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Journal articles on the topic 'Experimental diagnosis of drives'
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1
Rashid, Umair, Muhammad Asim Abbasi, Abdul Qayyum Khan, Muhammad Irfan, Muhammad Abid, and Grzegorz Nowakowski. "Robust Data-Driven Design for Fault Diagnosis of Industrial Drives." Electronics 11, no. 23 (November 23, 2022): 3858. http://dx.doi.org/10.3390/electronics11233858.
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Due to the presence of actuator disturbances and sensor noise, increased false alarm rate and decreased fault detection rate in fault diagnosis systems have become major concerns. Various performance indexes are proposed to deal with such problems with certain limitations. This paper proposes a robust performance-index based fault diagnosis methodology using input–output data. That data is used to construct robust parity space using the subspace identification method and proposed performance index. Generated residual shows enhanced sensitivity towards faults and robustness against unknown disturbances simultaneously. The threshold for residual is designed using the Gaussian likelihood ratio, and the wavelet transformation is used for post-processing. The proposed performance index is further used to develop a fault isolation procedure. To specify the location of the fault, a modified fault isolation scheme based on perfect unknown input decoupling is proposed that makes actuator and sensor residuals robust against disturbances and noise. The proposed detection and isolation scheme is implemented on the induction motor in the experimental setup. The results have shown the percentage fault detection of 98.88%, which is superior among recent research.
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R, Jyothi, Tejas Holla, Umesh NS, K. Uma Rao, and Jayapal R. "Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive." International Journal of Engineering and Advanced Technology 11, no. 1 (October 30, 2021): 174–80. http://dx.doi.org/10.35940/ijeat.a3173.1011121.
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AC drives are employed mainly in process plants for various applications. In most industrial applications, Induction motor drives are preferred as they are robust, reliable, and efficient. Process industries have seen a paradigm shift from manual control to automatic control. Advancements in power electronics technology have led to smooth control of the induction motor using variable frequency drives over an entire speed range. Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. Various electric faults that are incipient in the VFD cause an abrupt change in circuit parameters resulting in insulation damage, reduced efficiency, and leading to catastrophic failure of the entire system. Hence, continuous monitoring of the system parameters such as stator current, speed, and the vibration of the machine is essential to diagnose incipient faults in the system. AI techniques have been effectively used in the fault diagnosis of electrical systems. In the proposed work, simulation results of machine learning-based fault diagnosis techniques are presented. Real-time IoT-based condition monitoring of the Variable Frequency Drive is also implemented for enhanced fault diagnosis of various incipient electrical faults in AC drives. The experimental results obtained are validated with the simulation data.
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Kołodziejek, Piotr, and Daniel Wachowiak. "Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive." Energies 15, no. 3 (February 8, 2022): 1244. http://dx.doi.org/10.3390/en15031244.
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This paper presents the theoretical analysis and experimental verification of a direct fault harmonic identification approach in a converter-fed electric drive for automated diagnosis purposes. On the basis of the analytical model of the proposed real-time direct fault diagnosis, the fault-related harmonic component is calculated using recursive DFT (RDFT) and Goertzel DFT (GDFT), applied instead of the full spectrum calculations required in the most popular FFT algorithm. The simulation model of an inverter sensorlessly controlled induction motor drive is linked with the induction machine rotor fault model for testing the sensitivity of the GDFT- and RDFT-based fault diagnosis to state variable estimation errors. According to the presented simulation results, the accuracy of the direct identification of a fault-related harmonic is sensitive to the quality of fault harmonic frequency estimation. The sensitivity analysis with respect to RDFT and GDFT algorithms is included. Based on the experimental setup with a sensorlessly controlled induction motor drive with the investigated rotor fault, fault diagnosis algorithms were implemented in the microprocessor by integration with the control system in one microcontroller and experimentally verified. The RDFT and GDFT approach has shown accurate and fast direct automated fault identification at a significantly decreased number of arithmetical operations in the microcontroller, which is convenient for the frequency-domain fault diagnosis in electric drives and supports fault-tolerant control system implementation.
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Kumar, K. Vinoth, S. Suresh Kumar, and A. Immanuel Selvakumar. "Spectrum Analysis of Sidebands in Industrial Drives." International Journal of Measurement Technologies and Instrumentation Engineering 5, no. 2 (July 2015): 1–13. http://dx.doi.org/10.4018/ijmtie.2015070101.
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This paper deals with the diagnosis of induction motors (IM) with the so-called motor current signature analysis (MCSA). The MCSA is one of the most efficient techniques for the detection and the localization of electrical and mechanical failures, in which faults become apparent by harmonic components around the supply frequency. This paper presents a summary of the most frequent faults and its consequences on the stator current spectrum of an IM. A three-phase IM model was used for simulation taking into account in one hand the normal healthy operation and in the other hand the broken rotor bars, the shorted turns in the stator windings, the voltage unbalance between phases of supply and the abnormal behavior of load. The MCSA is used by many authors in literature for faults detection of IM. The major contribution of this work is to prove the efficiency of this diagnosis methodology to detect different faults simultaneously, in normal and abnormal functional conditions. The results illustrate good agreement between both simulated and experimental results.
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Tabet, Seddik, Adel Ghoggal, Hubert Razik, Ishaq Amrani, and Salah Eddine Zouzou. "Experimental and simulation investigation for rotor bar fault diagnosis in closed-loop induction motors drives." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2058–68. http://dx.doi.org/10.11591/beei.v12i4.4833.
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This research presents a comparative analysis of two broken rotor bar (BRB) fault identification techniques for closed-loop induction motors (IMs). Both motor current signal analysis and Hilbert transform (HT) rely on spectrum analysis by means of fast fourier transform (FFT). Both approaches have shown their ability to identify BRBs under varying loads. In contrast, the HT is deemed more efficient than the motor current signature analysis (MCSA) approach when the motor is working without load. To maintain a high-performance speed control and to compensate for BRBs effect on the mechanical speed, the approach of control used is direct torque control (DTC). Utilizing a real-time implementation in MATLAB/Simulink with the real-time interface (RTI) based on the dSPACE 1104 board, the efficacy of the two techniques was evaluated.
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Tabet, Seddik, Adel Ghoggal, Hubert Razik, Ishaq Amrani, and Salah Eddine Zouzou. "Experimental and simulation investigation for rotor bar fault diagnosis in closed-loop induction motors drives." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2058–68. http://dx.doi.org/10.11591/eei.v12i4.4833.
