This paper reviews the research of monitoring technologies for bolted structural connections. The acoustoelastic effect based method, the piezoelectric active sensing method, and the piezoelectric impedance method are the three commonly used to monitor bolted connections. The basic principle and the applications of these three methods are discussed in detail in this paper. In addition, this paper presents a comparison of these methods and discusses their suitability for in situ or real-time bolt connection monitoring.

The traditional delay-and-sum imaging algorithm usually requires sending an excitation pulse at each piezoceramic transducer and obtains a damage image by drawing only ellipses. A multi-delay-and-sum imaging algorithm is proposed for damage detection of thin-plate-like structures using sparse piezoceramic transducers. Compared with the traditional delay-and-sum imaging algorithm, the proposed algorithm sends only one excitation pulse for each detection. A reflection coefficient is employed in the proposed method to cancel the artifacts caused by the boundary reflection signals, and the reflection coefficient is determined by the distribution of piezoceramic transducers and strength of the reflection signals. An additional time compensation due to the excitation pulse is also made to reduce the error of damage locating. To increase the image pixel value of a damage, the damage image is obtained by drawing both ellipses and hyperbolas with transmitter–sensor pair signals. The experimental results obtained on an aluminum alloy plate demonstrate that the proposed multi-delay-and-sum imaging algorithm can identify a bonded mass damage efficiently and accurately.

Mechanical vibration signal mapped into a high-dimensional space tends to exhibit a special distribution and movement characteristics, which can further reveal the dynamic behavior of the original time series. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, the tensor decomposition algorithm has broad application prospects in signal processing. High-dimensional tensor can be obtained from a one-dimensional vibration signal by using phase space reconstruction, which is called the tensorization of data. As a new signal decomposition method, tensor-based singular spectrum algorithm (TSSA) fully combines the advantages of phase space reconstruction and tensor decomposition. However, TSSA has some problems, mainly in estimating the rank of tensor and selecting the optimal reconstruction tensor. In this paper, the improved TSSA algorithm based on convex-optimization and permutation entropy (PE) is proposed. Firstly, aiming to accurately estimate the rank of tensor decomposition, this paper presents a convex optimization algorithm using non-convex penalty functions based on singular value decomposition (SVD). Then, PE is employed to evaluate the desired tensor and improve the denoising performance. In order to verify the effectiveness of proposed algorithm, both numerical simulation and experimental bearing failure data are analyzed.

Abstract:To improve the performance of single-channel, multi-fault blind source separation (BSS), a novel method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) is proposed in this paper. The RPSEMD method is used to decompose the original single-channel vibration signal into several intrinsic mode functions (IMFs), with the obtained IMFs and original signal together forming a new observed signal for the dimensional lifting. Therefore, an undetermined problem is transformed into a positive definite problem. Compared with the existing EMD method and its improved version, the proposed RPSEMD method performs better in solving the mode mixing problem (MMP) by employing sinusoid-assisted technology. Meanwhile, it can also reduce the computational load and reconstruction errors. The number of source signals is estimated by adopting singular value decomposition (SVD) and Bayes information criterion (BIC). Simulation analysis has demonstrated the superiority of this method being applied in multi-fault BSS. Furthermore, its effectiveness in identifying the multi-fault features of rolling-bearing has been also verified based on a test rig.

Convex 1-D first-order total variation (TV) denoising is an effective method for eliminating signal noise, which can be defined as convex optimization consisting of a quadratic data fidelity term and a non-convex regularization term. It not only ensures strict convex for optimization problems, but also improves the sparseness of the total variation term by introducing the non-convex penalty function. The convex 1-D first-order total variation denoising method has greater superiority in recovering signals with flat regions. However, it often produces undesirable staircase artifacts. Moreover, actual denoising efficacy largely depends on the selection of the regularization parameter, which is utilized to adjust the weights between the fidelity term and total variation term. Using this, algorithms based on second-order total variation regularization and regularization parameter optimization selection are proposed in this paper. The parameter selection index is determined by the permutation entropy and cross-correlation coefficient to avoid the interference by human experience. This yields the convex 1-D second-order total variation denoising method based on the non-convex framework. Comparing with traditional wavelet denoising and first-order total variation denoising, the validity of the proposed method is verified by analyzing the numerical simulation signal and the vibration signal of fault bearing in practice.

This paper presents the analysis of the vibration time series of a gear system acquired by piezoelectric acceleration transducer using the detrended fluctuation analysis (DFA). The experimental results show that gear vibration signals behave as double-scale characteristics, which means that the signals exhibit the self-similarity characteristics in two different time scales. For further understanding, the simulation analysis is performed to investigate the reasons for double-scale of gear's fault vibration signal. According to the analysis results, a DFA double logarithmic plot based feature vector combined with scale exponent and intercept of the small time scale is utilized to achieve a better performance of fault identification. Furthermore, to detect the crossover point of two time scales automatically, a new approach based on the Hough transform is proposed and validated by a group of experimental tests. The results indicate that, comparing with the traditional DFA, the faulty gear conditions can be identified better by analyzing the double-scale characteristics of DFA. In addition, the influence of trend order of DFA on recognition rate of fault gears is discussed.

Transverse cracks in cylindrical parts can be detected by using the ultrasound based pulse-echo method, which has been widely used in industrial applications. However, it is still a challenge to identify the echoes reflected by a crack and bottom surfaces of a cylindrical part due to the multipath propagation and wave mode conversion. In this paper, an interval energy approach is proposed to evaluate the severity of the transverse crack in a cylindrical part. Lead zirconate titanate patch transducers are used to generate the ultrasound pulse and to detect the echoes. The echo signals are preprocessed and divided into two zones, the normal reflection zone and the crack reflection zone. Two energy factors evaluating the severity of the crack are computed based on the interval energy. When using this proposed method, it is not necessary to identify the echo sources since all the crack and boundary echoes are automatically taken into consideration by using the proposed method. The experimental results indicate that proposed approach is more suitable and sensitive to evaluate the transverse crack severity of cylindrical part than the traditional method.

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