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.

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 paper, the time reversal method based on piezoelectric active sensing is investigated for health monitoring of bolted joints. Experiments are conducted on bolted joints to study the relationship between the time reversal focused signal peak amplitudes and the bolt preload. Two piezoelectric patches are bonded on two different sides of a bolted joint. Any one of the piezoelectric patches can be used as an actuator to generate an ultrasonic wave, and the other one can be used as a sensor to detect the propagated wave. With the time reversal method, the received response signal is reversed in the time domain and then is re-emitted as an excitation signal to acquire the time reversal focused signals. The experimental results show that the time reversal focused signal peak amplitudes increase with the increasing bolt preload until reaching saturation, and when the bolt preload increases to a certain value, the focused signal peak amplitudes will remain unchanged. Experiments show that the surface roughness of the bolted joint impacts the saturation value. A higher surface roughness value corresponds to a higher saturation value. In addition, the proposed method has a high signal to noise ratio benefiting from the time reversal method time and space focusing ability.

Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, the fault of a rolling bearing can only be identified when it has developed to a certain degree. At that moment, there is already not much time for maintenance, and could cause serious damage to the entire mechanical system. This paper proposes a novel approach to health degradation monitoring and early fault diagnosis of rolling bearings based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved multivariate multiscale sample entropy (MMSE). The smoothed coarse graining process was proposed to improve the conventional MMSE. Numerical simulation results indicate that CEEMDAN can alleviate the mode mixing problem and enable accurate intrinsic mode functions (IMFs), and improved MMSE can reflect intrinsic dynamic characteristics of the rolling bearing more accurately. During application studies, rolling bearing signals are decomposed by CEEMDAN to obtain IMFs. Then improved MMSE values of effective IMFs are computed to accomplish health degradation monitoring of rolling bearings, aiming at identifying the early weak fault phase. Afterwards, CEEMDAN is performed to extract the fault characteristic frequency during the early weak fault phase. The experimental results indicate the proposed method can obtain a better performance than other techniques in objective analysis, which demonstrates the effectiveness of the proposed method in practical application. The theoretical derivations, numerical simulations, and application studies all confirmed that the proposed health degradation monitoring and early fault diagnosis approach is promising in the field of prognostic and fault diagnosis of rolling bearings.

To detect small cracks in plate like structures, the high frequency characteristics of local dynamics were studied with the piezoelectric electro-mechanical impedance (EMI) method, and damages were monitored by the changes of the EMI. The finite element simulation model of EMI was established, and numerical analysis was conducted. The simulation results indicated that the peak frequency of the piezoelectric admittance signal is a certain order resonance frequency of the structure, and the piezoelectric impedance method could effectively detect the dynamic characteristics of the structure. The piezoelectric admittance simulation and experimental study of aluminum beams with different crack sizes were performed. Simulation and experimental results revealed that the peak admittance frequency decreases with the increase of crack size, and the higher resonance frequency is more sensitive to the small-scale damage. The proposed method has good repeatability and strong signal-to-noise ratio to monitor the occurrence and development of small-scale crack damage, and it has an important application prospect.