Harvesting Ambient Environmental Energy for Wireless Sensor Networks: A Survey

In recent years, wireless sensor networks (WSNs) have grown dramatically and made a great progress in many applications. But having limited life, batteries, as the power sources of wireless sensor nodes, have restricted the development and application of WSNs which often requires a very long lifespan for better performance. In order to make the WSNs prevalent in our lives, an alternative energy source is required. Environmental energy is an attractive power source, and it provides an approach to make the sensor nodes self-powered with the possibility of an almost infinite lifetime. The goal of this survey is to present a comprehensive review of the recent literature on the various possible energy harvesting technologies from ambient environment for WSNs

Fault Detection of Markov Jumping Linear Systems

In this paper, the fault detection (FD) problems of discrete-time Markov jumping linear systems (MJLSs) are studied. We first focus on the stationary MJLS. The proposed FD system consists of two steps: residual generation and residual evaluation. A new reference model strategy is applied to construct a residual generator, such that it is robust against disturbances and sensitive to system faults. The generated residual signals are then evaluated according to their stochastic properties, and a threshold is computed for detecting the occurrences of faults. The upper bound of the corresponding false alarm rate (FAR) is also given. For the nonstationary MJLS, similar results are also obtained. All the solutions are presented in the form of linear matrix inequalities (LMIs). Finally, a numerical example is used to illustrate the results

Identification and diagnosis of concurrent faults in rotor-bearing system with WPT and zero space classifiers

An effective method for identifying and diagnosing the concurrent fault combined by two or more single faults is yet to be further developed because most existing approaches focus on single faults. On the other hand, rotor-bearing system is an important part of rotating machinery. Therefore a new fault identification and diagnosis method based on wavelet packet transform (WPT) and zero space classifiers is presented in this paper. Firstly, the vibration signals collected from the rotor-bearing system are decomposed into several time-frequency compositions by WPT. Then the appropriate composition characterizing fault signatures is selected to extract features for constructing zero space classifiers. Finally, the effectiveness of the proposed method is demonstrated by an experiment carried out on a machinery fault simulator. The experimental results show that the proposed approach is feasible and effective to identify and diagnose the concurrent faults in a rotor-bearing system

Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition

Mine hoists play a crucial role in vertical-shaft transportation, and one of the main causes of their faults is abnormal lifting load. However, direct measurement of the load value is difficult. Further, the original structure must be destroyed for sensor installation. To facilitate efficient and accurate monitoring of the lifting load of mine hoist, this paper presents a novel condition-monitoring method based on variational mode decomposition (VMD) and support vector machine (SVM) through vibration signal analysis. First, traditional empirical mode decomposition (EMD) is used to analyze the vibration signal collected by an acceleration sensor, and the number of obtained intrinsic mode functions (IMFs) is employed to set the VMD mode number. Second, the obtained vibration signal is processed by the parameterized VMD, and the useful IMFs of VMD are selected through correlation analysis for feature extraction. Third, the obtained features are used to train an SVM model, and the trained SVM is used to monitor the mine-hoist lifting load. In this study, experiments on an operated mine hoist are also conducted to verify the reliability and validity of the proposed method. The experimental results show that the proposed method can accurately identify the considered lifting load conditions

Health monitoring of rolling element bearing using a spectrum searching strategy

Aiming at achieving early fault diagnosis and tracking the degradation process of bearings, we propose a novel monitoring methodology using a spectrum searching strategy in this paper. Firstly, a vibration signal is collected with appropriate sampling frequency and length. Secondly, the structural information of spectrum (SIOS) on a predefined frequency grid is constructed through a searching algorithm after deriving the single-sided FFT spectrum. Finally, the two-dimensional (2-D) line plot of the frequency grid versus the average power in SIOS is employed to conduct fault detection and the sum of the largest six total-power (SLSTP) of the frequency grid in SIOS is calculated as a health indication to demonstrate the changes in the bearing’s health status. The performance of the proposed scheme is validated with both simulation and bearing data. Experimental results show that the monitoring algorithm could manifest satisfactory behaviors in early fault diagnosis and health assessment of bearings

