scholarly journals Monitoring of the Looseness in Cargo Bolts under Random Excitation Based on Vibration Transmissibility

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guangming Dong ◽  
Jin Chen ◽  
Fagang Zhao
Keyword(s):  
Early Stage ◽  
Vibration Signal ◽  
Random Excitation ◽  
Industrial Applications ◽  
System Matrix ◽  
Electromechanical Impedance ◽  
Manual Inspection ◽  
Torque Wrench ◽  
Vibration Transmissibility ◽  
Physical Principles

Bolted joints are widely used in industrial applications and joint failure can cause a disastrous accident if loosening happens. Bolt loosening detection can be made by regular manual inspection or by using sensors based on different physical principles, such as acoustoelastic effect, piezoelectric active sensing, and electromechanical impedance. Compared with the above methods, vibration based bolt looseness monitoring using accelerometers is appealing for its economy and convenience for measurement. In this paper, cargo bolts looseness monitoring under random excitation is studied based on vibration transmissibility, which overcomes the drawback of commonly used vibration methods in finding local bolt looseness. Vibration transmissibility analysis only uses two vibration transducers to monitor bolt group looseness, where the vibration signal below the cargo bolts is used as the “input” and the other one above the cargo bolts is used as the “output.” There are 12 bolts in the cargo bolts studied in this paper, providing an essential clamping force to fix cargo during transportation. Six kinds of bolt group looseness with an increasing degree are simulated in the experiment. The experimental analysis shows that variation of the spectral moment can be used to monitor the global variation of the torque wrench exerted on the cargo bolts. The early stage of the bolt group looseness is that some one or two bolts begin to loose; however, the spectrum moment factor is insensitive to the local bolt looseness in the bolt group. To address this issue, the eigensystem realization algorithm (ERA) based on random input and output is utilized to find the subtle eigenvalue variation of the system matrix, which is neglected by the frequency transmissibility function. The experimental results show the effectiveness of the proposed method for detecting local bolt looseness.

scholarly journals HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 660 ◽  
Author(s):  
Fang Liu ◽  
Liubin Li ◽  
Yongbin Liu ◽  
Zheng Cao ◽  
Hui Yang ◽  
...  
Keyword(s):  
Kernel Method ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Industrial Applications ◽  
Performance Degradation ◽  
Support Vector ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

scholarly journals A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3309 ◽  
Author(s):  
Zia Ullah ◽  
Jin Hur
Keyword(s):  
Fault Diagnosis ◽  
Early Stage ◽  
Short Circuit ◽  
Industrial Applications ◽  
Operational Environment ◽  
Compact Structure ◽  
Torque Density ◽  
Prime Concern ◽  
Attractive Option ◽  
The Impact

Contemporary research has shown impetus in the diagnostics of permanent magnet (PM) type machines. The manufacturers are now more interested in building diagnostics features in the control algorithms of machines to make them more salable and reliable. A compact structure, exclusive high-power density, high torque density, and efficiency make the PM machine an attractive option to use in industrial applications. The impact of a harsh operational environment most often leads to faults in PM machines. The diagnosis and nipping of such faults at an early stage have appeared as the prime concern of manufacturers and end users. This paper reviews the recent advances in fault diagnosis techniques of the two most frequently occurring faults, namely inter-turn short fault (ITSF) and irreversible demagnetization fault (IDF). ITSF is associated with a short circuit in stator winding turns in the same phase of the machine, while IDF is associated with the weakening strength of the PM in the rotor. A detailed literature review of different categories of fault indexes and their strengths and weaknesses is presented. The research trends in the fault diagnosis and the shortcomings of available literature are discussed. Moreover, potential research directions and techniques applicable for possible solutions are also extensively suggested.

scholarly journals The Role of Methyl Groups in the Early Stage of Thermal Polymerization of Polycyclic Aromatic Hydrocarbons Revealed by Molecular Imaging

2020 ◽  
Author(s):  
Pengcheng Chen ◽  
Shadi Fatayer ◽  
Bruno Schuler ◽  
Jordan N. Metz ◽  
Leo Gross ◽  
...  
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Early Stage ◽  
Complex Mixture ◽  
Thermal Polymerization ◽  
Industrial Applications ◽  
Thermal Reactions ◽  
Force Microscopy ◽  
Wide Range ◽  
Polycyclic Aromatic

The initial thermal reactions of aromatic hydrocarbons are relevant to many industrial applications. However, tracking the growing number of heavy polycyclic aromatic hydrocarbon (PAH) products is extremely challenging because many reactions are unfolding in parallel from a mixture of molecules. Herein, we studied the reactions of 2,7-dimethylpyrene (DMPY) to decipher the roles of methyl substituents during mild thermal treatment. We found that the presence of methyl substituents is key for reducing the thermal severity required to initiate chemical reactions in natural molecular mixtures. A complex mixture of thermal products including monomers, dimers, and trimers were characterized by NMR, mass spectrometry and non-contact atomic force microscopy (nc-AFM). A wide range of structural transformations including methyl transfer and polymerization reactions were identified. A detailed mechanistic understanding was obtained on the roles of H radicals during the polymerization of polycyclic aromatic hydrocarbons.

