A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm

Liu, Hanzhong; Qin, Chaoxuan; Liu, Ming

doi:10.3390/s19153300

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…In addition to the use of deep learning algorithms, other feature extraction algorithms can be used for pre-processing the signals before classification, such as EMD and WL [14]. This is in addition to other different techniques, such as the use of video images in analysing the movements of large structures [15].…”

Section: Review Of the Contributions In This Special Issuementioning

confidence: 99%

Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

Abbod

Shieh

2019

Sensors

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Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.

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Section: Review Of the Contributions In This Special Issuementioning

confidence: 99%

Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

Abbod

Shieh

2019

Sensors

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“…Due to the complex and multidisciplinary nature of the monitored systems, the FDI task on EMA systems is particularly challenging, as different failure modes interact and acceptable accuracy is hardly achieved. A wide choice of FDI techniques is nowadays available in literature: direct comparison of the system response with an appropriate monitoring model [4,5], spectral analysis of system-specific behaviors [6][7][8], artificial neural networks [9][10][11][12], or several combinations of some of these methods [13,14]. Typically, model-based approaches are more computationally expensive and require proper system knowledge but often give more accurate results than data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

A novel model-based metaheuristic method for prognostics of aerospace electromechanical actuators equipped with PMSM

Matteo

Vedova

Berri

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The prior knowledge of incipient failures of primary flight command electromechanical actuators (EMAs) with prognostic algorithms can be very beneficial. Indeed, early and proper detection and interpretation of the deterioration pattern can warn for replacing the servomechanism before the actual manifestation of the abnormal behaviour. Furthermore, such algorithms often exploit a model-based approach established on the direct comparison between the actual (High Fidelity) and the monitor (Low Fidelity) systems to identify fault parameters through optimization processes. The monitor model allows the acquisition of accurate and precise results with a contained computational effort. The authors developed a new simplified monitor model capable of faithfully reproducing the dynamic response of a typical aerospace EMA equipped with a Permanent Magnet Sinusoidal Motor (PMSM). This digital twin senses mechanical and electrical faults: friction, backlash, coil short circuit, static rotor eccentricity, and proportional gain. Fault detection and identification task are performed by comparing the output signal of the reference system (real or simulated) with the one obtained from the monitor model. After that, the Genetic Algorithm is chosen as the optimization algorithm to match the two signals by iteratively changing the fault parameters to detect the global minimum of a quadratic error function. Once a suitable fit is obtained, the corresponding optimization parameters are correlated with the considered progressive failures to evaluate the system’s health status. The high-fidelity reference models analysed in this work have been previously conceived, developed, implemented in Matlab-Simulink, and validated experimentally by researchers of the ASTRA group of the DIMEAS of Politecnico di Torino.

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“…Empirical mode decomposition (EMD) is able to decompose the signal into some intrinsic mode functions (IMFs) [12][13][14]. Each IMF component can be an amplitude-modulated (AM) or frequency-modulated (FM) signal [15]. Therefore, the rub-impact information can be extracted from each IMF [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Strategy for Signal Denoising Using Two-Layer SVD and Its Application to Rub-Impact Fault Diagnosis of Aeroengine

Zhang

et al. 2022

Applied Sciences

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The rub-impact fault, which occurs in the aeroengine, can cause frequency modulation (FM) in its vibration signal. Oscillated instantaneous frequency (IF) is a phenomenon of such a modulation mode, which carries critical fault information from the aeroengine. In this paper, by using Variational Mode Decomposition (VMD), the vibration signal can be decomposed into several signal components. Two-layer Singular Value Decomposition (SVD) is utilized to reduce the signal noise of each signal component and instantaneous frequency of each signal component. The intra–wave FM feature of the signal are presented through the Hilbert transform (HT) analysis. The proposed method is validated with numerical simulation and then demonstrated on aeroengine data with the rub-impact fault. The proposed method is suitable for the analysis of modulated signals and is a valid tool for detecting the rub-impact faults in aeroengines.

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A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm

Cited by 10 publications

References 27 publications

Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

A novel model-based metaheuristic method for prognostics of aerospace electromechanical actuators equipped with PMSM

A Novel Strategy for Signal Denoising Using Two-Layer SVD and Its Application to Rub-Impact Fault Diagnosis of Aeroengine

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