Received Vibration Signals Research Articles

Precise disturbance localization of long distance fiber interferometer vibration senor based on an improved time–frequency variation feature extraction scheme

Locating intrusions with high accuracy is crucial for prompting long distance distributed optical fiber vibration sensing systems to practical applications in engineering filed. To achieve the high measurement accuracy and real-time performance simultaneously, in this paper, an intensity-modulated ultra-long distance distributed optical fiber vibration sensing system using an asymmetric dual Mach-Zehnder interferometer is experimentally established. More importantly, a positioning approach based on an ameliorated time–frequency feature extraction scheme is proposed with both theoretically analysis and experimental demonstration. In particular, an endpoint detection algorithm based on differential operation is firstly utilized to rapidly and accurately locate the endpoints of the received vibration signals. Thus the large-band width signals can be successfully acquired by setting an approximate window. Then, the ameliorated time–frequency feature extraction scheme based on a maximal overlap discrete wavelet packet transform is applied to eliminate the asymmetry of the large-band width signals, as it could lead to an erroneous positioning result when directly using a cross-correlation time delay estimation algorithm. Finally, the vibration positions along the sensing fiber link of the established sensing system can be successfully demodulated with the help of the extracted time–frequency features. To validate the effectiveness of the proposed scheme, vibration positioning tests were conducted in a real perimeter security scenario. And the results show that a mean positioning error of 11.12 m and a mean processing time of 0.276 s have been realized in a sensing length of 101 km. Thus, it is believed that through continuous technological advancements and refine algorithm optimization, the long distance distributed optical fiber vibration sensor founded on the proposed positioning strategy is poised to play an important role in the practical engineering applications.

Identification of temporal anomalies of spectrograms of vibration measurements of a turbine generator rotor using a recurrent neural network autoencoder

A method is proposed for recognizing pre-emergency conditions of rotary installations based on the use of the Hamming window and advanced Deep Learning techniques in retrospective analysis of the results of accounting for the factors of operation of a turbine generator, diagnostics and control under critical impacts. A program of experimental studies on the model of a turbine plant with simulation of faults and receiving vibration signals has been developed. An experiment based on the homostatic method of checking the signal with Hamming windows, in the frequency, time and modulation domains and common initial data, allows one to determine the most promising signal characteristics for identification. A method has been developed for monitoring the state of turbine generators in an automatic mode for timely notification of the CHPP personnel about the appearance of signs of pre-emergency situations, as well as about the nature of faults by the method of predicting the state of a pre-emergency situation using convolutional neural networks implemented in the form of a recurrent autoencoder. Clustering is applied and clusters are identified that correspond to the spectrograms of pre-emergency situations. The effectiveness of the use of the homostatic method in combination with correlation analysis is based on the decision-making model described in more detail in other works.

ІНТЕРПРЕТАЦІЯ ВІБРАЦІЙНИХ СИГНАЛІВ СКЛАДНОЇ РОТОРНОЇ СИСТЕМИ НА ОСНОВІ ФРАКТАЛЬНОГО АНАЛІЗУ

The work analyzes vibration signals obtained by simulating a turbine of a complex rotor system, for example, an aviation gas turbine engine, under conditions of stationary and non-stationary excitations. Four modes of vibration excitation are considered: stationary poly-harmonic excitation with the frequency of rotor rotation and super-harmonic components; stationary poly-harmonic excitation with the frequency of rotor rotation and sub-harmonic components; non-stationary vibration excitation with a linear increase in the rotor speed with super-harmonic and sub-harmonic components of the instantaneous rotor speed. In the course of the turbine model, vibration signals are generated, which are further analyzed without taking into account and taking into account additive noise. For signal processing, fractal and time-scale (wavelet) analysis were used. The determination of the fractal structure of the simulated vibration signals is made based on R / S analysis, or the method of normalized scope, as a result of which the Hurst exponent is determined. The Hurst exponent is a number that is interpreted as the ratio of the “strength” of a trend to the signal noise level and is used in the study to interpret the received vibration signals. The results showed that the vibration signals obtained in all considered modes of vibration excitation without taking into account the additive noise, in terms of the Hurst exponent, are classified as anti-persistent trend-non-stable signals. Taking into account additive noise, the Hurst exponent increases, the vibration properties in stationary excitation modes approach persistence and the appearance of a trend, and in non-stationary vibration excitation signals approach to processes such as white noise. For the vibration signal obtained at stationary poly-harmonic excitation with super-harmonic components, a preliminary wavelet - decomposition was carried out into a set of approximations and details, followed by determination of the Hurst exponent for each element of decomposition. The results obtained showed an ambiguous change in the Hurst exponent for various decomposition elements. The obtained results can be used to improve the methodological and algorithmic support systems for functional diagnostics of complex rotor systems with the appearance and propagation of damage to their rotating elements.

