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This study aims to reconstruct the frictional vibration signal from noise and characterize the running-in process by frictional vibration.

There is a strong correlation between tangential frictional vibration and normal frictional vibration. On this basis, a new frictional vibration reconstruction method combining cross-correlation analysis with ensemble empirical mode decomposition (EEMD) was proposed. Moreover, the concept of information entropy of friction vibration is introduced to characterize the running-in process.

Compared with the wavelet packet method, the tangential friction vibration and the normal friction vibration reconstructed by the method presented in this paper have a stronger correlation. More importantly, during the running-in process, the information entropy of friction vibration gradually decreases until the equilibrium point is reached, which is the same as the changing trend of friction coefficient, indicating that the information entropy of friction vibration can be used to characterize the running-in process.

The study reveals that the application EEMD method is an appropriate approach to reconstruct frictional vibration and the information entropy of friction vibration represents the running-in process. Based on these results, a condition monitoring system can be established to automatically evaluate the running-in state of mechanical parts.

The EEMD method was applied to reconstruct the frictional vibration. Furthermore, the information entropy of friction vibration was used to analysis the running-in process.

This paper presents experiment results that examine the validity of extracting blade vibration signature from the shaft torsional vibration signals. A special test rig was designed and manufactured for this objective. A set of strain gages were bonded to the shaft and to the blades to measure the shaft twisting and blade bending deformations respectively. A controlled frequency exciter excited the blade vibration. The shaft torsional and blade bending vibration signals were simultaneously recorded and presented in the time and frequency domains. The blade bending vibration frequencies appeared dominantly in the shaft torsional vibration signals for all blade vibration frequencies up to 100Hz. For frequencies higher than 100Hz, less sensitivity of the torsional vibration to blade vibration was observed.

The purpose of this paper is to validate mathematically the feasibility of extracting the rotating blades vibration condition from the shaft torsional vibration measurement.

A mathematical model is developed and simulated for extracting rotating blades vibration signatures from the shaft torsional vibration signals. The model simulates n‐blades attached to a rigid disk at setting angles and the shaft drives the disk is flexible in torsion. The model is developed using the multi‐body dynamics approach in conjunction with the Lagrangian dynamics. A three‐blade rotor system example is simulated for blades free and forced vibration under stationary and rotating conditions. Frequency spectrums for the shaft torsional and blades bending vibration are represented and studied for analysis verification purposes.

The torsional vibration frequency spectrums showed blades free and forced vibration signatures. The blade setting angle is shown to reduce the sensitivity of torsional vibration signal to blades vibration signatures as it increases. The torsional vibration signals captured the variation in blades properties and produced broadband frequency components for mistuned system. The shaft torsional rigidity is shown to reduce the sensitivity of torsional vibration signal to blades vibration if increased to extremely high values (approaching rigid shaft). The rotor inertia is shown to have less effect on the torsional vibration signals sensitivity. The method of torsional vibration as a tool for rotating blades vibration measurement, based on the proposed mathematical model and its simulation, is feasible.

There is a growing need for reliable predictive maintenance programs that in turn requires continuous development in methods for machinery health monitoring through vibration data collection and analysis. Turbo machinery and bladed assemblies like fans, marine propellers and wind turbine systems usually suffer from the problem of blades high vibration that is difficult to measure. The proposed new method for blades vibration measurement depends on the shaft torsional vibration signals and can be used also for verifying the signals from other types of bearings sensors for possible blades vibration condition monitoring.

This paper presents a unique mathematical model and simulation results for the rotating blades vibration monitoring. The developed model can be simulated for studying coupled blades vibration problems in the design stage as well as for condition monitoring in maintenance applications.

Vibration measurement is needed in many industrial production processes, such as equipment monitoring, fault diagnosis, and noise analysis and eliminating and so on. The purpose of this paper is to propose a simple vibration testing system which includes the laser, the string, position sensitive detector (PSD) and the corresponding signal processing circuit.