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2955 ◽  
Author(s):  
Mario de Oliveira ◽  
Andre Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

2009 ◽  
Author(s):  
Young-Sun Hong ◽  
Gil-Yong Lee ◽  
Young-Man Cho ◽  
Sung-Hoon Ahn ◽  
Chul-Ki Song
Keyword(s):  
Wavelet Packet ◽  
Position Effect ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Wavelet Packet Transform ◽  
Bevel Gear ◽  
Diagnostic Methods ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fault Features

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

Utilizing Direct Tension Squirter Indicators in Industrial Applications

2005 ◽  
Author(s):  
Ron Frisard
Keyword(s):  
New Technology ◽  
Industrial Applications ◽  
Ease Of Use ◽  
Bolted Joints ◽  
Direct Tension ◽  
Root Cause ◽  
Torque Wrench ◽  
The World ◽  
Industry Applications ◽  
Visual Indication

In the world of critical bolting applications there is a need for achieving correct gasket loads. The new technology of Direct Tension Squirter Washers is designed to show visual indication when 60% of yield for a B7 bolt has been reached. Torque wrench inconsistencies have caused many gasket failures. There are many times the incorrect gasket load has been applied to bolted joints resulting in joint failure. The root cause of these failures range from bad calculation of friction for anti seize to damaged threads. DTSW ensures the bolts have reached the correct bolting load resulting in a properly loaded gasket that will not be doomed for failure. This paper will review the DTSW Technology and how it can be applied in Industry applications. It will discuss the inaccuracy of torque wrench technology in today’s industrial bolting environment. The paper will also highlight show the DTSW technology ease of use compared to typical Indicator washers that require feeler gauges.

An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

2014 ◽  
Author(s):  
Wei Guo
Keyword(s):  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Similarity Criterion ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Combination Technique ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Eemd Method ◽  
Effective Signal

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.

BLOCKCHAIN TECHNOLOGY — INNOVATION AND SECURITY

2020 ◽  
Vol 136 (4) ◽  
pp. 60-78
Author(s):  
GRZEGORZ BOROWIK ◽  
ZBIGNIEW WAWRZYNIAK ◽  
PAWEŁ CICHOSZ
Keyword(s):  
Business Processes ◽  
Technology Innovation ◽  
Early Stage ◽  
Industrial Applications ◽  
Third Party ◽  
Main Trend ◽  
Computer Language ◽  
Administrative Costs ◽  
Blockchain Technology ◽  
Scripting Language

Blockchain is one of the most revolutionary technologies of the 21st century, which is still under development, and whose potential is not yet fully exploited. Although blockchain gained importance in 2009, scientists and entrepreneurs are still at an early stage of understanding its mechanisms and fully appreciating its potential, especially from the perspective of the technical challenges and limitations of the technology. Blockchain fi nds a variety of applications, especially in areas that have so far been based on third-party transactions in order to maintain a certain level of trust. Although blockchain is a promising technology for the reorganisation of business processes and many industrial applications, it still has many weaknesses despite various implementations in many forms. An innovative element, and one of the most attractive functions, of blockchain is intelligent contracts, as they reduce or even completely eliminate the administrative costs associated with the lack of trust in the transaction. However, the existing software that is built on this infrastructure has many shortcomings and unfortunately, combined with the lack of maturity of the scripting language to write the contract representation in the computer language, leads to errors or gaps in security that are not noticed or addressed by the author of the script. So far, no blockchain-based system has been completely broken. Nevertheless, phishing is the main trend in the operation of criminals in blockchain networks. Research has shown that over $115 million has been stolen from nearly 17,000 victims in the Ethereum blockchain alone. It is estimated that in total about 10% of the money invested in Ethereum’s ICO has ended up in the hands of criminals.

Analysis and Sensitivity Evaluation of AE Signals and Vibration Signals for Fault Diagnosis of Low-Speed Rotating Machinery

2011 ◽  
Vol 128-129 ◽  
pp. 79-84 ◽  
Author(s):  
Hai Yang Jiang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Early Stage ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Low Speed ◽  
Rotating Machine ◽  
Sensitivity Evaluation ◽  
Ae Signal ◽  
Ae Signals

The fault detection of low-speed rotating machine is very difficult at the early stage. Because it often breaks down suddenly, there are many problems in the planned maintenance of low-speed rotating machinery in industry plants. In order to detect fault of low-speed rotating machinery as earlier as possible, this paper proposes a sensitivity evaluation method of fault diagnosis by using symptom parameters and frequency analysis of vibration signal and acoustic emission (AE) signal measured for the diagnosis. The practical examples are shown for explaining the efficiency of the sensitivity evaluation method proposed in this paper.

scholarly journals Informativity of the Vibration Signal of the Spindle Unit of the Metal Cutting Machine

2021 ◽  
Vol 346 ◽  
pp. 03043
Author(s):  
Alexander Denisenko ◽  
Viktor Mikhailov
Keyword(s):  
Energy Balance ◽  
Power Flow ◽  
Metal Cutting ◽  
Early Stage ◽  
Vibration Signal ◽  
Balance Equations ◽  
Vibration Field ◽  
Spindle Unit ◽  
Urgent Task ◽  
Vibration Power Flow

Monitoring the condition of the spindle units of modern metal-cutting machines by methods of non-selective diagnostics involves the possibility of installing monitoring sensors in places with maximum vibration information. In this regard, the assessment of the informative value of the vibration field of the spindle unit, which can be carried out in advance, taking into account the design features, geometric and dimensional characteristics, is an urgent task. Based on the energy approach, a computational model based on the median planes of the walls is proposed using the example of a universal lathe spindle unit. On the basis of the calculated model, the energy balance equations are drawn up taking into account the conditions for the transmission of vibration power between the walls of the housing. The dependences that allow us to calculate the coefficients that take into account the absorption of vibration energy by the walls of the housing are given. The solution of the energy balance equations made it possible, based on the level of the vibration power flow, to rank the walls of the spindle unit body by information content,. The resulting model of the vibration field can be used to determine the reference values of vibration velocities that are formed from sources in the absence of defects. This will allow for non-selective diagnostics to detect the occurrence of a defect at an early stage, and in the presence of a defect to assess the level of its development.

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