Partial least square/projection to latent structures (PLS) regression to estimate impact localization in structures

This paper presents results from the application of partial least squares/projection to latent structures (PLS) as a regression tool in order to estimate the localization of impacts in an aircraft structure using the strain wave produced by the impact and recorded by sensors attached to the structure. PLS is a technique that maximizes the covariance between the predictor matrix X and the predicted matrix Y for each component of the space. The main objectives of PLS are: to model X and Y, and to predict Y from X. The structure used in this work can be considered as a small scale version of a part of an aircraft wing. A total of 574 experiments were performed impacting the wing over its surface and receiving vibration signals from nine sensors. The data set (time history signal) is organized in a matrix to be used as predictors, while the predicted matrix is given by the real localization of the impact (x, y coordinates). Experiments are divided into four groups depending on their localization and probability of occurrence. A PLS model is built using three of these groups (X and Y) and tested using the remaining group. Results are presented, discussed and compared with other methods.

Intelligence-based condition monitoring model

Condition-based maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on changes in equipment condition (e.g., changes in vibration, changes in power usage, changes in operating performance, changes in temperatures, changes in noise levels, changes in chemical composition, increase in debris content and changes in volume of material). In this study, we present the newly developed condition monitoring model (CMM) based on Bayesian decision theory, which takes vibration signals from the equipment, and classifies them to either normal or abnormal condition. Using conditional risk function, the equipment condition can be classified to either normal or abnormal condition. The conditional risk function is calculated based on loss table and the class posterior probabilities. The developed model can efficiently avoid unnecessary maintenance and make timely actions through analysing the received vibration signals from the equipment. An illustrative example is demonstrated to present the application of the model. Also, the results derived from CMM programme coded in Visual Basic are discussed.

The influence of drone physical condition on the likelihood of receiving vibration signals from worker honey bees, Apis mellifera

Honey bee workers will perform vibration sig- nals on adult drones, which respond by increasing the time spent receiving trophallaxis. Because trophallaxis provides the proteins for sexual maturation, workers could direct vibration signals towards drones showing certain physical characteristics, potentially influencing drone development and colony reproductive output. We examined the influence of drone condition on the likelihood of receiving vibration signals by comparing body weight, protein concentrations, and hemolymph juvenile hormone (JH) titers between drones that received the vibration signal and same-age, non- vibrated controls. Vibrated and control drones did not differ in total body weight, abdomen weight, abdomen-to-body weight ratio, total protein concentrations, or hemolymph JH titers. In contrast, vibrated drones had significantly lower thorax weight and smaller thorax-to-body weight ratios compared with controls. Because relative thorax weight may affect flight ability and mating success, workers could use the vibration signal to increase the care received by less developed drones, potentially contributing to the production of greater numbers of competitive males. However, the differences in thorax weights, while significant, were very small, and it is unknown how such slight differences might be assessed by workers or affect drone performance. Nev- ertheless, vibration signals performed on drones may pro- vide opportunities for exploring the effect of the quality of reproductive individuals on caste interactions in honey bees.

Communication signals used in worker–drone interactions in the honeybee, Apis mellifera

Although the use of communication signals during worker–queen interactions in honeybees have been studied in detail, signals involved in worker–drone interactions have received less attention. We determined whether workers perform piping and the vibration signal on drones, and whether drones respond in ways that potentially enhance their mating success. Workers did not produce piping on drones, but they did perform vibration signals. Approximately 50% of the drones examined received vibration signals, and drones were more likely to be vibrated when they were sexually immature than when they were mature. Drones responded to the vibration signal by increasing movement and by interacting more with workers, which contributed to an increase in the proportions of time that they received trophallaxis and grooming. Because trophallaxis supplies nutrients necessary for sexual maturation, and because grooming may also contribute to drone health, workers could potentially use the vibration signal to promote development and mating performance in drones. We found no evidence that the vibration signal was associated with drone flight. The majority of signals were performed on drones in the morning several hours before flight activity, there was no relationship between the level of vibration activity experienced by drones in the morning and flight activity later that same day, and drones were rarely vibrated when returning to the nest between consecutive mating flights. In many social insect species, reproductive males are fed through trophallaxis and workers perform vibrational signals to influence activity of nestmates. Tactile, vibratory signals may therefore provide mechanisms for investigating the role of worker–drone interactions in colony reproductive decisions for a wide array of species.