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This research presents a comparative analysis of two broken rotor bar (BRB) fault identification techniques for closed-loop induction motors (IMs). Both motor current signal analysis and Hilbert transform (HT) rely on spectrum analysis by means of fast fourier transform (FFT). Both approaches have shown their ability to identify BRBs under varying loads. In contrast, the HT is deemed more efficient than the motor current signature analysis (MCSA) approach when the motor is working without load. To maintain a high-performance speed control and to compensate for BRBs effect on the mechanical speed, the approach of control used is direct torque control (DTC). Utilizing a real-time implementation in MATLAB/Simulink with the real-time interface (RTI) based on the dSPACE 1104 board, the efficacy of the two techniques was evaluated.
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Saidi, Lotfi, Mohamed Benbouzid, Demba Diallo, Yassine Amirat, Elhoussin Elbouchikhi, and Tianzhen Wang. "Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine." Energies 13, no. 11 (June 5, 2020): 2888. http://dx.doi.org/10.3390/en13112888.
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Most electrical machines and drive signals are non-Gaussian and are highly nonlinear in nature. A useful set of techniques to examine such signals relies on higher-order statistics (HOS) spectral representations. They describe statistical dependencies of frequency components that are neglected by traditional spectral measures, namely the power spectrum (PS). One of the most used HOS is the bispectrum where examining higher-order correlations should provide further details and information about the conditions of electric machines and drives. In this context, the stator currents of electric machines are of particular interest because they are periodic, nonlinear, and cyclostationary. This current is, therefore, well adapted for analysis using bispectrum in the designing of an efficient condition monitoring method for electric machines and drives. This paper is, therefore, proposing a bispectrum-based diagnosis method dealing the with tidal stream turbine (TST) rotor blades biofouling issue, which is a marine environment natural process responsible for turbine rotor unbalance. The proposed bispectrum-based diagnosis method is verified using experimental data provided from a permanent magnet synchronous generator (PMSG)-based TST experiencing biofouling emulated by attachment on the turbine blade. Based on the achieved results, it can be concluded that the proposed diagnosis method has been very successful. Indeed, biofouling imbalance-related frequencies are clearly identified despite marine environmental nuisances (turbulences and waves).
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Papathanasopoulos, Dimitrios A., Konstantinos N. Giannousakis, Evangelos S. Dermatas, and Epaminondas D. Mitronikas. "Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives." Energies 14, no. 8 (April 16, 2021): 2248. http://dx.doi.org/10.3390/en14082248.
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A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed both under the healthy and faulty operating conditions to highlight the fault signature. Therefore, the second harmonic component of the vibration signal spectrum is evaluated as a reliable signature for the detection of misalignment faults, while the fourth harmonic component is investigated for the position sensor breakdown fault, considering both single and double sensor faults. As the fault signature is localized at these harmonic components, the Goertzel algorithm is promoted as an efficient tool for the harmonic analysis in a narrow frequency band. Simulation results of the system operation, under healthy and faulty conditions, are presented along with the experimental results, verifying the proposed technique performance in detecting the position sensor faults in a non-invasive manner.
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CHAHMI, Abdelghani. "Diagnosis of the Induction Machine Using Advanced Signal Processing Methods." Algerian Journal of Signals and Systems 3, no. 3 (September 15, 2018): 143–50. http://dx.doi.org/10.51485/ajss.v3i3.70.
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This work is a part of the thematic of monitoring and fault diagnosis of the squirrel cage three-phase induction machine. The choice of this type of machine is justified by the growing success it has exhibited, mainly, in the electric drives with variable speed. Signal based detection methods are presented is validated in simulation. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.In this study, the proposed approach considers the value of rotor resistance as fixed for condition monitoring. This value in the diagnostic tools which one uses is not fixed contrary to the classical approaches of control of machine.
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Abidova, E. A., P. V. Povarov, V. M. Popov, O. Yu Pugacheva, and V. Ya Shpicer. "Diagnostics of the drive of the control and protection system of the reactor plant ark type." Global Nuclear Safety, no. 2 (June 21, 2023): 79–87. http://dx.doi.org/10.26583/gns-2023-02-09.
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The drive of the reactor control and protection system (CPS) is a system of normal operation, important for safety. Malfunctions of the drives of the ARC type CPS are often the initial events for accidents leading to unauthorized downtime. The currently existing standard methods for monitoring the parameters of the CPS do not allow for a reliable assessment of the condition of the drives. In this paper, it is proposed to implement an approach that provides an increase in sensitivity when recognizing the states of the drives of the ARC control system by processing the initial signals, which are vibroacoustic signals registered on the lid of the upper unit of the VVER reactor. A procedure for processing diagnostic signals using singular spectrum analysis is proposed. The proposed approach differs from the known ones by the presence of a scaling procedure, which is implemented by multiplying the eigenvalue matrices by the gankelized matrices of the source data. Due to scaling, the necessary sensitivity increase is provided. The expected increase in sensitivity is based on the fact that the eigenvalues reflect the structure of the signals, which changes significantly under the influence of the defect. The proposed method was used to process vibroacoustic signals registered on the lid of the upper block of the VVER reactor unit. Experimental studies were carried out at the test stand of Izhorskiye Zavody OJSC. The results of processing experimental data indicate a high quality of diagnosis. The hypothesis was clearly confirmed that the difference in the structure of diagnostic signals of serviceable and faulty equipment can manifest itself in the eigenvalues of the gankel signal matrices. The approach proposed in the article to the processing of diagnostic signals is easily amenable to automation and can be implemented in the development of a diagnostic system for the drive of an ARC-type CPS.
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Chahmi, Abdelghani, Mokhtar Bendjebbar, Bertrand Raison, and Mohamed Benbouzid. "An Extender Kalman Filter-based Induction Machines Faults Detection." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 535. http://dx.doi.org/10.11591/ijece.v6i2.9387.
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This paper deals with the detection and localization of electrical drives faults, especially those containing induction machines. First, the context of the study is presented and an Extended Kalman Filter is described for induction machines fault detection. Then the modeling procedure under faulty conditions is shown, and the machine diagnosis methods are developed. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.Fault detection uses signal processing techniques in known operating phases (fixed speed), considering and locating malfunctions.
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Chahmi, Abdelghani, Mokhtar Bendjebbar, Bertrand Raison, and Mohamed Benbouzid. "An Extender Kalman Filter-based Induction Machines Faults Detection." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 535. http://dx.doi.org/10.11591/ijece.v6i2.pp535-548.
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This paper deals with the detection and localization of electrical drives faults, especially those containing induction machines. First, the context of the study is presented and an Extended Kalman Filter is described for induction machines fault detection. Then the modeling procedure under faulty conditions is shown, and the machine diagnosis methods are developed. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.Fault detection uses signal processing techniques in known operating phases (fixed speed), considering and locating malfunctions.