Fault diagnosis of rotating machinery based on noise reduction using empirical mode decomposition and singular value decomposition

Vibration signals collected from a faulty rotating machine include in general impulse information reflecting fault types, irrelevant vibration components caused by other normal mechanical parts, and other environmental noise. Cleaning the obtained vibration signals can prove practical significance for the fault diagnosis of rotating machinery. To address this issue, this paper proposes a new fault diagnosis method based on noise reduction technology using empirical mode decomposition (EMD) and singular value decomposition (SVD). In this approach, EMD is first applied to decompose the collected vibration signal into a set of intrinsic mode functions (IMFs) and residual signal. Then the first several IMFs including bearing characteristic damage frequencies (CDFs) and higher frequency components are selected to do further noise reduction by SVD for features, and the other remaining decomposition components of EMD are abandoned as noise. Finally, the fault diagnosis of rotating machinery is realized by these obtained features using a support vector machine (SVM) model. Experimental results testify that the proposed method is effective for mechanical fault diagnosis

Maternal exposure to ambient air pollution and congenital heart defects in China

Background: Evidence of maternal exposure to ambient air pollution on congenital heart defects (CHD) has been mixed and are still relatively limited in developing countries. We aimed to investigate the association between maternal exposure to air pollution and CHD in China.Method: This longitudinal, population-based, case-control study consecutively recruited fetuses with CHD and healthy volunteers from 21 cities, Southern China, between January 2006 and December 2016. Residential address at delivery was linked to random forests models to estimate maternal exposure to particulate matter with an aerodynamic diameter of ≤1 µm (PM1), ≤2.5 µm, and ≤10 µm as well as nitrogen dioxides, in three trimesters. The CHD cases were evaluated by obstetrician, pediatrician, or cardiologist, and confirmed by cardia ultrasound. The CHD subtypes were coded using the International Classification Diseases. Adjusted logistic regression models were used to assess the associations between air pollutants and CHD and its subtypes.Results: A total of 7055 isolated CHD and 6423 controls were included in the current analysis. Maternal air pollution exposures were consistently higher among cases than those among controls. Logistic regression analyses showed that maternal exposure to all air pollutants during the first trimester was associated with an increased odds of CHD (e.g., an interquartile range [13.3 µg/m3] increase in PM1 was associated with 1.09-fold ([95% confidence interval, 1.01-1.18]) greater odds of CHD). No significant associations were observed for maternal air pollution exposures during the second trimester and the third trimester. The pattern of the associations between air pollutants and different CHD subtypes was mixed.Conclusions: Maternal exposure to greater levels of air pollutants during the pregnancy, especially the first trimester, is associated with higher odds of CHD in offspring. Further longitudinal well-designed studies are warranted to confirm our findings

Power Allocation Based on Data Classification in Wireless Sensor Networks

Limited node energy in wireless sensor networks is a crucial factor which affects the monitoring of equipment operation and working conditions in coal mines. In addition, due to heterogeneous nodes and different data acquisition rates, the number of arriving packets in a queue network can differ, which may lead to some queue lengths reaching the maximum value earlier compared with others. In order to tackle these two problems, an optimal power allocation strategy based on classified data is proposed in this paper. Arriving data is classified into dissimilar classes depending on the number of arriving packets. The problem is formulated as a Lyapunov drift optimization with the objective of minimizing the weight sum of average power consumption and average data class. As a result, a suboptimal distributed algorithm without any knowledge of system statistics is presented. The simulations, conducted in the perfect channel state information (CSI) case and the imperfect CSI case, reveal that the utility can be pushed arbitrarily close to optimal by increasing the parameter V, but with a corresponding growth in the average delay, and that other tunable parameters W and the classification method in the interior of utility function can trade power optimality for increased average data class. The above results show that data in a high class has priorities to be processed than data in a low class, and energy consumption can be minimized in this resource allocation strategy