2022年第1期1卷 29-36页

作者： Yuandong Xu Xiaoli Tang Guojin Feng Dong Wang Craig Ashworth Fengshou Gu Andrew D.Ball Dynamics Group Department of Mechanical EngineeringImperial College LondonLondonUK School of Engineering and Technology Aston UniversityBirminghamUK School of Mechanical Engineering Hebei University of TechnologyTianjinChina State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong UniversityChina Control System Integration Ltd LittleboroughUK Centre for Efficiency and Performance Engineering University of HuddersfieldHuddersfieldUK

Thanks to the fast development of micro-electro-mechanical systems(MEMS)technologies,MEMS accelerometers show great potentialities for machine condition *** overcome the problems of a poor signal to noise ratio(SNR),complicated modulation,and high costs of vibration measurement and computation using conventional integrated electronics piezoelectric accelerometers,a triaxialMEMS accelerometer-based on-rotor sensing(ORS)technology was developed in this *** wireless data transmission capability,the ORS unit can be mounted on a rotating rotor to obtain both rotational and transverse dynamics of the rotor with a high *** orthogonal outputs lead to a construction method of analytic signals in the time domain,which is versatile in fault detection and diagnosis of rotating *** case studies based on an induction motor were carried out,which demonstrated that incipient bearing defect and half-broken rotor bar can be effectively diagnosed by the proposed measurement and analysis ***,vibration signals from translational on-casing accelerometers are less capable of detecting such *** demonstrates the superiority of the ORS vibrations in fault detection of rotating machines.

关键词： On-rotor sensing vibration condition monitoring rotating machines

Active Vibration Control of rotating machines with Active Machine Foot Mounts on Soft Foundations Based on a 3D-Model

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Journal of Applied Mathematics and Physics 2021年第1期9卷 57-87页

作者： Ulrich Werner Faculty of Electrical Engineering Precision Engineering Information Technology Nuremberg Tech Georg Simon Ohm University of Applied Sciences Nuremberg Germany

The paper presents a simplified 3D-model for active vibration control of rotating machines with active machine foot mounts on soft foundations, considering static and moment unbalance. After the model is mathematical described in the time domain, it is transferred into the Fourier domain, where the frequencies response functions regarding bearing housing vibrations, foundation vibrations and actuator forces are derived. Afterwards, the mathematical coherences are described in the Laplace domain and a worst case procedure is presented to analyze the vibration stability. For special controller structures in combination with certain feedback strategies, a calculation method is shown, where the controller parameters can be directly implemented into the stiffness matrix, damping matrix and mass matrix. Additionally a numerical example is presented, where the vibration stability and the frequency response functions are analyzed.

关键词： Active Vibration Control rotating machines Actuators Steel Frame Foundation

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of machines With Incorrect Annotation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 932-945页

作者： Bin Yang Yaguo Lei Xiang Li Naipeng Li Asoke K.Nandi the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System the School of Mechanical EngineeringXi’an Jiaotong UniveristyXi’an 710049 the Hunan Provincial Key Labortory of Health Maintenance for Mechanical Equipment Hunan Univeristy of Science and TechnologyXiangtan 411201China the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniveristyXi’an 710049China the Department of Electronic and Electrical Engineering Brunel University LondonUxbridge UB83PHUK

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source ***,in engineering scenarios,achieving such high-quality label annotation is difficult and *** incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled *** overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three ***,a residual network with dual classifiers is to learn features from cross-domain ***,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source *** the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised ***,the adaptation trajectories are designed for sample distributions across *** ensures that the target domain samples are only adapted with the pure-labeled *** LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related *** results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

关键词： Deep transfer learning domain adaptation incorrect label annotation intelligent fault diagnosis rotating machines

A Novel Method Based on UNET for Bearing Fault Diagnosis

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Computers, Materials & Continua 2021年第10期69卷 393-408页

作者： Dileep Kumar Soother Imtiaz Hussain Kalwar Tanweer Hussain Bhawani Shankar Chowdhry Sanaullah Mehran Ujjan Tayab Din Memon National Centre of Robotics and Automation HHCMS LabMehran University of Engineering&TechnologyJamshoro76020SindhPakistan Department of Electrical Engineering DHA SUFFA UniversityKarachiSindhPakistan Department of Electronic Engineering Mehran University of Engineering and TechnologyJamshoro76020SindhPakistan

Reliability of rotating machines is highly dependent on the smooth rolling of ***,it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring *** the recent past,Deep Learning(DL)has become applicable in condition monitoring of rotating machines owing to its *** paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration *** proposed method is the UNET model that is a recent development in DL *** model is applied to the 2D vibration images obtained by transforming normalized amplitudes of the time-series vibration data samples into the corresponding vibration *** UNET model performs pixel-level feature learning using the vibration images owing to its unique *** results demonstrate that the model can perform dense predictions without any loss of label information,generally caused by the sliding window labelling *** comparative analysis with other DL models confirmed the superiority of the UNET model which has achieved maximum accuracy of 98.91%and F1-Score of 99%.

关键词： Condition monitoring deep learning fault diagnosis rotating machines vibration

NUMERICAL ANALYSIS OF DEFECTS IN THE ROTOR SUPPORTING STRUCTURE

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NUMERICAL ANALYSIS OF DEFECTS IN THE ROTOR SUPPORTING STRUCT...