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Chao, Kuei-Hsiang, and Chen-Hou Ke. "Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives." Energies 13, no. 23 (November 29, 2020): 6302. http://dx.doi.org/10.3390/en13236302.
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This paper presents an extension theory-based assessment method to perform fault diagnosis for inverters in motor driving systems. First, a three-level neutral-point clamped (NPC) inverter is created using the PSIM software package to simulate faults for any power transistor in the NPC-type inverter. Fast Fourier transformation is used to transform the line current signals in the time domain into a spectrum in the frequency domain for analysis of the corresponding spectrum of features of the inverter for faults with different power transistors. Then, the relationships between fault types and specific spectra are established as characteristics for the extension assessment method, which is then used to create a smart fault diagnosis system for inverters. Fault-tolerant control (FTC) is used here when the rated output of a faulty inverter is decreased in order to maintain balanced output in three phases by changing the framework of the transistor connection. This is performed to reinforce the reliability of the inverter. Finally, by the simulation and experimental results, the feasibility of the proposed smart fault diagnosis system is confirmed. The proposed fault diagnosis method is advantageous due to its minimal use of data and lack of a learning process, which thereby reduces the fault diagnosis time and makes the method easily used in practice. The proposed fault-tolerant control strategy allows both online and smooth switching in the wiring structure of the inverter.
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Pietrzak, Przemyslaw, and Marcin Wolkiewicz. "Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms." Sensors 23, no. 4 (February 4, 2023): 1757. http://dx.doi.org/10.3390/s23041757.
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Reliable fault diagnosis and condition monitoring are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be subject to various types of damage during operation. Magnetic damage is a unique fault of PMSM and concerns the permanent magnet (PM) of the rotor. PM damage may be mechanical in nature or be related to the phenomenon of demagnetization. This article presents a machine learning (ML) based demagnetization fault diagnosis method for PMSM drives. The time-frequency domain analysis based on short-time Fourier transform (STFT) is applied in the process of PM fault feature extraction from the stator phase current signal. Moreover, two ML-based models are verified and compared in the process of the automatic fault detection of demagnetization fault. These models are k-nearest neighbors (KNN) and multiLayer perceptron (MLP). The influence of the input vector elements, key parameters and structures of the models used on their effectiveness is extensively analyzed. The results of the experimental verification confirm the very high effectiveness of the proposed method.
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Yanghong, Tan, Zhang Haixia, and Zhou Ye. "A Simple-to-Implement Fault Diagnosis Method for Open Switch Fault in Wind System PMSG Drives without Threshold Setting." Energies 11, no. 10 (September 26, 2018): 2571. http://dx.doi.org/10.3390/en11102571.
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The conversion system is a major contributor to failure rates. These faults lead to time and cost consuming. Fault diagnosis capabilities pay as a solver to achieve a steady system. This paper presents a full analysis of permanent magnet synchronous generator wind system (PMSGWS) and proposes a special RMS voltage-based fault diagnosis method. The full analysis presents a comprehensive knowledge of faulty behaviors especially under arm current flowing or cutting off. Due to enough knowledge of faulty behaviors, the implementation of the detection method without threshold setting is contributed by the special RMS voltage. Its sample period is set longer than the time of the maximum pulse width ratio (MPR) and shorter than the fault show time of lower tube voltage. Due to this, the detection speed and robustness are achieved. By these simple settings for the fault diagnosis method, the faulty switch is detected in less than 1/4 of the period. Simulation and experimental results confirm the validity and feasibility of the new proposed fault detection method.
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Pishnograev, Roman, Sergey Lukyanov, Oksana Logunova, Nikolay Shvidchenko, and Dmitriy Shvidchenko. "DIAGNOSIS OF THE ELECTRIC DRIVE OF THE DISCHARGE ROLLER CONVEYOR OF A WIDE-STRIP HOT MILL." Bulletin of the South Ural State University series "Power Engineering" 22, no. 1 (March 2022): 78–88. http://dx.doi.org/10.14529/power220109.
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Abstract. The purpose of the study is to develop an automated system for technical diagnostics of the state and regu-lation of the electric drive of the rollers of the collector roller table at a wide hot rolling mill based on change in the load currents of the rollers electric motors. The system allows the quality of finished rolled products and the produc-tivity of the mill to be enhanced due to the timely detection of faulty equipment and its rapid replacement based on the results of diagnostics. The following methods of diagnostics of the electric drive of the diverting roller table and its possible malfunctions which can negatively affect the quality of the finished rolled products were analyzed: experimental identification of the relation of the forms of change in the load currents of the electric motors of the rollers with specific types of malfunctions; determination of diagnostic signs of malfunctions in the values of change in the load currents of the electric motors of the rollers; creation of a mathematical model for calculating the forces of strip transportation; develop-ment of methods and algorithms for technical diagnostics of the electric drive of the collector roller table based on charac-teristics of change in the load currents of the electric motors of the rollers; development of a generalized algorithm for the operation of the automated technical diagnostics system; experimental evaluation of the effectiveness of the proposed methods and algorithms for diagnostics on the operating mill. Analytical methods of solving algebraic and differential equations and systems were used in the study. As a result of the research, the technical effectiveness of the methods and algorithms developed to diagnose the eccentricity of the roller barrel of the diverting roller table, malfunctioning of the brush-collector device of the roller electric motor, the destruction of the couplings in the roller electric drive line, malfunc-tioning of the bearing units in the roller electric drive line or the roller sides, the correct alignment of the roller relative to the technological plane of the diverting roller was experimentally confirmed. The diagnostic methods and algorithms de-veloped can be used to create diagnostic systems for electric drives of diverting roller table at operating mills during their reconstruction, as well as at newly built mills. The system thus developed was installed at the industrial plant's 2000 hot rolling mill.
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Zhang, Xi, Yiyun Zhao, Hui Lin, Saleem Riaz, and Hassan Elahi. "Real-Time Fault Diagnosis and Fault-Tolerant Control Strategy for Hall Sensors in Permanent Magnet Brushless DC Motor Drives." Electronics 10, no. 11 (May 25, 2021): 1268. http://dx.doi.org/10.3390/electronics10111268.
