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Showing papers in "Mechanical Systems and Signal Processing in 2018"


Journal ArticleDOI
TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.

1,287 citations


Journal ArticleDOI
Yaguo Lei1, Naipeng Li1, Liang Guo1, Ningbo Li1, Tao Yan1, Jing Lin1 
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

1,116 citations


Journal ArticleDOI
TL;DR: An end-to-end method that takes raw temporal signals as inputs and thus doesn’t need any time consuming denoising preprocessing and can achieve high accuracy when working load is changed is proposed.

805 citations


Journal ArticleDOI
TL;DR: This article presents a systematic review of artificial intelligence based system health management with an emphasis on recent trends of deep learning within the field and demonstrates plausible benefits for fault diagnosis and prognostics.

740 citations


Journal ArticleDOI
TL;DR: In this article, a review of fault severity assessment of rolling bearing components is presented, focusing on data-driven approaches such as signal processing for extracting proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions.

453 citations


Journal ArticleDOI
TL;DR: By analyzing the kernels of the convolutional layers of DNCNN via NAM algorithm, it is found that these kernels act as filters and they become complex when the layers go deeper, which may help to understand what DNCNN has learned in intelligent fault diagnosis of machinery.

405 citations


Journal ArticleDOI
TL;DR: In this paper, the authors proposed a parameter-adaptive variational mode decomposition (VMD) method based on grasshopper optimization algorithm (GOA) to analyze vibration signals from rotating machinery.

347 citations


Journal ArticleDOI
TL;DR: The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.

321 citations


Journal ArticleDOI
TL;DR: In this paper, a detailed literature review focuses on dynamics-based gearbox fault modeling, detection and diagnosis, focusing on the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, and gearbox transmission path modeling and method validation.

315 citations


Journal ArticleDOI
TL;DR: A novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing and results confirm that the developed method is more effective than the traditional methods.

289 citations


Journal ArticleDOI
TL;DR: A review on different methods and techniques for gearbox condition monitoring in wind turbines aiming to increase lifetime expectancy of components while reducing operation and maintenance cost is gathered.

Journal ArticleDOI
TL;DR: A new indicator, Combined Squared Envelope Spectrum, is employed to consider all the frequency bands with valuable diagnostic information and to improve the fault detectability of the Autogram, and a thresholding method is also proposed to enhance the quality of the frequency spectrum analysis.

Journal ArticleDOI
TL;DR: In this article, a real-time dynamic path planning method for autonomous driving that avoids both static and moving obstacles is presented, which determines not only an optimal path, but also the appropriate acceleration and speed for a vehicle.

Journal ArticleDOI
TL;DR: In this paper, the authors reviewed and critically discussed the current progress of mechanical model development of RBR systems, and identified future trends for research, and summarized five kinds of rolling bearing models, namely, the lumped-parameter model, the quasi-static model, quasi-dynamic model, dynamic model, and the finite element (FE) model.

Journal ArticleDOI
TL;DR: In this article, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in order to improve the precision of navigation information, and the accuracy of the integrated navigation can be improved due to the reduction of the influence of environment noise.

Journal ArticleDOI
TL;DR: In this paper, a time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis.

Journal ArticleDOI
TL;DR: The proposed Adaptive Variational Mode Decomposition (AVMD) method has strong adaptability and is robust to noise and can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.

Journal ArticleDOI
TL;DR: In this article, a sparsity guided empirical wavelet transform is proposed to automatically establish Fourier segments required in the EWT for fault diagnosis of rolling element bearings, which can detect single and multiple railway axle bearing defects.

Journal ArticleDOI
TL;DR: This paper presents a novel approach to detect the milling chatter based on Variational Mode Decomposition (VMD) and energy entropy and shows that the proposed method can effectively detect the chatter.

Journal ArticleDOI
TL;DR: The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions and lays the foundation for tool wear monitoring in real industrial settings.

Journal ArticleDOI
TL;DR: A novel coordinated path following system (PFS) and direct yaw-moment control (DYC) of autonomous electric vehicles via hierarchical control technique is presented, and a pseudo inverse (PI) low-level control allocation law is designed to realize the tracking of desired external moment torque and management of the redundant tire actuators.

Journal ArticleDOI
TL;DR: In this paper, the spectral kurtosis can be decomposed into squared envelope and squared L2/L1 norm, and then extended to spectral Lp/Lq norm.

Journal ArticleDOI
TL;DR: In this paper, a generalized composite multiscale permutation entropy (GCMPE) method was proposed to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing.

Journal ArticleDOI
TL;DR: Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements.

Journal ArticleDOI
TL;DR: A novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes is proposed.

Journal ArticleDOI
TL;DR: Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

Journal ArticleDOI
TL;DR: In this paper, a matching synchrosqueezing transform (MSST) was proposed to improve the readability of the TF representation of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF).

Journal ArticleDOI
TL;DR: Support vector machine (SVM) is combined with other methods, such as phase space reconstruction, wavelet analysis and particle swarm optimization, to build the prediction model of dam deformation, which demonstrates the modeling efficiency and forecasting accuracy can be improved.

Journal ArticleDOI
TL;DR: A systematic literature review of different piezoelectric shunt damping strategies developed for the attenuation of vibration and noise in mechanical systems, including an assessment of the basic principles underlying the electromechanical behavior.

Journal ArticleDOI
TL;DR: A method using deep belief networks (DBNs) is proposed to detect multiple faults in axial piston pumps using DBNs, which confirms the feasibility and effectiveness of multiple faults detection in axials piston pumps.