Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation

Ultrasonic guided waves (UGWs) have been extensively utilized in nondestructive testing (NDT) and structural health monitoring (SHM) for detection and real-time monitoring of structural defects. By implementing multiple piezoelectric sensors onto a plane of the target structure to form a sensor network, damages within the sensing range can be detected or even visualized through a pitch-catch configuration. On the other hand, deep learning (DL) techniques have recently been widely used to aid UGW-based SHM when the waveform is over complicated to extract a specific mode of interest due to irregular structure or boundary reflections. However, not too much research work has been conducted to thoroughly combine sensor networks with DL. Existing research using DL approaches is mainly used to train and interpret waveforms from isolated sensor pairs. The topological structure of sensor layout and sensor-damagerelative positions are hardly considered in the data-driven process. Motivated by these concerns, this study offers a first-of-its-kindperspective to interpret UGW data collected from a sensor network by mapping the physical sensor-damage layout into a graph, in which sensors and potential damages serve as graph vertices bearing heterogenous properties upon coming to UGWs and the process of UGW transmission between sensors are encapsulated as wavelike messagepassing between the vertices. A novel physics-informedend-to-end GNN model, named as WaveNet, was exquisitely and meticulously developed. By utilizing wave information and topological structure, WaveNet enables inference of multiple damages in terms of severity and location with satisfactory accuracy, even when the waveforms are chaotic and the sensor arrangement is different at the training and testing stages. More importantly, beyond the SHM scenario, the present study is expected to enlighten new thinking on interconnecting physical wave propagation with virtual messaging passing in neural networks.

HEAD ON HALL EFFECT SENSOR ARRANGEMENT FOR DISPLACEMENT MEASUREMENT

Hall effect sensor is a very frequently used sensor in mechatronic products. There is little information in the catalogue sheets about the application possibilities of this type of sensor. This article examines the configuration options of this sensor and how to use it to achieve the best measurement uncertainty and minimal hysteresis.

FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot

Conveyor belts are key pieces of equipment for bulk material transport, and they are of great significance to ensure safe operation. With the development of belt conveyors in the direction of long distances, large volumes, high speeds, and high reliability, the use of inspection robots to perform full inspections of belt conveyors has not only improved the efficiency and scope of the inspections but has also eliminated the dependence of the traditional method on the density of sensor arrangement. In this paper, relying on the wireless-power-supply orbital inspection robot independently developed by the laboratory, aimed at the problem of the deviation of the belt conveyor, the methods for the diagnosis of the deviation of the conveyor belt and FPGA (field-programmable gate array) parallel computing technology are studied. Based on the traditional LSD (line segment detection) algorithm, a straight-line extraction IP core, suitable for an FPGA computing platform, was constructed. This new hardware linear detection algorithm improves the real-time performance and flexibility of the belt conveyor diagnosis mechanism.

A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking

Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction.

Effect of Strain Measurement Layout on Damage Detection and Localization in a Free Falling Compliant Cylinder Impacting a Water Surface

The need for effective and reliable damage detection and localization systems is growing in several engineering fields, in particular in water impact problems characterized by impulsive loading conditions, high amplitude vibrations and large local deformations. In this paper, we further develop the approach presented in previous works to detect damage of water-impacting structures. Specifically, we provide a set of experimental tests on a flexible plastic cylinder impacting the water after a 50 cm free fall. The cylindrical specimen is artificially damaged in a known position. Strain measurements are performed through a set of nine fiber Bragg gratings distributed along the circumference of a cylinder section. We show that strain sensors can be used as reference sensors, for structure displacements reconstruction, and control sensors, for damage detection purposes, and the computation of the difference between measured and expected deformation may allow damage detection. Moreover, we investigate how exchanging control and reference sensors in the same sensor arrangement affect damage detection and localization.

An experimental study of cable tension identification based on measurements of mode shapes

<p>Cable tension identification based on mode shapes extracted from vibration measurements is a relatively new method. In this method, the cable is equivalent to a beam model hinged at its ends and under axial tension with an unknown length to eliminate the effects of boundary conditions. This study focuses on the influences of sensor arrangement in the measurements on the accuracy of the tension identification. For this purpose, full-scale cable experiments have been carried out, where a number of sensors were attached to the cable to record cable acceleration during artificial excitation. The eigenvalue realization algorithm (ERA) has then been applied to identify the mode shapes and frequencies of the cables from the multiple acceleration measurements. The effects of different sensor arrangement schemes and cable tension identification method based on higher- order modes are compared and discussed.</p>

Microseismic Monitoring, Positioning Principle, and Sensor Layout Strategy of Rock Mass Engineering

Microseismic monitoring technology can start from the most initial stage of rock deformation and track and monitor the progressive failure process of rock mass from the fracture of rock blocks to the instability of rock mass. Thus, the scientific nature of monitoring work is greatly promoted, and the accuracy and advance of the prediction of engineering and geological disasters are improved. In this paper, the fracture and instability of rock can be analyzed by analyzing the microseismic signals produced by rock failure; The location of microseismic source can be determined by multipoint synchronous data acquisition to determine the time when each sensor (at least 5) receives microseismic signals; Combined with practical engineering experience for underground engineering in growth, heading tunnel, put forward only sensor arrangement to take mobile, follow the semienclosed layout network. We hope to give some reference to the researchers who are concerned with microseismic monitoring technology.

Compressed Sensing-Based Order Analysis for Blade Tip Timing Signals Measured at Varying Rotational Speed

Monitoring the vibrations of high-speed rotating blades is significant to the security of turbomachineries. Blade tip timing (BTT) is considered as a promising technique for detecting blade vibrations without contact online. However, extracting blade vibration characteristics accurately from undersampled BTT signals measured at varying rotational speed (VRS) has become a big challenge. The existing two methods for this issue are restricted within the order bandwidth limitation and require prior information and precise sensor installation angles, which is often unpractical. To overcome these difficulties, a compressed sensing-based order analysis (CSOA) method was proposed. Its feasibility comes from the sparsity of BTT vibration signals in the order domain. The mathematical model for the proposed method was built, and the optimizing principles for sensor number and sensor arrangement were given. Simulated and experimental results verified the feasibility and advantages of the proposed method that it could extract order spectrum accurately from BTT vibration signals measured at VRS without the drawbacks in the existing two methods.

Mapping Property of Bilateration and Its Application to Human-Following Robot

Objective of this paper is twofold. The first one is to study the mapping property and unified form of the component equations of the unknown node in bilateration, and the second one is to introduce the concept model for human-following robot based on bilateration. Bilateration needs only two known nodes and two distances’ data. Because of the simple sensor arrangement in bilateration, it needs less computation and uses less number of unavoidable erroneous distances compared to the trilateration.