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The Hall sensor is the most commonly used position sensor of the permanent magnet brushless direct current (PMBLDC) motor. Its failure may lead to a decrease in system reliability. Hence, this article proposes a novel methodology for the Hall sensors fault diagnosis and fault-tolerant control in PMBLDC motor drives. Initially, the Hall sensor faults are analyzed and classified into three fault types. Taking the Hall signal as the system state and the conducted MOSFETs as the system event, the extended finite state machine (EFSM) of the motor in operation is established. Meanwhile, a motor speed observer based on the super twisting algorithm (STA) is designed to obtain the speed signal of the proposed strategy. On this basis, a real-time Hall sensor fault diagnosis strategy is established by combining the EFSM and the STA speed observer. Moreover, this article proposes a Hall signal reconstruction strategy, which can generate compensated Hall signal to realize fault-tolerant control under single or double Hall sensor faults. Finally, theoretical analysis and experimental results validate the superior effectiveness of the proposed real-time fault diagnosis and fault-tolerant control strategy.
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Kumar, Sujesh, M. Lokesha, M. V. Kiran Kumar, and C. G. Ramachandra. "Fault Diagnosis in Belt Transmission Using Wavelet Enveloped Spectrum." IOP Conference Series: Materials Science and Engineering 1013, no. 1 (January 1, 2021): 012025. http://dx.doi.org/10.1088/1757-899x/1013/1/012025.
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Abstract In the recent years, there is a need for the evolution of methodologies for vibrational analysis by condition monitoring. In this regard, wavelet analysis has become a tool to efficiently detect the features of any vibration signal. The application of wavelet analysis for finding fault in the belt drive is presented in this paper. Vibration signal has been obtained from a healthy and faulty belt drive using a belt drive testing apparatus for experimental studies. The fault diagnostic capability of wavelet analysis has been compared by processing an experimental data. The experiment is done for various working conditions of belt drive. The advantages of wavelet analysis is shown in comparison with Fast Fourier Transform.
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Sun, J., Gang Yu, and Chang Ning Li. "Bearing Fault Diagnosis Using Gaussian Mixture Models (GMMs)." Applied Mechanics and Materials 10-12 (December 2007): 553–57. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.553.
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This paper presents a novel method for bearing fault diagnosis based on wavelet transform and Gaussian mixture models (GMMs). Vibration signals for normal bearings, bearings with inner race faults, outer race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the vibration signals and to generate feature vectors. GMMs were trained and used as a diagnostic classifier. Experimental results have shown that GMMs can reliably classify different fault conditions and have a better classification performance as compared to the multilayer perceptron neural networks.
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Mendelman, Lisa. "Diagnosing Desire: Mental Health and Modern American Literature, 1890–1955." American Literary History 33, no. 3 (August 3, 2021): 601–19. http://dx.doi.org/10.1093/alh/ajab050.
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Abstract This second book project argues that psychological diagnosis drives literary and scientific innovation in the late nineteenth- and early twentieth-century US. I demonstrate how experimental modernism and biomedical development both deploy and resist evolving classifications of mental life. These underappreciated cultural dialogues generate authoritative models of cognitive and corporeal health determined by race and gender. I take up four such medicalized types and establish how these pathologized figures embody anxieties about social change, particularly related to race, gender, and sexuality. Synthesizing literary fiction with transatlantic medical discourse and computational methods with traditional archival practices, this project rethinks the cultural politics at work in biological schemas of wellness and disorder, while highlighting the stumbling blocks of interpretive practices shared by the sciences and the arts.
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Skowron, Maciej, Marcin Wolkiewicz, Teresa Orlowska-Kowalska, and Czeslaw T. Kowalski. "Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors." Energies 12, no. 12 (June 21, 2019): 2392. http://dx.doi.org/10.3390/en12122392.
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This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.
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Kumar, B. Raghu, K. V. Ramana, and K. Mallikharjuna Rao. "Fault Diagnosis for a Booster Pump Unit." International Journal of Mechanical Engineering Education 37, no. 4 (October 2009): 275–85. http://dx.doi.org/10.7227/ijmee.37.4.2.
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Condition monitoring and diagnostic engineering is a course that is well recognized by academics. It is offered to students across the globe who want exposure to instrumentation, data acquisition and processing. This paper introduces the technique of condition monitoring to budding engineers, from its conception to the present state of development, through a case study. An experimental investigation is highlighted in which the vibration condition of booster pump unit is monitored; the unit is a part of boiler feed pump train of a large thermal power plant. The booster pump is driven by a 3500 kW, 1440 rpm motor. The motor, along with the booster pump, is supported by four bearings. Tri-axial measurements were made at the bearing supports for 12 months. Root mean square values of displacement and velocity were measured along the horizontal, vertical and axial directions. The experimental data are plotted on a time domain for graphical analysis. Based on the experimental data, faults are diagnosed using ISO standards and causes are predicted. It is observed that the front and rear bearings of the booster pump experienced excess vibration. The work is concluded by way of suggesting remedial measures to ensure the vibration intensity at the said points falls within safe limits.
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Pietrzak, Przemyslaw, and Marcin Wolkiewicz. "Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis." Sensors 22, no. 24 (December 10, 2022): 9668. http://dx.doi.org/10.3390/s22249668.
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Permanent magnet synchronous motors (PMSMs) have become one of the most important components of modern drive systems. Therefore, fault diagnosis and condition monitoring of these machines have been the subject of many studies in recent years. This article presents an intelligent stator current-data driven PMSM stator winding fault detection and classification method. Short-time Fourier transform is applied in the process of fault feature extraction from the stator phase current symmetrical components signal. Automation of the fault detection and classification process is carried out with the use of three selected machine learning algorithms: support vector machine, naïve Bayes classifier and multilayer perceptron. The concept and online verification of the original intelligent fault diagnosis system with the potential of a real industrial deployment are demonstrated. Experimental results are presented to evaluate the effectiveness of the proposed methodology.
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Li, Mingfei, Zhengpeng Chen, Jiangbo Dong, Kai Xiong, Chuangting Chen, Mumin Rao, Zhiping Peng, Xi Li, and Jingxuan Peng. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems." Energies 15, no. 7 (March 31, 2022): 2556. http://dx.doi.org/10.3390/en15072556.
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In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.
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de Avelar, Daniel Moreira, Camila Chaves Santos, and Alice Fusaro Faioli. "Developments in Leishmaniasis diagnosis: A patent landscape from 2010 to 2022." PLOS Global Public Health 3, no. 11 (November 1, 2023): e0002557. http://dx.doi.org/10.1371/journal.pgph.0002557.
