In this paper, a piezoelectric impedance frequency shift method is developed to estimate the bolt preload for the detection of bolt looseness in engineering structures. An experimental device that allows the precision control of the axial preload force on a bolt is designed and fabricated. A universal testing machine is used to preload accurately on the bolt in the experiments. Under different bolt preload conditions, the impedance analyzer measures the admittance (inverse of the impedance) signal of the PZT (Lead ZirconateTitanate) patches which are bonded on the bolt head. Firstly, a wide frequency band is swept to find a sensitive frequency band of the piezoelectric admittance with the imaginary part. Then in the sensitive frequency band, a specified peak frequency of the admittance signature is chosen to investigate the frequency shift with different bolt preloads. The relationship between the specified frequency shift and the bolt preload is established. The experimental results show that the specified peak frequency decreases as the bolt preload increases for both M16 and M12 bolts, and the frequency shift has a linear relationship with the preload on the bolt. The frequencies of the real and imaginary parts of the admittance signature have the same results. Therefore, the bolt preload can be determined by measuring the specified frequency shift and this method has a good application prospect.

Piezoceramic based active sensing methods have been researched to monitor preload on bolt connections. However, there is a saturation problem involved with this type of method. The transmitted energy is sometimes saturated before the maximum preload which is due to it coming into contact with flat surfaces. When it comes to flat contact surfaces, the true contact area will easily saturate with the preload. The design of a new type of bolt looseness monitoring sensor, a smart washer, is to mitigate the saturation problem. The smart washer is composed of two annular disks with contact surfaces that are machined into convex and concave respectively, to eliminate the complete flat contact surfaces and to reduce the saturation effect. One piezoelectric patch is bonded on the non-contact surface of each annular disk. These two mating annular disks form a smart washer. One of the two piezoelectric patches serves as an actuator to generate an ultrasonic wave that propagates through the contact surface; the other one serves as a sensor to detect the propagated waves. The wave energy propagated through the contact surface is proportional to the true contact area which is determined by the bolt preload. The time reversal method is used to extract the peak of the focused signal as the index of the transmission wave energy; then, the relationship between the signal peak and bolt preload is obtained. Experimental results show that the focused signal peak value changes with the bolt preload and presents an approximate linear relationship when the saturation problem is experienced. The proposed smart washer can monitor the full range of the rated preload.

In this study, a piezoelectric active sensing-based time reversal method was investigated for monitoring pipeline internal corrosion. An effective method that combines wavelet packet energy with a Convolutional Neural Network (CNN) was proposed to identify the internal corrosion status of pipelines. Two lead zirconate titanate (PZT) patches were pasted on the outer surface of the pipeline as actuators and sensors to generate and receive ultrasonic signals propagating through the inner wall of the pipeline. Then, the time reversal technique was employed to reverse the received response signal in the time domain, and then to retransmit it as an excitation signal to obtain the focused signal. Afterward, the wavelet packet transform was used to decompose the focused signal, and the wavelet packet energy (WPE) with large components was extracted as the input of the CNN model to rapidly identify the corrosion degree inside the pipeline. The corrosion experiments were conducted to verify the correctness of the proposed method. The occurrence and development of corrosion in pipelines were generated by electrochemical corrosion, and nine different depths of corrosion were imposed on the sample pipeline. The experimental results indicated that the classification accuracy exceeded 99.01%. Therefore, this method can quantitatively monitor the corrosion status of pipelines and can pinpoint the internal corrosion degree of pipelines promptly and accurately. The WPE-CNN model in combination with the proposed time reversal method has high application potential for monitoring pipeline internal corrosion.

Bolt monitoring plays a vital role in ensuring the safe operation of engineering structures. The utilization of piezoelectric (PZT) active sensing and analysis of ultrasonic energy transmitted through the interface of bolted connections has demonstrated high feasibility for monitoring bolt looseness. However, the ultrasonic energy saturation effect (i.e. the response signal energy changes slightly as the bolt preload variation) near the rated bolt preload restricts its applicability in early detection of bolt loosening. In this paper, for the energy saturation in the PZT active sensing method, a new bolt looseness indicator with the energy of leading waves (i.e. the first several wave packets) in the response signal is proposed for bolt loosening monitoring, especially for the early bolt loosening monitoring since the energy of the leading wave packets has the linear relationship with bolt preload. The experimental results show that the energy of the first several wave packets in the response signals can be as a looseness indicator of bolt preload. Within the entire range of bolt preload, the indicator exhibits a linear relationship with the bolt preload. Moreover, this method successfully resolves the challenge of energy saturation, providing an effective approach for monitoring bolt preload.

To solve the problem of multi-fault blind source separation (BSS) in the case that the observed signals are under-determined, a novel approach for single channel blind source separation (SCBSS) based on the improved tensor-based singular spectrum analysis (TSSA) is proposed. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, TSSA method can be employed to extract the multi-fault features from the measured single-channel vibration signal. However, SCBSS based on TSSA still has some limitations, mainly including unsatisfactory convergence of TSSA in many cases and the number of source signals is hard to accurately estimate. Therefore, the improved TSSA algorithm based on canonical decomposition and parallel factors (CANDECOMP/PARAFAC) weighted optimization, namely CP-WOPT, is proposed in this paper. CP-WOPT algorithm is applied to process the factor matrix using a first-order optimization approach instead of the original least square method in TSSA, so as to improve the convergence of this algorithm. In order to accurately estimate the number of the source signals in BSS, EMD-SVD-BIC (empirical mode decomposition—singular value decomposition—Bayesian information criterion) method, instead of the SVD in the conventional TSSA, is introduced. To validate the proposed method, we applied it to the analysis of the numerical simulation signal and the multi-fault rolling bearing signals.

Pipeline corrosion, especially inner wall corrosion, will cause serious safety accidents. It is important to monitor the corrosion inside the pipeline to reduce the potential risk. Considering the thickness of the pipe wall will become thinner due to the corrosion which will cause the attenuation of the propagated ultrasonic signal energy. Based on this, a novel method for real-time monitoring of pipeline inner wall corrosion using piezoelectric active sensing technology combined with time reversal method is proposed. Two PZT patches are bonded on the outer surface of the pipe as an actuator and a sensor to generate and receive ultrasonic signals propagating in the pipeline wall, respectively. With the time reversal method, the received response signal is reversed in the time domain and then re-emitted as an excitation signal again. Then the time reversal focused signal which represents the signal energy is obtained. By analyzing the received signal energy, that is the focused signal peak, the corrosion state in the inner wall of pipeline can be judged. Experiments are performed to verify the proposed method. In the experiments, the electrochemical corrosion is used to generate the occurrence and evolution of erosion inside the pipeline. Eight different corrosion states of three sample pipelines were studied at different corrosion time. The experimental results show that the peak amplitude of the focused signal decreases with the increase of corrosion time with good repeatability. The method is feasible in pipeline corrosion monitoring, especially in monitoring of the early pipeline corrosion, which has a good application prospect.