Hilbert-Huang Transform in Fault Detection

Germán-Salló, Zoltán; Grif, Horatiu Stefan

doi:10.1016/j.promfg.2019.02.257

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…In recent times, there has been a rise in attention on the use of HHT. This includes many applications such as using HHT in the detection of motor faults 27 , detection of existing fractures in the movable mechanical arms 28 , and detection of distortions in ball bearings 29 , 30 . Belshein et al 24 tried to enhance the empirical mode decomposition in HHT for diagnosis of some faults inside the engine.…”

Section: Introductionmentioning

confidence: 99%

Using energy time–frequency of Hilbert Huang transform to analyze the performance of the variable valve timing engine

Mohammed

Haris

2022

Sci Rep

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In this study, a diagnosis method was successfully implemented to identify different sounds coming from individual mechanical parts within a group of engine moving parts controlled through a variable valve timing system. The novelty of this diagnosis method is in the determination of specific sounds coming from each part within this group when they are in good working condition and without any defects. This will facilitate in early detection of faults occurring on the parts, identified through changes in the sound wave energy. Through this study, this diagnosis method was validated in three ways, namely the consistency of the results with previous studies, the synchronization of sounds from mechanical parts in overlapping cases, and the cross-correlation of engine sound modes that results from analysis using the Hilbert Huang Transform. In this paper, the distribution of sound energy according to its frequencies was utilized to distinguish which of the engine combustion chambers of a Dodge Journey 2.4 was faulty. To conduct that, the noise-based test technique was selected to record the engine sound. The results show that there is a link between the RMS energy of the engine sound and the engine output torque.

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Section: Introductionmentioning

confidence: 99%

Using energy time–frequency of Hilbert Huang transform to analyze the performance of the variable valve timing engine

Mohammed

Haris

2022

Sci Rep

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“…The second layer parameters known as Antecedent parameters and parameters between third and fourth layer known as Conclusion parameters determine the performance of ANFIS. Standard ANFIS uses combination of LS and BP learning algorithm for tuning these parameters [33]. ANFIS utilizes K-Means clustering algorithm for fuzzy rules extraction.…”

Section: A Anfismentioning

confidence: 99%

“…Unlike WDT and FFT, the HH transform does not involve the concept of frequency resolution or time resolution, but introduces the concept of instantaneous frequency. More details regarding HH transform can be found in [32], [33].…”

Section: Hh Transformmentioning

confidence: 99%

A Hybrid Method Based on Optimized Neuro-Fuzzy System and Effective Features for Fault Location in VSC-HVDC Systems

Rohani

Koochaki

2020

IEEE Access

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One of the most important issues with HVDC systems are the occurrence of various faults that can lead to considerable electrical power losses, serious damage to expensive equipment and huge financial losses. Hence, it is highly required to design an accurate and automatic fault location method in HVDC systems for maintaining uninterrupted supply of energy and protecting sensitive equipment such as rectifiers and inverters. Accordingly, this paper proposes a new hybrid system based on adaptive neuro-fuzzy inference system (ANFIS) with optimal parameters and Hilbert-Huang (HH) transform for fault location in voltage sourced converter-HVDC (VSC-HVDC) systems. The proposed fault location method consists of three major sections. In the first section, HH transform is applied to extract new features from current signal. In the second part, ANFIS uses the extracted features to estimate the fault location in transmission lines. Learning algorithm determines the accuracy and efficiency of each machine-learning algorithm. In the third section of the developed system, enhanced version of particle swarm optimization (PSO) algorithm named chaotic dynamic weight PSO (CDWPSO) algorithm is implemented as learning algorithm to train the ANFIS. The developed fault detection and location system was tested on a VSC-HVDC system with 250 km length and the obtained results using MATLAB simulations have shown that combination of new features, and CDWPSO-based ANFIS has high accuracy in fault detection and location in VSC-HVDC systems. High fault location accuracy, robust performance of neuro-fuzzy system, optimal training of ANFIS, extraction of novel effective features from current signal and fault location only with six features are the main contribution of the developed system.

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“…The WT could provide a good resolution for both time and frequency domain that overcome the limitation of the STFT, thus the method is widely used for fault diagnosis [15,16]. In addition to Fourier transform and WT, the HHT is also an effective approach for motor vibration signal analysis, it can detect malfunctioning by revealing the instantaneous amplitude and nonlinear and nonstationary characteristics in the frequency content [17]. Furthermore, the vibration signal contaminated by signal noise could be filtered by using the wavelet packet decomposition (WPD), the energy of the WPD coefficients could be utilized to detect the rolling bearing failures effectively [18].…”

Section: Introductionmentioning

confidence: 99%

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

et al. 2021

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In recent years, the internet of things (IoT) represents the main core of Industry 4.0 for cyberphysic systems (CPS) in order to improve the industrial environment. Accordingly, the application of IoT and CPS has been expanded in applied electrical systems and machines. However, cybersecurity represents the main challenge of the implementation of IoT against cyber-attacks. In this regard, this paper proposes a new IoT architecture based on utilizing machine learning techniques to suppress cyber-attacks for providing reliable and secure online monitoring for the induction motor status. In particular, advanced machine learning techniques are utilized here to detect cyber-attacks and motor status with high accuracy. The proposed infrastructure validates the motor status via communication channels and the internet connection with economical cost and less effort on connecting various networks. For this purpose, the CONTACT Element platform for IoT is adopted to visualize the processed data based on machine learning techniques through a graphical dashboard. Once the cyber-attacks signal has been detected, the proposed IoT platform based on machine learning will be visualized automatically as fake data on the dashboard of the IoT platform. Different experimental scenarios with data acquisition are carried out to emphasize the performance of the suggested IoT topology. The results confirm that the proposed IoT architecture based on the machine learning technique can effectively visualize all faults of the motor status as well as the cyber-attacks on the networks. Moreover, all faults of the motor status and the fake data, due to the cyber-attacks, are successfully recognized and visualized on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization, thereby contributing to enhancing the decision-making about the motor status. Furthermore, the introduced IoT architecture with Random Forest algorithm provides an effective detection for the faults on motor due to the vibration under industrial conditions with excellent accuracy of 99.03% that is significantly greater than the other machine learning algorithms. Besides, the proposed IoT has low latency to recognize the motor faults and cyber-attacks to present them in the main dashboard of the IoT platform.

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Hilbert-Huang Transform in Fault Detection

Cited by 9 publications

References 12 publications

Using energy time–frequency of Hilbert Huang transform to analyze the performance of the variable valve timing engine

Using energy time–frequency of Hilbert Huang transform to analyze the performance of the variable valve timing engine

A Hybrid Method Based on Optimized Neuro-Fuzzy System and Effective Features for Fault Location in VSC-HVDC Systems

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

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