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The current study aims to contribute to the understanding of leishmaniasis diagnosis by providing an overview of patent filings in this field and analyzing whether the methods revealed are consistent with the needs described by the scientific community, in special the main gaps detected by the World Health Organization’s 2021–2030 Roadmap for Neglected Tropical Diseases. To this aim, a patent search was carried out focusing on documents disclosing leishmaniasis diagnostic methods supported by experimental evidence and with earliest priority date from 2010 onwards. Our results show that patenting activity is low and patent families are often formed by individual filings. Most R&D activity occurs in Brazil, which is also the main market of protection. Brazilian academic institutions are the main patent drivers, and collaboration between different institutions is rare. Most patent families describe immunological methods based on ELISA assays, using antibodies directed to K39 and homologues. kDNA is the primary gene for molecular testing. Experimental evidence of test performance in fulfilling critical diagnostic gaps is usually absent. The patent scenario suggests that leishmaniasis diagnostic gaps need to be more closely addressed to drive innovation directed to the control and/or elimination of leishmaniasis. From the public policy point of view, the following strategies are suggested: (i) strengthening collaborative networks, (ii) enhancing the participation of the private sector, and (iii) increasing funding, with special focus on the remaining diagnostic gaps.
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Arca, Alejandro A., Kaitlin M. Stanford, and Mustapha Mouloua. "Effects of Individual Differences, Attention, and Memory Deficits on Driver Distraction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1196–201. http://dx.doi.org/10.1177/1071181320641285.
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The current study was designed to empirically examine the effects of individual differences in attention and memory deficits on driver distraction. Forty-eight participants consisting of 37 non-ADHD and 11 ADHD drivers were tested in a medium fidelity GE-ISIM driving simulator. All participants took part in a series of simulated driving scenarios involving both high and low traffic conditions in conjunction with completing a 20-Questions task either by text- message or phone-call. Measures of UFOV, simulated driving, heart rate variability, and subjective (NASA TLX) workload performance were recorded for each of the experimental tasks. It was hypothesized that ADHD diagnosis, type of cellular distraction, and traffic density would affect driving performance as measured by driving performance, workload assessment, and physiological measures. Preliminary results indicated that ADHD diagnosis, type of cellular distraction, and traffic density affected the performance of the secondary task. These results provide further evidence for the deleterious effects of cellphone use on driver distraction, especially for drivers who are diagnosed with attention-deficit and memory capacity deficits. Theoretical and practical implications are discussed, and directions for future research are also presented.
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Chang, Lien-Kai, Shun-Hong Wang, and Mi-Ching Tsai. "Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering." Energies 13, no. 17 (August 30, 2020): 4467. http://dx.doi.org/10.3390/en13174467.
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In recent years, many motor fault diagnosis methods have been proposed by analyzing vibration, sound, electrical signals, etc. To detect motor fault without additional sensors, in this study, we developed a fault diagnosis methodology using the signals from a motor servo driver. Based on the servo driver signals, the demagnetization fault diagnosis of permanent magnet synchronous motors (PMSMs) was implemented using an autoencoder and K-means algorithm. In this study, the PMSM demagnetization fault diagnosis was performed in three states: normal, mild demagnetization fault, and severe demagnetization fault. The experimental results indicate that the proposed method can achieve 96% accuracy to reveal the demagnetization of PMSMs.
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Panigrahy, Parth Sarathi, Deepjyoti Santra, and Paramita Chattopadhyay. "Decent fault classification of VFD fed induction motor using random forest algorithm." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 4 (July 20, 2020): 492–504. http://dx.doi.org/10.1017/s0890060420000311.
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AbstractA data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.
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Ocak, Hasan, and Kenneth A. Loparo. "HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings." Journal of Vibration and Acoustics 127, no. 4 (September 23, 2004): 299–306. http://dx.doi.org/10.1115/1.1924636.
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In this paper, we introduce a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. Features extracted from amplitude demodulated vibration signals from both normal and faulty bearings were used to train HMMs to represent various bearing conditions. The features were based on the reflection coefficients of the polynomial transfer function of an autoregressive model of the vibration signals. Faults can be detected online by monitoring the probabilities of the pretrained HMM for the normal case given the features extracted from the vibration signals. The new technique also allows for diagnosis of the type of bearing fault by selecting the HMM with the highest probability. The new scheme was also adapted to diagnose multiple bearing faults. In this adapted scheme, features were based on the selected node energies of a wavelet packet decomposition of the vibration signal. For each fault, a different set of nodes, which correlates with the fault, is chosen. Both schemes were tested with experimental data collected from an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor (Reliance Electric 2 HP IQPreAlert) driven mechanical system and have proven to be very accurate.
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Zhen, Cheng Gang, and Yin Yin Zhang. "Fault Diagnosis for Wind Turbines Based on Vibration Signal Analysis." Advanced Materials Research 354-355 (October 2011): 458–61. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.458.
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Fault diagnosis is an important technical method to improve the safety index and economic effectiveness of wind turbines, it also provide support to advanced maintenance and design in wind power equipment. In this paper, we have done vibration testing on two wind turbines, one is in normal, the other is with faulty, and then carried on comparative analysis of vibration signal to the experimental data, finally designed a fault diagnosis method for direct-driven wind turbine generators.
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Trabelsi, Mohamed, Eric Semail, and Ngac Ky Nguyen. "Experimental Investigation of Inverter Open-Circuit Fault Diagnosis for Biharmonic Five-Phase Permanent Magnet Drive." IEEE Journal of Emerging and Selected Topics in Power Electronics 6, no. 1 (March 2018): 339–51. http://dx.doi.org/10.1109/jestpe.2017.2719634.
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Kostomakhin, Mikhail, Nikolay Petrishchev, Alexander Sayapin, Efim Pestryakov, Vitalii Tseiko, and Nikolay Kostomakhin. "Experimental sample for diagnosis of hydraulic drive pumps of agricultural equipment by pressure pulsation coefficient." E3S Web of Conferences 458 (2023): 10013. http://dx.doi.org/10.1051/e3sconf/202345810013.
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It is well known that often hydraulic pumps are resource-limiting components of the units in whose design they are included, and the condition of the hydraulic pump determines the performance and efficiency of the entire unit. The purpose of the work was to develop a prototype of a diagnostic tool that provides monitoring of the technical condition of hydraulic pumps during operation. An analysis of the tools produced by the Federal Scientific Agro-Engineering Center VIM for diagnosing the technical condition of hydraulic pumps of agricultural machinery was carried out. The material of research was a prototype of a tool for diagnosing the technical condition of hydraulic units, which was based on the method of amplitude-phase characteristics for rapid assessment of the technical condition of pumps during operation. On the base of our research we came to conclusions that for further development of the technology and subsequent implementation of the presented prototype in the process of diagnosing hydraulic pumps by pulsation coefficient it is necessary to increase the controllability of hydraulic systems of agricultural machinery by installing diagnostic points for connecting pressure sensors. Also together with manufacturers of hydraulic units, develop criteria for nominal, permissible, limit states, characterized by both the supply coefficient KQ and the pulsation coefficient ɛ.
