scholarly journals Measuring System of Vibration Diagnostic with the Metrological Self-Control Function

2019 ◽  
Vol 9 (1) ◽  
pp. 6639-6646
Keyword(s):  
Control Function ◽  
Vibration Signal ◽  
Diagnostic Methods ◽  
Self Control ◽  
Measuring System ◽  
Vibration Measurement ◽  
Control Mode ◽  
Structural System ◽  
Time Frequency ◽  
Developing System

In the article, there is given the description of the method of metrological self-control in the measuring system of vibration diagnostic, the structural system of scheme is shown and the peculiarities of its functioning is self-control mode are given. The diagnostic methods, discussed in the article, usually are not completely independent and show the bigger effectiveness in the combined usage. The following problem exists: in the early stages of the appearing of a defect the informative components of vibration signals have extremely small amount of the energy and are covered with the background noise. Therefore, the effective method of the signal processing should assume the extraction of the informative signs of a damage when the signal/noise ratio is less than 1. Currently the biggest interest is presented by the group of the time frequency methods because they allow localizing in time the peculiarities of a vibration signal, therefore, they are potentially more sensitive to occurring defects than the time and spectral methods. Among their disadvantages are the mathematical complexity and the complexity of the realization and the interpretation of the results. The main advantage of developing system is the presence of the high frequency vibration measurement channel and also the built-in functional self-diagnosis system which principle is experimentally confirmed.

scholarly journals Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881675 ◽  
Author(s):  
Hsiung-Cheng Lin ◽  
Yu-Chen Ye
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Vibration Measurement ◽  
Time Frequency ◽  
Diagnosis And Prognosis ◽  
Vibration Signal Analysis ◽  
Critical Components ◽  
Comparative Results

The rolling element bearing is one of the most critical components in a machine. Vibration signals resulting from these bearings imply important bearing defect information related to the machinery faults. Any defect in a bearing may cause a certain vibration with specific frequencies and amplitudes depending on the nature of the defect. Therefore, the vibration analysis plays a key role for fault detection, diagnosis, and prognosis to reach the reliability of the machines. Although fast Fourier transform for time–frequency analysis is still widely used in industry, it cannot extract enough frequencies without enough samples. If the real frequency does not match fast Fourier transform frequency grid exactly, the spectrum is spreading mostly among neighboring frequency bins. To resolve this drawback, the recent proposed enhanced fast Fourier transform algorithm was reported to improve this situation. This article reviews and compares both fast Fourier transform and enhanced fast Fourier transform for vibration signal analysis in both simulation and practical work. The comparative results verify that the enhanced fast Fourier transform can provide a better solution than traditional fast Fourier transform.

A Frequency-Shift Synchrosqueezing Method for Instantaneous Speed Estimation of Rotating Machinery

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Songtao Xi ◽  
Hongrui Cao ◽  
Xuefeng Chen ◽  
Xingwu Zhang ◽  
Xiaoliang Jin
Keyword(s):  
Frequency Shift ◽  
Viterbi Algorithm ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Vibration Measurement ◽  
Frequency Resolution ◽  
Estimation Methods ◽  
Real Time Control ◽  
Time Frequency ◽  
Instantaneous Speed

Instantaneous speed (IS) measurement is crucial in condition monitoring and real-time control of rotating machinery. Since the direct measurement of instantaneous rotating speed is not always available, the vibration measurement has been used for indirect estimation methods. In this paper, a novel indirect method is proposed to estimate the IS of rotating machinery. First, a frequency-shift synchrosqueezing transform is proposed to process the vibration signal to obtain the time–frequency (TF) representation. Second, the Viterbi algorithm is employed to extract the shifted instantaneous frequency (IF) from the TF representation. Finally, the extracted IF is used to recover the IF of the measured vibration signal. The IS of rotating machinery can be calculated from the estimated IF. The proposed method is validated with both numerical simulations and experiments. The results show that the proposed method could provide much higher frequency resolution, better TF concentration results, and more accurate IF estimation of the considered signal compared with the synchrosqueezing method. Furthermore, the proposed method was confirmed to be less sensitive to noise, especially for high-frequency components.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Wei ◽  
Xuwen Jing ◽  
Bingqiang Li ◽  
Chao Kang ◽  
Zhenhuan Dou ◽  
...  
Keyword(s):  
Vibration Signal ◽  
Rotating Machinery ◽  
Second Order ◽  
Variable Speed ◽  
Short Time Fourier Transform ◽  
Stationary Characteristic ◽  
Nonstationary Signals ◽  
Time Frequency ◽  
Effective Technology ◽  
Short Time

