Vibration frequency measurement using a local multithreshold technique

Potential of Non-Contact Dynamic Response Measurements for Predicting Small Size or Hidden Damages in Highly Damped Structures

Vibration-based structural health monitoring (SHM) is essential for evaluating structural integrity. Traditional methods using contact vibration sensors like accelerometers have limitations in accessibility, coverage, and impact on structural dynamics. Recent digital advancements offer new solutions through high-speed camera-based measurements. This study explores how camera settings (speed and resolution) influence the accuracy of dynamic response measurements for detecting small cracks in damped cantilever beams. Different beam thicknesses affect damping, altering dynamic response parameters such as frequency and amplitude, which are crucial for damage quantification. Experiments were conducted on 3D-printed Acrylonitrile Butadiene Styrene (ABS) cantilever beams with varying crack depth ratios from 0% to 60% of the beam thickness. The study utilised the Canny edge detection technique and Fast Fourier Transform to analyse vibration behaviour captured by cameras at different settings. The results show an optimal set of camera resolutions and frame rates for accurately capturing dynamic responses. Empirical models based on four image resolutions were validated against experimental data, achieving over 98% accuracy for predicting the natural frequency and around 90% for resonance amplitude. The optimal frame rate for measuring natural frequency and amplitude was found to be 2.4 times the beam's natural frequency. The findings provide a method for damage assessment by establishing a relationship between crack depth, beam thickness, and damping ratio.

Diaphragms simulation, fabrication, and testing of a high temperature fiber optic F-P accelerometer based on MEMS

High-sensitivity detection of vibrations under high temperatures is a topic of great interest in modern engineering such as thermal engine deep-sea aquaculture factory ship, aerospace, high temperature casting, energy, etc. As traditional accelerometers and some fiber optic F-P accelerometers have shown their sensing limits at about 400 °C and 650 °C, respectively, a high temperature fiber optic F-P accelerometer based on MEMS technology is proposed. To obtain a high-performance chip for the sensor, an examination of the theoretical performance of an L and Г-shaped cantilever beam diaphragm shows a sensitivity of 15.05 nm/g and 53.7 nm/g, respectively, and a wide working frequency range. Thanks to the designed sensor's various protections, frequency measurements with a high-temperature performance of 850 °C are recorded. The L-shaped cantilever beams diaphragm allows the sensor measurements at 850 °C with a repeatability of 5.46%, a working frequency range of 100-1000 Hz, an experimental sensitivity of 389 mV/g, an overall stability of 8 jumps at its adjacent frequency resolution range over 150 measurements, a linearity of 0.9856 and a maximum relative error maintained below 1.72%. In the field of application, it also exhibits a good relative error of measurement respecting the technical specification of 5 Hz.

Non-Invasive Inspections: A Review on Methods and Tools

Non-Invasive Inspection (NII) has become a fundamental tool in modern industrial maintenance strategies. Remote and online inspection features keep operators fully aware of the health of industrial assets whilst saving money, lives, production and the environment. This paper conducted crucial research to identify suitable sensing techniques for machine health diagnosis in an NII manner, mainly to detect machine shaft misalignment and gearbox tooth damage for different types of machines, even those installed in a hostile environment, using literature on several sensing tools and techniques. The researched tools are critically reviewed based on the published literature. However, in the absence of a formal definition of NII in the existing literature, we have categorised NII tools and methods into two distinct categories. Later, we describe the use of these tools as contact-based, such as vibration, alternative current (AC), voltage and flux analysis, and non-contact-based, such as laser, imaging, acoustic, thermographic and radar, under each category in detail. The unaddressed issues and challenges are discussed at the end of the paper. The conclusions suggest that one cannot single out an NII technique or method to perform health diagnostics for every machine efficiently. There are limitations with all of the reviewed tools and methods, but good results possible if the machine operational requirements and maintenance needs are considered. It has been noted that the sensors based on radar principles are particularly effective when monitoring assets, but further comprehensive research is required to explore the full potential of these sensors in the context of the NII of machine health. Hence it was identified that the radar sensing technique has excellent features, although it has not been comprehensively employed in machine health diagnosis.

Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles

The concept of predictive and preventive maintenance and constant monitoring of the technical condition of industrial machinery is currently being greatly improved by the development of artificial intelligence and deep learning algorithms in particular. The advancement of such methods can vastly improve the overall effectiveness and efficiency of systems designed for wear analysis and detection of vibrations that can indicate changes in the physical structure of the industrial components such as bearings, motor shafts, and housing, as well as other parts involved in rotary movement. Recently this concept was also adapted to the field of renewable energy and the automotive industry. The core of the presented prototype is an innovative interface interconnected with augmented reality (AR). The proposed integration of AR goggles allowed for constructing a platform that could acquire data used in rotary components technical evaluation and that could enable direct interaction with the user. The presented platform allows for the utilization of artificial intelligence to analyze vibrations generated by the rotary drive system to determine the technical condition of a wind turbine model monitored by an image processing system that measures frequencies generated by the machine.