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Wang, Zeng Qiang, Hua Jie Zhang, Xu Hui Zhang, Xian Gang Cao, and Hong Wei Ma. "Fault Diagnosis of Bearing in Mechanical Drive System Using Wavelet Analysis." Applied Mechanics and Materials 303-306 (February 2013): 515–20. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.515.
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Exploring a proper wavelet base function to characterize fault information of mechanical equipment is a big problem when wavelet analysis is applied into the field of fault diagnosis.Improper wavelet base function can dilute fault information, which will cause difficulties in fault diagnosis , based on the principle that the wavelet domain oscillation waveform are similar to the detected signal components, a proper wavelet base function was selected; through wavelet transform on the acceleration signals of the fault bearing, the spectrum chart of fault signals and the frequency characteristics of fault bearing were obtained. The experimental result shows that this method has a great advantage over extracting character signals.
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Lu, Feng, Jipeng Jiang, and Jinquan Huang. "Gas Turbine Engine Gas-path Fault Diagnosis Based on Improved SBELM Architecture." International Journal of Turbo & Jet-Engines 35, no. 4 (December 19, 2018): 351–63. http://dx.doi.org/10.1515/tjj-2016-0050.
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Abstract Various model-based methods are widely used to aircraft engine fault diagnosis, and an accurate engine model is used in these approaches. However, it is difficult to obtain general engine model with high accuracy due to engine individual difference, lifecycle performance deterioration and modeling uncertainty. Recently, data-driven diagnostic approaches for aircraft engine become more popular with the development of machine learning technologies. While these data-driven methods to engine fault diagnosis tend to ignore experimental data sparse and uncertainty, which results in hardly achieve fast fault diagnosis for multiple patterns. This paper presents a novel data-driven diagnostic approach using Sparse Bayesian Extreme Learning Machine (SBELM) for engine fault diagnosis. This methodology addresses fast fault diagnosis without relying on engine model. To enhance the reliability of fast fault diagnosis and enlarge the detectable fault number, a SBELM-based multi-output classifier framework is designed. The reduced sparse topology of ELM is presented and utilized to fault diagnosis extended from single classifier to multi-output classifier. The effects of noise and measurement uncertainty are taken into consideration. Simulation results show the SBELM-based multi-output classifier for engine fault diagnosis is superior to the existing data-driven ones with regards to accuracy and computational efforts.
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Ouanas, Ali, Ammar Medoued, Mourad Mordjaoui, Abdesselam Lebaroud, and Djamel Sayad. "Fault diagnosis in yaw drive induction motor for wind turbine." Wind Engineering 42, no. 6 (June 10, 2018): 576–95. http://dx.doi.org/10.1177/0309524x18780379.
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Wind turbines are widely exploited throughout the world. The availability and reliability of offshore wind turbines are asking to impose a constant maintenance strategy. In this work, we propose a method that allows filtering the signal of the frequency inverter that feeds the yaw drive used in wind turbine. The redundant information is eliminated via discrete wavelet transform and empirical modal decomposition. The two types of faults are detected from the envelope of the Hilbert transform. The magnitude imbalance detection is carried out in the time domain. The root mean square values of the envelopes of the three-phase system have a good indicator for the fuzzy system to evaluate the severity of the defect. In the frequency domain, the signature of the broken bar fault is located in the low-frequency bandwidth. The harmonics appeared in the spectrum sensitive in amplitude and frequency to the variation of load. Experimental results have demonstrated the accuracy of the proposed method.
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Huynh, Thanh-Canh, The-Duong Nguyen, Duc-Duy Ho, Ngoc-Loi Dang, and Jeong-Tae Kim. "Sensor Fault Diagnosis for Impedance Monitoring Using a Piezoelectric-Based Smart Interface Technique." Sensors 20, no. 2 (January 16, 2020): 510. http://dx.doi.org/10.3390/s20020510.
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For a structural health monitoring (SHM) system, the operational functionality of sensors is critical for successful implementation of a damage identification process. This study presents experimental and analytical investigations on sensor fault diagnosis for impedance-based SHM using the piezoelectric interface technique. Firstly, the piezoelectric interface-based impedance monitoring is experimentally conducted on a steel bolted connection to investigate the effect of structural damage and sensor defect on electromechanical (EM) impedance responses. Based on the experimental analysis, sensor diagnostic approaches using EM impedance features are designed to distinguish the sensor defect from the structural damage. Next, a novel impedance model of the piezoelectric interface-driven system is proposed for the analytical investigation of sensor fault diagnosis. Various parameters are introduced into the EM impedance formulation to model the effect of shear-lag phenomenon, sensor breakage, sensor debonding, and structural damage. Finally, the proposed impedance model is used to analytically estimate the change in EM impedance responses induced by the structural damage and the sensor defect. The analytical results are found to be consistent with experimental observations, thus evidencing the feasibility of the novel impedance model for sensor diagnosis and structural integrity assessment. The study is expected to provide theoretical and experimental foundations for impedance monitoring practices, using the piezoelectric interface technique, with the existence of sensor faults.
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Yang, Huixin, Xiang Li, and Wei Zhang. "Interpretability of deep convolutional neural networks on rolling bearing fault diagnosis." Measurement Science and Technology 33, no. 5 (February 4, 2022): 055005. http://dx.doi.org/10.1088/1361-6501/ac41a5.
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Abstract Despite the rapid development of deep learning-based intelligent fault diagnosis methods on rotating machinery, the data-driven approach generally remains a ‘black box’ to researchers, and its internal mechanism has not been sufficiently understood. The weak interpretability significantly impedes further development and application of the effective deep neural network-based methods. This paper contributes to understanding the mechanical signal processing of deep learning on the fault diagnosis problems. The diagnostic knowledge learned by the deep neural network is visualized using the neuron activation maximization and the saliency map methods. The discriminative features of different machine health conditions are intuitively observed. The relationship between the data-driven methods and the well-established conventional fault diagnosis knowledge is confirmed by the experimental investigations on two datasets. The results of this study can benefit researchers on understanding the complex neural networks, and increase the reliability of the data-driven fault diagnosis model in real engineering cases.
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Rohajawati, Siti. "Optimizing Prediabetes Diagnosis Through Knowledge-Based Systems." Asian Journal of Engineering, Social and Health 3, no. 2 (February 6, 2024): 330–42. http://dx.doi.org/10.46799/ajesh.v3i2.244.
