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Purpose Fatigue damage of internal threads has gradually become the main failure mode of force sensor. To make the internal thread structure of force sensor meet the fatigue performance requirements, the design criteria of static strength and fatigue life are comprehensively considered in this paper. Design/methodology/approach The variation of static stress and fatigue life with the size of the main structure is obtained by simulation. By changing the number of thread turns, the hub height and outer diameter of the hub, the optimized design of the spoke force sensor is determined. Findings The experiment was carried out based on the determined optimized structure, and the results showed that the fatigue life meets the design requirements. Originality/value This research has certain guiding significance for the design and developments of high-cycle fatigue force sensors.

To process the non-stationary vibration signals and improve accuracy of bearing fault diagnosis, this paper presents a novel intelligent fault diagnosis method based on the adaptive fast ensemble empirical mode decomposition (AFEEMD) and one-dimensional convolutional neural networks (1D CNNs). First, the AFEEMD algorithm is utilized to decompose the raw signals into intrinsic mode functions (IMFs). Then, the time and frequency statistic features of the first several IMFs are analyzed to form feature vector, which are used as the input of 1D CNNs to identify the bearing fault. The performance of the proposed method is validated using the dataset from the Case Western Reserve University (CWRU). Compared with the traditional back propagation neural network (BPNN), the results show that the proposed AFEEMD-1D CNNs method not only can obtain higher accuracy and achieve more reliable performance, but also can improve the generalization performance. Due to the end-to-end feature learning capacity, it can be extended to other machinery for fault diagnosis.

A feature extraction method combined with variational mode decomposition (VMD) and kernel independent component analysis (KICA) is proposed to improve the fault feature extraction of vibration signal of rotary machinery. Firstly, VMD is used to decompose the single-channel vibration signal. Secondly, calculate the correlation coefficient between each component and the original signal. Finally, a new multidimensional observation signal is formed with high correlation components, and the fault signals will be extracted from the new observation signal by KICA. Compared with some typical fault feature extraction methods, the better performance of the proposed method is demonstrated by two experiments which are faulty rolling bearing experiment and a comprehensive experiment with faulty rolling bearing and faulty gear. Furthermore, an experiment of faulty rotary shaft verifies the effectiveness of this method. The results demonstrate that the proposed method is efficient for fault feature extraction of single-channel vibration signal of rotary machinery.

Earthquake early warning is one of the most effective means to reduce earthquake disasters. At present, many countries and regions in the world have established their own earthquake early warning systems. Compared with traditional geophone, the MEMS seismic sensor system has some advantages, including high sensitivity and compact structure, so it has been widely used in earthquake early warning systems. This paper mainly describes some projects of MEMS technology in earthquake early warning, and uses artificial excitation compares the MEMS seismic sensor system with traditional geophone. It is found that the sensitivity of the MEMS seismic sensor system is about 1.7 times that of the traditional geophone. Finally, some prospects are put forward for the application of MEMS technology in the field of earthquake early warning.

In the era of big data, longer time series fault signals will not only be easy to copy and store, but also reduce the labor cost of manual labeling, which can better meet the needs of industrial big data. Aiming to effectively extract the key classification information from a longer time series of bearing vibration signals and achieve high diagnostic accuracy under noise and different load conditions. The one-dimensional adaptive long sequence convolutional network (ALSCN) is proposed. ALSCN can better extract features directly from high-dimensional original signals without manually extracting features and relying on expert knowledge. By adding two improved multi-scale modules, ALSCN can not only extract important features efficiently from noise signals, but also alleviate the problem of losing key information due to continuous down-sampling. Moreover, a Bayesian optimization algorithm is constructed to automatically find the best combination of hyperparameters in ALSCN. Based on two bearing data sets, the model is compared with traditional model such as SVM and deep learning models such as convolutional neural networks (CNN) et al. The results prove that ALSCN has a higher diagnostic accuracy rate on 5120-dimensional sequences under &minus, 5 signal to noise ratio (SNR) with better generalization.

The transmission capacity (TC) of the wireless ad-hoc network is one of the key properties to ensure the quality of network service. The main goal of this study is to explore the mobility of the network to establish a node mobile model with a saturated state and determine the TC and delay for the network based on the bidirectional full-duplex (BFD) system. Considering the outage probability, packet arrival rate, and failure probability of packet attempting transmission, the node mobility models are established based on the node mobility and network self-organization. In analysis, this study determines the mobile state, static state, communication state, and outage stat by defining the mobile rule and range of nodes, then the steady-state probability of each state is obtained by calculating the state transition matrix. After optimising the existing saturated Markov model, the authors determine the network delay and TC based on the mobile nodes, contention window size, maximum number of given packet attempting retransmission, back-off time and signal-interference ratio (SIR) etc. The theoretical analysis and numerical simulation show that the TC of the bidirectional ad-hoc network can be significantly improved by using space resources in two directions, and the introduction of the BFD system is indeed to reduce the network delay.