Biomechanics of the peafowl's crest reveals frequencies tuned to social displays

Feathers act as vibrotactile sensors that can detect mechanical stimuli during avian flight and tactile navigation, suggesting that they may also detect stimuli during social displays. In this study, we present the first measurements of the biomechanical properties of the feather crests found on the heads of birds, with an emphasis on those from the Indian peafowl (Pavo cristatus). We show that in peafowl these crest feathers are coupled to filoplumes, small feathers known to function as mechanosensors. We also determined that airborne stimuli with the frequencies used during peafowl courtship and social displays couple efficiently via resonance to the vibrational response of their feather crests. Specifically, vibrational measurements showed that although different types of feathers have a wide range of fundamental resonant frequencies, peafowl crests are driven near-optimally by the shaking frequencies used by peacocks performing train-rattling displays. Peafowl crests were also driven to vibrate near resonance in a playback experiment that mimicked the effect of these mechanical sounds in the acoustic very near-field, reproducing the way peafowl displays are experienced at distances ≤ 1.5m in vivo. When peacock wing-shaking courtship behaviour was simulated in the laboratory, the resulting airflow excited measurable vibrations of crest feathers. These results demonstrate that peafowl crests have mechanical properties that allow them to respond to airborne stimuli at the frequencies typical of this species' social displays. This suggests a new hypothesis that mechanosensory stimuli could complement acoustic and visual perception and/or proprioception of social displays in peafowl and other bird species. We suggest behavioral studies to explore these ideas and their functional implications.

Non-Contact Smartphone-Based Monitoring of Thermally Stressed Structures

The in-situ measurement of thermal stress in beams or continuous welded rails may prevent structural anomalies such as buckling. This study proposed a non-contact monitoring/inspection approach based on the use of a smartphone and a computer vision algorithm to estimate the vibrating characteristics of beams subjected to thermal stress. It is hypothesized that the vibration of a beam can be captured using a smartphone operating at frame rates higher than conventional 30 Hz, and the first few natural frequencies of the beam can be extracted using a computer vision algorithm. In this study, the first mode of vibration was considered and compared to the information obtained with a conventional accelerometer attached to the two structures investigated, namely a thin beam and a thick beam. The results show excellent agreement between the conventional contact method and the non-contact sensing approach proposed here. In the future, these findings may be used to develop a monitoring/inspection smartphone application to assess the axial stress of slender structures, to predict the neutral temperature of continuous welded rails, or to prevent thermal buckling.

Camera vibration measurement using blinking light-emitting diode array

We present a new method for measuring camera vibrations such as camera shake and shutter shock. This method successfully detects the vibration trajectory and transient waveforms from the camera image itself. We employ a time-varying pattern as the camera test chart over the conventional static pattern. This pattern is implemented using a specially developed blinking light-emitting-diode array. We describe the theoretical framework and pattern analysis of the camera image for measuring camera vibrations. Our verification experiments show that our method has a detection accuracy and sensitivity of 0.1 pixels, and is robust against image distortion. Measurement results of camera vibrations in commercial cameras are also demonstrated.

Realistic limits for subpixel movement detection

Object tracking with subpixel accuracy is of fundamental importance in many fields since it provides optimal performance at relatively low cost. Although there are many theoretical proposals that lead to resolution increments of several orders of magnitude, in practice this resolution is limited by the imaging systems. In this paper we propose and demonstrate through simple numerical models a realistic limit for subpixel accuracy. The final result is that maximum achievable resolution enhancement is connected with the dynamic range of the image, i.e., the detection limit is 1/2^∧(nr.bits). The results here presented may aid in proper design of superresolution experiments in microscopy, surveillance, defense, and other fields.

Method for targetless tracking subpixel in-plane movements

We present a targetless motion tracking method for detecting planar movements with subpixel accuracy. This method is based on the computation and tracking of the intersection of two nonparallel straight-line segments in the image of a moving object in a scene. The method is simple and easy to implement because no complex structures have to be detected. It has been tested and validated using a lab experiment consisting of a vibrating object that was recorded with a high-speed camera working at 1000 fps. We managed to track displacements with an accuracy of hundredths of pixel or even of thousandths of pixel in the case of tracking harmonic vibrations. The method is widely applicable because it can be used for distance measuring amplitude and frequency of vibrations with a vision system.