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The escalating global prevalence of prediabetes highlights the urgency of preventive measures, particularly given its association with increased age, obesity, and additional risk factors. Addressing this concern, the explainability component of Artificial Intelligence (AI) emerges as a valuable asset in diabetes prevention strategies. This study adopts an experimental design grounded in knowledge-based systems, utilizing the knowledge engineering method to craft a web-based health tool for diabetes diagnosis. The process encompasses acquisition, representation, validation, inferencing, and explanation phases. The online diagnostic tool not only facilitates self-diagnosis but also delivers conclusive findings and enables user registration. Practical solutions and preventive recommendations are offered, aligning with the overarching goal of diabetes prevention. The study identifies three operational phases – self-diagnosis, presentation of final findings, and member registration. To enhance the application's efficacy, the analysis provides constructive suggestions for future refinements and advancements. This research underscores the potential of AI-driven, explainable systems in contributing to the global effort to combat the rising prevalence of diabetes.
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Zhang, Liming, Lei Rong, Meng Jiao, Ling Li, and Yaohua Yu. "Research on Roller Status Diagnosis of CRDM Based on SWT and HHT." E3S Web of Conferences 252 (2021): 01053. http://dx.doi.org/10.1051/e3sconf/202125201053.
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For the control rod drive mechanism roller vibration signal’s characteristics of nonlinear and nonstationary. Based on actual equipment life experiment and roller fault experiment, a status diagnosis method of roller of control rod drive mechanism based on Semi-soft wavelet threshold and Hilbert-Huang transform is proposed. Firstly, semi-soft wavelet threshold method is used to reduce noise interference and the influence of end-point effect on empirical mode decomposition, and the improved Hilbert transform method is used to extract the fault characteristics of roller vibration signal. The experimental results show that the method can effectively eliminate the interference of noises and realize the status diagnosis of the roller.
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Tarchała and Wolkiewicz. "Performance of the Stator Winding Fault Diagnosis in Sensorless Induction Motor Drive." Energies 12, no. 8 (April 21, 2019): 1507. http://dx.doi.org/10.3390/en12081507.
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This paper deals with the diagnosis of stator winding inter-turn faults for an induction motor drive operating without a speed sensor in a speed-sensorless mode. The rotor direct field oriented control structure (DFOC) was applied, its reference current and voltage component values were analyzed, and their selected harmonics were applied as effective fault indicators. To ensure robust speed estimation, a sliding mode model reference adaptive system (SM-MRAS) estimator was selected. The influence of load torque, reference speed, proportional-integral (PI) controller parameters, and short-circuit current on fault diagnosis and speed estimation performance was verified. Experimental test results obtained for a 3 kW induction motor drive are included.
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Gmati, Badii, Amine Ben Rhouma, Houda Meddeb, and Sejir Khojet El Khil. "Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm." World Electric Vehicle Journal 15, no. 2 (February 5, 2024): 53. http://dx.doi.org/10.3390/wevj15020053.
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Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.
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Zhao, Genghong, Wen Cheng, Wei Cai, Xia Zhang, and Jiren Liu. "Leveraging Interpretable Feature Representations for Advanced Differential Diagnosis in Computational Medicine." Bioengineering 11, no. 1 (December 26, 2023): 29. http://dx.doi.org/10.3390/bioengineering11010029.
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Diagnostic errors represent a critical issue in clinical diagnosis and treatment. In China, the rate of misdiagnosis in clinical diagnostics is approximately 27.8%. By comparison, in the United States, which boasts the most developed medical resources globally, the average rate of misdiagnosis is estimated to be 11.1%. It is estimated that annually, approximately 795,000 Americans die or suffer permanent disabilities due to diagnostic errors, a significant portion of which can be attributed to physicians’ failure to make accurate clinical diagnoses based on patients’ clinical presentations. Differential diagnosis, as an indispensable step in the clinical diagnostic process, plays a crucial role. Accurately excluding differential diagnoses that are similar to the patient’s clinical manifestations is key to ensuring correct diagnosis and treatment. Most current research focuses on assigning accurate diagnoses for specific diseases, but studies providing reasonable differential diagnostic assistance to physicians are scarce. This study introduces a novel solution specifically designed for this scenario, employing machine learning techniques distinct from conventional approaches. We develop a differential diagnosis recommendation computation method for clinical evidence-based medicine, based on interpretable representations and a visualized computational workflow. This method allows for the utilization of historical data in modeling and recommends differential diagnoses to be considered alongside the primary diagnosis for clinicians. This is achieved by inputting the patient’s clinical manifestations and presenting the analysis results through an intuitive visualization. It can assist less experienced doctors and those in areas with limited medical resources during the clinical diagnostic process. Researchers discuss the effective experimental results obtained from a subset of general medical records collected at Shengjing Hospital under the premise of ensuring data quality, security, and privacy. This discussion highlights the importance of addressing these issues for successful implementation of data-driven differential diagnosis recommendations in clinical practice. This study is of significant value to researchers and practitioners seeking to improve the efficiency and accuracy of differential diagnoses in clinical diagnostics using data analysis.
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Hou, Liqun, and Zijing Li. "Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 02 (February 12, 2020): 47. http://dx.doi.org/10.3991/ijoe.v16i02.11953.
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Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.
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Jinxin, Dou, Yang Tongguang, Yu Xiaoguang, Xue Zhengkun, Liu Zhongxin, and Sun Jie. "Model-driven fault diagnosis of slant cracks in aero-hydraulic straight pipes." Advances in Mechanical Engineering 12, no. 9 (September 2020): 168781402095497. http://dx.doi.org/10.1177/1687814020954970.
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A model-driven fault diagnosis method for slant cracks in aero-hydraulic straight pipes is presented in this paper. First, fracture mechanics theory and the principle of strain energy release are used to derive an expression for the local flexibility coefficient of straight pipes with slant cracks. The inverse method of total flexibility is used to calculate the stiffness matrix of straight pipe elements with slant cracks. Second, the Euler-Bernoulli beam model theory is used in conjunction with the finite element method to construct a dynamic model of the cracked pipe. Finally, a contour map method is used to diagnose the slant crack fault and quantitatively determine the crack position and depth. Experimental results show that the proposed method can accurately and effectively identify a slant crack fault in aero-hydraulic pipelines.
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Cheng, Chi-Cheng, Yih-Tun Tseng, Cheng-Da Wu, and Der-Lin Wang. "Fault Diagnosis of a High-Speed Cam-Driven Pin Assembly System." Advances in Materials Science and Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/5917408.