Detection of the fatigue crack propagation length with high precision is the key to correctly estimating the crack propagation life. In view of the cumbersome testing process, heavy labor intensity and many influences of subjective factors from the operators of the traditional detection methods, the image processing technology based on machine vision is applied to detect the growth state of laboratory standard M (T) specimen with a fatigue crack. First of all, several image de-noising methods are compared to verify that the median filtering method is more suitable for the processing of fatigue crack images in fatigue specimens under laboratory conditions. Secondly, the crack extraction algorithm based on Hough transform and regional growth is determined. Finally, the Zhang thinning algorithm based on the local binary texture feature is improved to refine the image, and the corresponding burr removal algorithm is proposed to refine the images. Compared with the traditional visual inspection method, the image processing technology proposed in this paper can obtain good processing results. The measured crack length error is smaller and smaller with the development of the crack propagation state and can replace the traditional visual inspection method to detect cracks extended status.

With the improvement in processing level and the quality requirements of parts, fatigue failure has gradually become one of the main failure modes of the spoke type load cell. Combining the requirements of both static strength and fatigue performance based on the computer-aided engineering technique, the maximum equivalent stress and fatigue lifetime of the elastic structure are obtained by changing the dimension of the length, width and height of the spoke, the transition fillet and the inclination angle, which realizes the optimal design of the spoke type load cell. The result provides a valuable reference for the design and development of force sensors for high-cycle fatigue.

The deep learning net has achieved breakthrough results in fault diagnosis. However, most deep learning algorithms rely on massive volumes of data and are difficult to resist the influence of noise. In this study, a new method based on the efficient attention prototypical net (EAPN) is proposed to address the problems. First, frequency slice wavelet transform (FSWT) that can make the signal well described is introduced to convert the signal into images. Subsequently, the efficient lightweight channel attention (ELCA) module and dilated convolution module are created to extract key information across channels under the greater receptive field. Finally, the improved prototypical network is applied to classify based on metric space. Compared with other methods, this paper verified the higher performance of the EAPN under limited samples and strong noise based on the standard bearing fault data set and the actual collected rotor fault data.

Objective. The aim of this study was to investigate the cognitive function characteristics of Parkinson’s disease (PD) with sleep disorders. Methods. Consecutive patients with PD (n=96), patients with primary sleep disorders (n=76), and healthy control subjects (n=66) were assessed. The patients with PD were classified into sleep disorder (PD-SD) and non-sleep disorder (PD-NSD) groups. Results. Among 96 patients with PD, 69 were diagnosed with a sleep disorder. There were 38 sleep disorder cases, 31 RBD cases, and 27 NSD cases. On the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and MoCA subtests, patients in the PD-SD, primary sleep disorder, and PD-NSD groups exhibited lower scores than those in the control group. Moreover, the PD-SD patients exhibited more significant cognitive impairment than was observed in the primary sleep disorder patients. In the PD-SD subgroup, the attention scores on the MoCA and on MoCA subtests were lower in the PD with RBD group than in the PD with insomnia group. Conclusion. PD with sleep disorders may exacerbate cognitive dysfunction in patients. PD associated with different types of sleep disorders differentially affects cognitive functions, and patients with PD with RBD exhibited poorer cognitive function than was seen in patients with PD with insomnia.

To target the accurate and fast joint quality identification, this work presents a method based on the particle swarm optimization (PSO) and the kernel extreme learning machine (KELM) in resistance spot welding (RSW). We perform welding and tensile tests to determine the related information and extract features from signals combined with the welding mechanism. Afterward, we optimize the parameters in the KELM with the PSO and fivefold cross validation (CV) and establish an identification model based on the KELM to classify the joint quality. The comparison results show that the joint quality identification model has a good generalization performance with an accuracy of up to 97.83%. Moreover, the feature extraction is reasonable, providing insight into the RSW process. The quality identification method based on the PSO and KELM is effective in RSW.

A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.

With widely application of large-scale wireless communication system, the spatial location of the user and the access point is often not completely regular. The intensity of the interference and the signal received by the receiver are all directly rely on the position of the node, and the spatial topology of the wireless Ad-Hoc network and its change are the key factors that affect network performance. The probability distribution is established when the nodes are mobile in wireless Ad-Hoc networks. Using angle θn to explain the different motion direction of the nodes, and the probability distribution function is derived from the node continuous motion angle difference Φn as a random variable. In the evaluation of interference, the interference distance is used as the basis of the assessment. Therefore, defining interference coefficient β to establish interference assessment scope, to analyze the intensity of interference between different links through the relationship between the distance of the sender node in interference link to receiving node in estimation link and interference assessment scope. And, the distribution function of the interference in wireless Ad-Hoc networks is derived with the random variable Φn. Then the reliability and universality of the model are illustrated by numerical simulation.

In order to improve the recognition rate of a sucker rod’s defect and reduce the rapture possibility of the rod, the mixed characters include of wavelet packet energy character and the peak value in the time-domain were used to input a recognition network, and support vector machines (SVM) were used. Tested results with lots of data acquired in laboratory showed that SVM based on the mixed characters can enhance recognition rate of the sucker rod’s defect.

In order to improve the recognition rate of a sucker rod's defect and reduce the rapture possibility of the rod, the mixed characters include of wavelet packet energy character and the peak value in the time-domain were used as the input of a recognition network, and artificial neural networks (ANN) and support vector machines (SVM) were used and compared as the recognition network to get the best recognition way. Tested results with lots of data acquired in laboratory showed that SVM was better than ANN at recognition of the sucker rod's defect, and SVM based on the mixed characters can enhance recognition rate of the sucker rod's defect.