AbstractIn recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.

scholarly journals Learning to See the Vibration: A Neural Network for Vibration Frequency Prediction

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2530 ◽  
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang
Keyword(s):  
Neural Network ◽  
Vibration Frequency ◽  
Frequency Estimation ◽  
Vibration Signal ◽  
Frequency Measurement ◽  
Optical Methods ◽  
Vibration Measurement ◽  
Vibration Signals ◽  
Engineering Practices ◽  
To Receive

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrate that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

scholarly journals Self Control and Server-Supervisory Control for Multiple Mobile Robots and its Applicability to Intelligent DNC System

Robotics ◽  
2013 ◽  
pp. 375-390
Author(s):  
F. Nagata ◽  
T. Yamashiro ◽  
N. Kitahara ◽  
A. Otsuka ◽  
K. Watanabe ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Control Systems ◽  
Supervisory Control ◽  
Low Cost ◽  
Self Control ◽  
Control Mode ◽  
Actual System ◽  
Multiple Mobile Robots ◽  
Collective Behaviors ◽  
Nc Machine

Multiple mobile robots with six PSD (Position Sensitive Detector) sensors are designed for experimentally evaluating the performance of two control systems. They are self-control mode and server-supervisory control mode. The control systems are considered to realize swarm behaviors such as Ligia exotica. This is done by using only information of PSD sensors. Experimental results show basic but important behaviors for multiple mobile robots. They are following, avoidance, and schooling behaviors. The collective behaviors such as following, avoidance, and schooling emerge from the local interactions among the robots and/or between the robots and the environment. The objective of the study is to design an actual system for multiple mobile robots, to systematically simulate the behaviors of various creatures who form groups such as a school of fish or a swarm of insect. Further, the applicability of the server-supervisory control scheme to an intelligent DNC (Direct Numerical Control) system is briefly considered for future development. DNC system is an important peripheral apparatus, which can directly control NC machine tools. However, conventional DNC systems can neither deal with various information transmitted from different kinds of sensors through wireless communication nor output suitable G-codes by analyzing the sensors information in real time. The intelligent DNC system proposed at the end of the chapter aims to realize such a novel and flexible function with low cost.

scholarly journals Results of diagnosing the level of innovative competence for secondary school teachers

2020 ◽  
Keyword(s):  
Secondary Education ◽  
Secondary School Teachers ◽  
Diagnostic Procedures ◽  
Educational Process ◽  
Diagnostic Methods ◽  
Self Control ◽  
Diagnostic Tools ◽  
Problem Situations ◽  
Competence Evaluation ◽  
Professional Self

One of the higher-priority directions concerning improvement of the institution of general secondary education is the adaptation of teachers’ educational, scientific methodological and organizational activities to the modern challenges. The orientation of the new Ukrainian school to the world educational standards calls for new approaches to the organization of the educational process in institutions of general secondary education, the formation of up-to-date effective innovative competence of teacher-practitioners. In order to diagnose the level of formation of teachers’ innovative competence a summative experiment was conducted as a method to obtain and fix theoretical and empirical data. In the course of the research the criteria for innovative competence evaluation were developed, diagnostic tools were selected, diagnostic procedures and the analysis of the obtained results were conducted. A methodological workshop was held for teachers aimed at defining such basic concepts as «innovative pedagogical activity», «competence», «innovative competence» The level of the formation of teachers’ innovative competence was diagnosed taking into account its components, namely: operational (the formation of intellectual abilities, the quickness of mental operations, ability to solve problem situations), professionalvalue (motivation to obtain innovative competences, professional orientation, ability to self-control and urge for constant professional self- improvement, formation of individual style of pedagogical activity, professionally important personal qualities). Having generalized the results of applying various diagnostic methods (the technique «Six Thinking Hats» by Edward de Bono, the technique of studying the value scales by M. Rokeach, the diagnostic test «Readiness for self-development», «Strategies to form a higher level of basic need for cognition» by Palchevsky, «The methodology of limit meanings» by D. Leontiev) the level of formation of innovative competence of the teacher of the institution of general secondary education was determined.

Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform and Its Application in Wind Turbine Gearbox Fault Diagnosis

2021 ◽  
Author(s):  
Zhaohong Yu ◽  
Cancan Yi ◽  
Xiangjun Chen ◽  
Tao Huang
Keyword(s):  
Wavelet Transform ◽  
Wind Turbine ◽  
Time Window ◽  
Vibration Signal ◽  
Second Order ◽  
Center Frequency ◽  
Wind Turbine Gearbox ◽  
Adaptive Wavelet ◽  
Time Frequency ◽  
Window Width

Abstract Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing Time-Frequency (TF) Analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated Time-Frequency Representation (TFR), this paper proposes a new TFA method, namely Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform (AMWSST2). Firstly, a short-time window is innovatively introduced on the foundation of classical Continuous Wavelet Transform (CWT), and the window width is adaptively optimized by using the center frequency and scale factor. After that, a smoothing process is carried out between different segments to eliminate the discontinuity and thus Adaptive Wavelet Transform (AWT) is generated. Then, on the basis of the theoretical framework of Synchrosqueezing Transform (SST) and accurate Instantaneous Frequency (IF) estimation by the utilization of second-order local demodulation operator, Adaptive Second-order Synchrosqueezing Wavelet Transform (AWSST2) is formed. Considering that the quality of actual time-frequency analysis is greatly disturbed by noise components, through performing multiple Synchrosqueezing operations, the congregation of TFR energy is further improved, and finally, the AMWSST2 algorithm studied in this paper is proposed. Since Synchrosqueezing operations are performed only in the frequency direction, this method AMWSST2 allows the signal to be perfectly reconstructed. For the verification of its effectiveness, this paper applies it to the processing of the vibration signal of the gearbox of a 750 kW wind turbine.

Assessment of delamination location in composite laminates based on a chaotic oscillator method

2021 ◽  
Vol 263 (2) ◽  
pp. 4709-4716
Author(s):  
Xuan Li ◽  
Dunant Halim ◽  
Xiaoling Liu
Keyword(s):  
Material Properties ◽  
Composite Laminates ◽  
Damage Index ◽  
Vibration Signal ◽  
Harmonic Excitation ◽  
Vibration Measurement ◽  
Chaotic Oscillator ◽  
Signal Characteristics ◽  
High Level ◽  
Delamination Damage

The work aims to study the assessment of delamination location in composite laminates using vibration measurement with a chaotic oscillator method. Delamination is a type of damage that commonly occurs in composite laminates, which can cause a severe degradation of their material properties. The traditional vibration-based methods can encounter difficulties in detecting and locating these delamination-type damages especially when the size of delamination is relatively small and there is a significant level of noise in its vibration measurement. With this particular consideration, a vibration-based method using a non-linear chaotic oscillator was used in this study due to its sensitivity to the change in vibration signal characteristics. A numerical model of composite laminates with delamination damage under harmonic excitation was developed and the vibration signal obtained from composite laminates was processed using the chaotic oscillator method. A feature named Lyapunov Exponent (LE) was used as a delamination damage index to describe the characteristics of the chaotic oscillator for cases with delamination at varying structural locations. The effects of delamination locations on the developed damage index were analyzed in this work. The results showed that there was a strong correlation between the delamination location and the LE feature, even for the case with a relatively high level of measurement noise. The results demonstrated the effectiveness of the method to identify delamination in composite laminates, which has also the potential to be used to detect other types of damages.

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