The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality in human–machine interaction (HMI), medical care, sports analysis, physical rehabilitation, assisted daily living (ADL), children and elderly care, has recently gained increasing attention. The emergence of some novel sensing devices featuring miniature size, a light weight, and wireless data transmission, the availability of wireless communication infrastructure, the progress of machine learning and deep learning algorithms, and the widespread IoT applications has promised new opportunities for a significant progress in this particular field. Motivated by a great demand for HAR-related applications and the lack of a timely report of the recent contributions to knowledge in this area, this investigation aims to provide a comprehensive survey and in-depth analysis of the recent advances in the diverse techniques and methods of human activity recognition and motion analysis. The focus of this investigation falls on the fundamental theories, the innovative applications with their underlying sensing techniques, data fusion and processing, and human activity classification methods. Based on the state-of-the-art, the technical challenges are identified, and future perspectives on the future rich, sensing, intelligent IoT world are given in order to provide a reference for the research and practices in the related fields.

To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

This paper numerically investigates the effect of turbulence and journal misalignment on the lubrication characteristics of micro-grooved bearings with low-viscosity lubricant. The generalized average Reynolds equation satisfying the mass conservation cavitation algorithm is developed by integrating the average flow model proposed by Patir and Cheng, the Ng–Pan turbulent model, and the P-Θ model proposed by Elrod and Adams. With this model, the finite difference method is used in the numerical procedure. Moreover, the mathematical models of micro-grooves with different bottom shapes, that is, rectangle, isosceles triangle, left triangle, and right triangle, are given. The validity of the proposed model is verified by the comparisons with the published literature. Based on numerical simulation, the minimum film thickness, eccentricity ratio, attitude angle, maximum film pressure, friction torque, misalignment moment, film thickness, and pressure distributions under different external loads, rotational speeds, radial clearances, misalignment angles, and micro-groove parameters between models with and without turbulence and misalignment are comparatively analyzed. The numerical results reveal that turbulence may occur under heavy external load, high rotational speed, and large radius clearance. Concurrently, turbulence increases the minimum fluid film thickness and attitude angle, decreases the eccentricity ratio and friction torque, and enhances the bearing capacity. Furthermore, the larger misalignment angle results in the smaller minimum film thickness, eccentricity ratio and attitude angle, and the larger maximum film pressure, misalignment moment, and axial tilt of film pressure. Numerical simulations can provide theoretical guidance for the optimization of the geometrical parameters of micro-grooved bearings.

Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency α_max that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.

Small cracks are common defects in steel and often lead to catastrophic accidents in industrial applications. Various nondestructive testing methods have been investigated for crack detection; however, most current methods focus on qualitative crack identification and image processing. In this study, eddy current pulsed thermography (ECPT) was applied for quantitative crack detection based on derivative analysis of temperature variation. The effects of the incentive parameters on the temperature variation were analyzed in the simulation study. The crack profile and position are identified in the thermal image based on the Canny edge detection algorithm. Then, one or more trajectories are determined through the crack profile in order to determine the crack boundary through its temperature distribution. The slope curve along the trajectory is obtained. Finally, quantitative analysis of the crack sizes was performed by analyzing the features of the slope curves. The experimental verification showed that the crack sizes could be quantitatively detected with errors of less than 1%. Therefore, the proposed ECPT method was demonstrated to be a feasible and effective nondestructive approach for quantitative crack detection.

Wear is one of the most common failures of hydrodynamic bearings. The main purpose of the present work was to investigate the effects of wear on lubrication performance and acquire efficient vibration signatures for fault diagnosis. In this paper, a finite element model (FEM) for a two-disk rotor supported on worn hydrodynamic bearings is presented in which the oil film force is evaluated by linear and nonlinear models. Numerical and experimental results indicate that the static and dynamic characteristics of the bearing are significantly changed by wear, leading to a drop in system critical speeds due to the deterioration of the constraint status provided by the bearings to the rotor. As the wear depth increases, the onset speed of oil whirl increases, while that of oil whip becomes lower, and large amplitudes of resonance and oil whip are more likely to be excited. More notably, all of the above vibration signatures in the y-direction are more sensitive to wear compared to those in the x-direction, which means that wear faults can be diagnosed by differences in vibration characteristics between the x- and y-directions. This research can provide a theoretical foundation and engineering guidance for the hydrodynamic bearing wear fault diagnosis.