Article
Engineering, Mechanical
Emanuele Spinosa, Alessandro Iafrati
Summary: In this paper, a denoising strategy based on EEMD is proposed to reduce background noise in non-stationary signals. This method decomposes the signal into modes and reconstructs it using a thresholding technique to preserve sharp features. The validation on synthetic signals and actual test data shows that this method outperforms other classical filtering methods in noise reduction.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Biomedical
Anirban Dasgupta, Aurobinda Routray
Summary: The study aims to develop a new denoising method, PEMBE, which effectively removes noise and interference in EOG signals while preserving eye movement signatures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Mechanical
Rui Liu, Xiaoxi Ding, Yudong Zhang, Mingkai Zhang, Yimin Shao
Summary: This study proposes a novel variable-scale evolutionary adaptive mode denoising method (VEAMD) for weak feature enhancement of gearbox early fault diagnosis. The VEAMD method separates multiscale noise interference into different scales as reference bases for mode denoising, and then utilizes evolutionary digital filters to refine the reference filtering and obtain multiple intrinsic mode subsignals (IMSs). The Pearson correlation coefficients between two parts of each subsignal pair are employed as a weight to synthesize a denoised signal. Simulation and experiment results demonstrate the superiority of VEAMD over other methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Jing Yuan, Chong Xu, Qian Zhao, Huiming Jiang, Yihang Weng
Summary: The safe operation of rotating machinery relies on accurately and timely capturing various damages and fault signatures, which is challenging due to multiple and weak faults. This paper proposes a high-fidelity noise-reconstructed empirical mode decomposition (HNEMD) method to overcome the limitations of existing methods and achieve accurate signature extraction.
Article
Engineering, Multidisciplinary
Jinde Zheng, Miaoxian Su, Wanming Ying, Jinyu Tong, Ziwei Pan
Summary: The study introduces the improved Uniform Phase Empirical Mode Decomposition (IUPEMD) method, which enhances the accuracy and performance of signal decomposition by adaptively selecting the amplitude of the sinusoidal wave and choosing the optimal result based on orthogonality index.
Article
Environmental Sciences
Bing Yang, Zhiqiang Yang, Zhen Tian, Pei Liang
Summary: In this study, two combined methods, CEEMD & WD and VMD & WD, were proposed to reduce flicker noise in GPS positioning time series. The results showed that the combined methods outperformed CEEMD and VMD alone in reducing flicker noise.
Article
Engineering, Mechanical
Chen Yin, Yulin Wang, Heow Pueh Lee, Jianliang He, Yan He, Yuxin Sun
Summary: The improved ensemble noise-reconstructed EMD (IENEMD) proposed in this paper effectively addresses the issues of mode mixing and noise interference in wear detection. Results from comparative studies on numerical simulations and experiments demonstrate the superior performance of the IENEMD method in processing signals with low signal-to-noise ratio (SNR).
Article
Engineering, Electrical & Electronic
Wenchao Miao, Qi Xu, K. H. Lam, Philip W. T. Pong, H. Vincent Poor
Summary: Protection devices are crucial in DC systems, but series arc faults may not be detected by conventional devices, leading to malfunctions and fire hazards. This paper proposes a series arc-fault detection system based on modified EMD technique and SVM algorithm for reliable and efficient operation of DC systems. The effectiveness of arc-fault detection is significantly improved by acquiring accurate arc signatures without predefining various thresholds.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Yun Li, Jiwen Zhou, Hongguang Li, Guang Meng, Jie Bian
Summary: In this article, a fast and adaptive empirical mode decomposition method (FAEMD) is proposed to address the limitations of the original EMD method, such as differences in white noise amplitude, number of trials, and low computational efficiency. FAEMD combines the advantages of the order statistics filter (OSF) with the original EMD to effectively extract key feature information from fault signals with low calculation cost.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Liansheng Liu, Zhuo Zhi, Yufei Yang, Shervin Shirmohammadi, Datong Liu
Summary: The condition of the harmonic reducer is important for the availability of industrial robots. A fault detection method utilizing the acoustic emission (AE) signal is proposed, consisting of two unique algorithms. This method effectively reduces noise and achieves fault detection through feature extraction and signal optimization.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Business, Finance
Kunliang Xu, Weiqing Wang
Summary: A reliable crude oil price forecast is crucial for market pricing. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms to improve accuracy. The results show that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed, but the rolling EEMD-denoising model has an advantage for long-term forecasting.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
Jian Shen, Xiaowei Zhang, Gang Wang, Zhijie Ding, Bin Hu
Summary: Depression is a serious mental disorder that is predicted to become a major threat to life in the near future. This study proposes an improved feature extraction method based on Singular Value Decomposition (SVD) to accurately extract features from Electroencephalogram (EEG) signals for depression detection. The experimental results show that the proposed method achieves high classification accuracy on four EEG databases, comparable to the existing method based on Empirical Mode Decomposition (EMD).
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Engineering, Chemical
Seunghwan Jung, Minseok Kim, Baekcheon Kim, Jinyong Kim, Eunkyeong Kim, Jonggeun Kim, Hyeonuk Lee, Sungshin Kim
Summary: In manufacturing processes using CNC machines, machine tool failures can significantly degrade product quality and process efficiency. Existing fault detection methods using univariate signals have limitations in applying multivariate models. This study proposes a method combining empirical mode decomposition and auto-associative kernel regression to detect faults in machine tools. Experimental results demonstrate the successful detection of actual machine tool faults using this method.
Article
Engineering, Multidisciplinary
Asma Alsadat Mousavi, Chunwei Zhang, Sami F. Masri, Gholamreza Gholipour
Summary: Signal processing is crucial in vibration-based approaches and damage detection for structural health monitoring. The complete ensemble empirical mode decomposition with adaptive noise technique shows superior robustness and sensitivity in addressing damage location, classification, and detection compared to other decomposition techniques.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Han Zhou, Ping Yan, Qin Huang, Yanfei Yuan, Jie Pei, Yong Yang
Summary: The acquired hob vibration signals in the industrial environment are contaminated by noise, which reduces the accuracy of feature extraction and hob wear identification. To solve this problem, a novel denoising and feature enhancement method, CEEMDAN-FRS, is proposed based on CEEMDAN and FRS. The method achieves better performance in terms of effective feature enhancement, noise removal, and signal-to-noise ratio improvement.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)