Failure Diagnosis System for a Ball-Screw by Using Vibration Signals

Recently, in order to reduce high maintenance costs and to increase operating ratio in manufacturing systems, condition-based maintenance (CBM) has been developed. CBM is carried out with indicators, which show equipment’s faults and performance deterioration. In this study, indicator signal acquisition and condition monitoring are applied to a ball-screw-driven stage. Although ball-screw is a typical linearly reciprocating part and is widely used in industry, it has not gained attention to be diagnosed compared to rotating parts such as motor, pump, and bearing. First, the vibration-based monitoring method, which uses vibration signal to monitor the condition of a machine, is proposed. Second, Wavelet transform is used to analyze the defect signals in time-frequency domain. Finally, the failure diagnosis system is developed using the analysis, and then its performance is evaluated. Using the system, we estimated the severity of failure and detect the defect position. The low defect frequency (≈58.7 Hz) is spread all over the time in the Wavelet-filtered signal with low frequency range. Its amplitude reflects the progress of defect. The defect position was found in the signal with high frequency range (768~1,536 Hz). It was detected from the interval between abrupt changes of signal.

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Study on Remote Condition Monitoring and Fault Diagnosis System for Rotating Machinery Based on LabVIEW

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.430-432.1939 ◽

2012 ◽

Vol 430-432 ◽

pp. 1939-1942 ◽

Cited By ~ 1

Author(s):

Chuan Hui Wu ◽

Yan Gao ◽

Yu Guo

Keyword(s):

Fault Diagnosis ◽

Frequency Domain ◽

Condition Monitoring ◽

Vibration Signal ◽

Signal Acquisition ◽

Diagnosis System ◽

Time Frequency ◽

Machinery Condition Monitoring ◽

Temperature Signal ◽

Fault Diagnosis System

In order to suit the demand of monitoring and fault diagnosis of modern small and medium machinery devices better, this paper discusses the development of machinery condition monitoring and fault diagnosis system of good universality and strong expansibility using LabVIEW. Mainly illuminates vibration signal, temperature signal and electric current signal acquisition module using NI data acquisition hardware; signal analysis module in time domain, frequency domain and joint time–frequency domain using signal processing technology. DataSocket, database and fuzzy diagnosis technique have been utilized enabling this system to monitor and diagnose machinery fault remotely.

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An Improved Random Decrement Algorithm and Applications

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.4372 ◽

2011 ◽

Vol 121-126 ◽

pp. 4372-4376

Author(s):

Qing Wei Ye ◽

Zhi Min Feng ◽

Hai Gang Hu

Keyword(s):

Response Function ◽

Vibration Signal ◽

Recognition Algorithm ◽

Mode Analysis ◽

Time Signal ◽

Signal Acquisition ◽

Free Response ◽

Time Frequency ◽

Random Decrement Technique ◽

Random Decrement

The free response function is the foundation of mode analysis and recognition of vibration signal, and random decrement algorithm is the commonly used classical algorithm of extracting the free response function. But under the restriction of engineering conditions, it may be impossible for long-time signal acquisition, which makes the number of sample points fail to meet the requirements of the random decrement algorithm, causing the extracted free response signals to contain strong noise and other influencing factors. Aiming at the shortcomings of the existing random decrement technique, this paper proposes an improved random decrement algorithm based on multi-secant method, which can get satisfactory free response signals with short vibration response signals to provide excellent basis of analysis for the vibration mode recognition algorithm of various time-frequency domains. Actual engineering tests confirm that the improved algorithm greatly improves the precision of extracting free response signals while basically keeping the computation speed unchanged, it has high application value.

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Characteristics of Valuable Microseismic Events in Heading Face of an Underground Coal Mine Using Microseismic System

Shock and Vibration ◽

10.1155/2021/6683238 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Wenlong Zhang ◽

Tianhong Huo ◽

Chen Li ◽

Cunwen Wang ◽

Xiaocheng Qu ◽

...

Keyword(s):

Rock Burst ◽

Maximum Amplitude ◽

Vibration Signal ◽

Geological Structure ◽

Signal Acquisition ◽

Monitoring Method ◽

Event Duration ◽

Underground Coal Mine ◽

Basic Characteristics ◽

Monitoring Technologies

Rock burst monitoring of heading face is a weak aspect of rock burst monitoring in China; acoustic emission (AE) monitoring is one of the few monitoring technologies used in heading face, but its target signals are small energy events which are easy to be disturbed. Researchers usually focus on the weak AE events but ignore the microseismic (MS) events (different from AE event and caused by a larger scale of coal fracture), while this kind of events can also reflect the pressure situation of heading face and have higher energy value which may become a better indicator for rock burst monitoring of heading face. So, the basic characteristics of MS events in heading face are studied based on a running vibration signal acquisition system, including the occurrence position, main frequency range, maximum amplitude (MA) range, event duration, and relationship with geological structure. This paper provides a development basis of the monitoring method for rock burst monitoring of heading face by using MS events.

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Research of Fault Warning System of the Cooling Tower Fan Blade

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.42.183 ◽

2010 ◽

Vol 42 ◽

pp. 183-187 ◽

Cited By ~ 1

Author(s):

Jian Ping Chen ◽

Wei Dong

Keyword(s):

Real Time ◽

Cooling Tower ◽

Warning System ◽

Control Unit ◽

Pulse Signal ◽

Failure Diagnosis ◽

Signal Acquisition ◽

Monitoring Method ◽

Fault Monitoring ◽

Fan Blade

A new monitoring method is described on the basis of the technology of the cooling tower fan monitoring and failure diagnosis, the warning system uses microprogrammed control unit real-time monitoring the blade. A magnet was fixed on the tip of every blade and these magnets were uniformly distributed in the same plane. A Hall proximity switch was fixed on the air duct which was at the same plane with the magenet, and the Hall proximity switch was used as a signal acquisition sensor, and the pulse signal of the Hall proximity swith was collected through microprogrammed control unit. Using the timing and interrupt function of microprogrammed control unit, the pulse signal was analysed and processed, when the blade breaks down and deforms, the preprogramme shut down the fan and outputted a forecast alarm, so the blade accident would be prevented. It is proved that running state of the system is good by production and practice and the monitoring method can greatly improve the reliability of the fault monitoring and warning. The monitoring system has a wide application.

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

Electronics ◽

10.3390/electronics10111248 ◽

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.

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Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

Mathematical Problems in Engineering ◽

10.1155/2016/7616393 ◽

2016 ◽

Vol 2016 ◽

pp. 1-16 ◽

Cited By ~ 19

Author(s):

Irena Orović ◽

Vladan Papić ◽

Cornel Ioana ◽

Xiumei Li ◽

Srdjan Stanković

Keyword(s):

Signal Processing ◽

Fourier Transform ◽

Compressive Sensing ◽

Signal Reconstruction ◽

Signal Acquisition ◽

Transform Domain ◽

Hermite Transform ◽

Time Frequency ◽

Reconstruction Methods ◽

Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

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

Scientific Reports ◽

10.1038/s41598-021-96343-2 ◽

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.

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Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽

10.1190/geo2020-0842.1 ◽

2021 ◽

pp. 1-62

Author(s):

Wencheng Yang ◽

Xiao Li ◽

Yibo Wang ◽

Yue Zheng ◽

Peng Guo

Keyword(s):

Mathematical Morphology ◽

Signal To Noise Ratio ◽

Critical Role ◽

Real Data ◽

Monitoring Method ◽

Frequency Spectra ◽

Arrival Times ◽

Time Frequency ◽

Fracturing Process ◽

Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

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