Multiway Partial Least Square (MPLS) to estimate impact localization in structures

This paper presents results from the application of Multiway Partial Least Square (MPLS) as a regressor tool in order to estimate the localization of impacts in an aircraft structure. MPLS is a technique that maximizes the covariance between the predictor matrix X and the predicted matrix Y for each component of the space. The structure can be considered as a small scale version of part of a wing aircraft. 574 experiments were performed impacting the wing over its surface and receiving vibration signals from nine sensors. Experiments are divided in four groups depending on their localization and probability of occurrence. A PLS model is build using three of these groups and tested using the remaining group. Results are presented, discussed and compared with results of other methods.

Vibration-based NVH control during idle operation of an automobile powertrain

A system and method of operating a vehicle powertrain that employs active control to reduce NVH, particularly during idle. The method includes selectively operating the powertrain in at least a non-idle condition and an idle condition; receiving vibration signals from a sensor disposed on an internal combustion engine; controlling spark timing of the internal combustion engine based on vibration signals received from the sensor; and during the idle condition, modifying a speed and/or a load of the internal combustion engine based on vibration signals received from the sensor.

The Vibration Signal and Juvenile Hormone Titers in Worker Honeybees, Apis mellifera

AbstractWe examined the association between the vibration signal and juvenile hormone (JH) titers of honeybees by comparing vibrated recipients and non‐vibrated control workers that had been matched for age, colony of origin, and time of collection. Recipients collected at the moment they received vibration signals (0‐min bees) did not have higher JH titers compared with controls, which suggests that a worker's initial JH level did not influence its likelihood of receiving signals. In contrast, JH titers in workers collected 15–30 min after receiving vibration signals were slightly, but significantly higher than those of controls monitored for the same amount of time. These trends were consistent among colonies, despite the fact that we collected different age ranges of workers and observed pronounced variation in JH titers within and between the 0‐ and 15–30‐min groups of bees. Thus, over a broad age range of workers the vibration signal may contribute to elevated JH levels, and this effect does not occur because recipients have higher titers at the moment they receive signals. Because JH affects response thresholds in honeybees, increased titers elicited by the vibration signal may allow the signal to influence the performance of a variety of tasks in different worker age groups.

Monitoring vibrations in a pipeline network

The invention relates to acoustic detection of leaks in a pipeline using locally-intelligent, data-adaptive monitors deployed for a period of time. Monitors are initialized by a base station. Following deployment, monitors receive vibration signals at programmed times, process the vibration signals to detect and characterize abnormal vibrations, and save the processed data in the monitor. Monitors may communicate digitally with a base station, sending processed data and receiving instructions. Alternatively, monitors may be removed from the pipeline and placed in a docking station prior to initialization and subsequent re-deployment on a pipeline. A procedural step of re-synchronization corrects for any mis-alignments in time that may occur among received vibration signals from different monitors. Received vibration signals from two or more monitors may then be analyzed using a correlative method to localize the position of any leaks present in the pipeline.

Factors influencing the selection of recipients by workers performing vibration signals in colonies of the honeybee,Apis mellifera

The vibration signal of the honeybee functions as ‘modulatory communication’ because it elicits a general increase in activity that may help integrate the behaviour of workers that perform different, interrelated task sets. Workers that produce vibration signals contact numerous other bees, some of which receive the signal while others are ‘bypassed’ (antennated but not vibrated). Vibrating bees may therefore select among potential recipients. We monitored vibration signal behaviour in six observation colonies to investigate the possible selection of recipients by vibrating bees and the factors that might influence these choices. Vibrating bees roamed throughout the nest and bypassed more than half of all workers contacted. Vibration signals were not directed towards specific worker age groups. There were no differences in the mean age of vibrated versus bypassed workers or the proportion of recipients that were of preforaging versus foraging age. The likelihood of receiving vibration signals was influenced by recipient activity level. Significantly more workers were vibrated if they were inactive versus active when contacted by a signalling bee. Signal production was not consistently influenced by relatedness. Vibrating bees from only a single patriline in one of our study colonies were more likely to perform signals on supersisters than on half-sisters. In all other colonies no kin preferences were observed during signal performance. Thus, vibrating bees may choose among potential recipients and direct their signals towards inactive workers of all ages and levels of relatedness. This, in combination with their movement throughout the colony, could help to activate simultaneously multiple worker groups that perform interdependent tasks, but which may be spatially segregated in the nest.