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A cam-driven mechanical system applied for pin assembly of connectors of electrical devices is studied in this paper. Three cooperative cams are involved in the tasks of approaching, cutting, insertion, and restoring. In order to meet the demanded productivity growth, the operation speed tends to be elevated. However, high running speeds usually cause deficiencies of pin dropping and inaccurate positioning. Diagnosis is therefore conducted to explore their physical reasons so that modification of future mechanical design can be made. Frequency responses of experimental measurements show greater natural frequency and system stiffness caused by nonlinear dynamics for higher operation speed. It also appears that the clamping force is reduced and drift of the locked pin’s location is induced for higher running speed. In addition, separation of the fixture system induced by contact oscillation generates clearance larger than the thickness of the pin. Based on the mathematical models obtained from the technique of system identification, deeper insight of the mechanical system and future system improvement can be highly expected.
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Hashimoto, Masafumi, Yuuki Nakamura, and Kazuhiko Takahashi. "Fault Diagnosis and Fault-Tolerant Control of a Joystick-Controlled Wheelchair." Journal of Robotics and Mechatronics 20, no. 6 (December 20, 2008): 903–11. http://dx.doi.org/10.20965/jrm.2008.p0903.
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This paper presents a method of fault diagnosis and fault-tolerant control for a nonholonomic powered wheelchair. Hard faults of sensors and actuators in two drive/steering units of the wheelchair are handled. The fault diagnosis is based on the interacting multiple-model (IMM) estimator. In order to improve fault decisions, we implement mode probability averaging and heuristic decision-making rule in the IMM-based algorithm. A fault-tolerant controller designed based on Ackerman geometry enables safe motion of the wheelchair even if sensors and actuators have partially failed. Experimental results verify the proposed method.
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Zhu, Tie Bin, and Feng Lu. "A Data-Driven Method of Engine Sensor on Line Fault Diagnosis and Recovery." Applied Mechanics and Materials 490-491 (January 2014): 1657–60. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1657.
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Considering the requirements of convinced sensor measurements for engine control, a method of aircraft engine sensor on line fault diagnosis and recovery based on least squares support vector machine (LS-SVM) is proposed. First, sensor sets correlations are calculated and the sensor with high correlation is selected by correlation analysis. Then sensor LS-SVM prediction model is established with the sensor itself primary data series and used to sensor fault diagnosis. The sensor recovery module is obtained based on the LSSVM algorithm with the high correlated sensor set, and is activated as the sensor failure detected. Experimental results show that the engine sensor fault recognition rate is satisfied by the proposed method, and could be used to turbofan engine sensor fault diagnosis and data recovery.
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Borghesi, M., G. Sarri, C. A. Cecchetti, I. Kourakis, D. Hoarty, R. M. Stevenson, S. James, et al. "Progress in proton radiography for diagnosis of ICF-relevant plasmas." Laser and Particle Beams 28, no. 2 (June 2010): 277–84. http://dx.doi.org/10.1017/s0263034610000170.
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AbstractProton radiography using laser-driven sources has been developed as a diagnostic since the beginning of the decade, and applied successfully to a range of experimental situations. Multi-MeV protons driven from thin foils via the Target Normal Sheath Acceleration mechanism, offer, under optimal conditions, the possibility of probing laser-plasma interactions, and detecting electric and magnetic fields as well as plasma density gradients with ~ps temporal resolution and ~ 5–10 µm spatial resolution. In view of these advantages, the use of proton radiography as a diagnostic in experiments of relevance to Inertial Confinement Fusion is currently considered in the main fusion laboratories. This paper will discuss recent advances in the application of laser-driven radiography to experiments of relevance to Inertial Confinement Fusion. In particular we will discuss radiography of hohlraum and gasbag targets following the interaction of intense ns pulses. These experiments were carried out at the HELEN laser facility at AWE (UK), and proved the suitability of this diagnostic for studying, with unprecedented detail, laser-plasma interaction mechanisms of high relevance to Inertial Confinement Fusion. Non-linear solitary structures of relevance to space physics, namely phase space electron holes, have also been highlighted by the measurements. These measurements are discussed and compared to existing models.
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Zhou, Funa, Yi Yang, Chaoge Wang, and Xiong Hu. "Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree." Entropy 25, no. 4 (April 3, 2023): 606. http://dx.doi.org/10.3390/e25040606.
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Federated learning is an effective means to combine model information from different clients to achieve joint optimization when the model of a single client is insufficient. In the case when there is an inter-client data imbalance, it is significant to design an imbalanced federation aggregation strategy to aggregate model information so that each client can benefit from the federation global model. However, the existing method has failed to achieve an efficient federation strategy in the case when there is an imbalance mode mismatch between clients. This paper aims to design a federated learning method guided by intra-client imbalance degree to ensure that each client can receive the maximum benefit from the federation model. The degree of intra-client imbalance, measured by gain of a class-by-class model update on the federation model based on a small balanced dataset, is used to guide the designing of federation strategy. An experimental validation for the benchmark dataset of rolling bearing shows that a 23.33% improvement of fault diagnosis accuracy can be achieved in the case when the degree of imbalance mode mismatch between clients is prominent.
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de l’Etoile, Shannon K., Christopher Bennett, and Cengiz Zopluoglu. "Infant Movement Response to Auditory Rhythm." Perceptual and Motor Skills 127, no. 4 (May 9, 2020): 651–70. http://dx.doi.org/10.1177/0031512520922642.
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Rhythmic entrainment occurs when an auditory rhythm drives an internal movement oscillator, thus providing a continuous time reference that improves temporal and spatial movement parameters. Entrainment processes and outcomes are well known for adults, but research is lacking for infants who might benefit from diagnosis and treatment of irregular rhythms within biological, sensorimotor, cognitive, and social domains. The present study used a combination of inertial measurement units and custom-made software to determine the amount, tempo, and regularity of movement in 28 infants aged 6-10 months while they were exposed to silence, an irregular rhythmic cue, or a regular rhythmic cue with tempo changes. We also assessed changes in the infants’ movement parameters following a one-week rhythm training protocol. While results revealed no significant effect of auditory condition on amount or tempo of movement, infant movement was significantly more regular when infants were exposed to 120 bpm (beats per minute) than to an irregular rhythmic cue or a 10% faster rhythmic cue (132 bpm). Infants showed no notable changes in movement amount, tempo, or regularity following one week of training involving auditory and physical rhythm. Overall, infants seem to engage in spontaneous movements with or without auditory rhythm but may not show tempo sensitivity through their movements. Increased movement regularity suggests that 120 bpm may be a preferred tempo for infants, at which they are more likely to demonstrate well-timed movements that may reflect interval entrainment. Infants’ auditory-motor systems appear not to respond to a 1-week rhythm training protocol.