PSD is an optical semiconductor sensor that can fast locate the luminous spot position precisely, which means that it can output different electric current according to the luminous spot at different position of its surface. Moreover, the experiment on PSD sensor using different vibration source and frequency had been carried out. Finally, the vibration waveform of the luminous spot on PSD photosurface was obtained.

According to the experimental results, each kind of vibration parameter with different vibration source, such as vibration frequency and amplitude can be calculated.

The experimental results agreed with the actual parameter, which showed PSD not only had its own good qualities in the position measurement, but also had the unique superiority in the vibration measurement.

Denoising of the vibration signal is crucial to identify a structure's damage. Based on noise frequency character, the “real” vibration signal can be gotten. The purpose of this paper is to propose a novel method for denoising a signal based on the wavelet transform.

The vibration signal with noise which can be collected by wireless network is decomposed by wavelet transform. In order to select optimal level of wavelet decomposition, based on noise's frequency, power spectral density is used. A soft thresholding method based on minimum mean‐variance is used for vibration signal de‐noising with Gaussian noise.

A novel method has been described in his paper. Based on the relationship between vibration signal's character and noise frequency, the way to get rid of noise is combined wavelet transform with power spectral density.

In order to select optimal level of wavelet decomposition, based on noise's frequency, power spectral density is used. A soft thresholding method based on minimum mean‐variance is used for vibration signal denoising with Gaussian noise.

Due to the non‐stationarity of vibration signals resulting from either varying operating conditions or natural deterioration of machinery, both the frequency components and their magnitudes vary with time. However, little research has been done on the parameter estimation of time‐varying multivariate time series models based on adaptive filtering theory for condition‐based maintenance purposes. This paper proposes a state‐space model of non‐stationary multivariate vibration signals for the online estimation of the state of rotating machinery using a modified extended Kalman filtering algorithm and spectral analysis in the time‐frequency domain. Adaptability and spectral resolution capability of the model have been tested by using simulated vibration signal with abrupt changes and time‐varying spectral content. The implementation of this model to detect machinery deterioration under varying operating conditions for condition‐based maintenance purposes has been conducted by using real gearbox vibration monitoring signals. Experimental results demonstrate that the proposed model is able to quickly detect the actual state of the rotating machinery even under highly non‐stationary conditions with abrupt changes and yield accurate spectral information for an early warning of incipient fault in rotating machinery diagnosis. This is achieved through combination with a change detection statistic in bi‐spectral domain.

This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response signals of the bridge structure are collected using sensors placed at different nodes. The different damaged states such as no damage, single damage, double damage and triple damage are introduced by cutting members of the bridge. The masked noise with recorded vibration responses generates challenge to properly analyze the health of bridge structure.

The analytical modal properties are obtained from finite element model (FEM) developed using SAP2000 software. The response signals are analyzed in frequency domain by power spectrum and in time-frequency domain using spectrogram and Stockwell transform. Various low pass signal-filtering techniques such as variational filter, lowpass sparse banded (AB) filter and Savitzky–Golay (SG) differentiator filter are also applied to refine vibration signals. The proposed methodology further comprises application of Hilbert transform in combination with MUSIC and ESPRIT techniques.

The outcomes of SG filter provided the denoised signals using appropriate polynomial degree with proper selected window length. However, certain unwanted frequency peaks still appeared in the outcomes of SG filter. The SG-filtered signals are further analyzed using fused methodology of Hilbert transform-ESPRIT, which shows high accuracy in identifying modal frequencies at different states of the steel truss bridge.

The sequence of proposed methodology for denoising vibration response signals using SG filter with Hilbert transform-ESPRIT is a novel approach. The outcomes of proposed methodology are much refined and take less computational time.

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.

A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.

The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.

The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.

The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

The purpose of this paper is to propose a method to remit or mitigate deterioration resulting from the influence of short data length to existing signal extracting methods.

Careful design of the pre-filtering circuits to refrain most of the noise and disturbance and remove the influence of operation speed of the concerned balancing machine. Based on the analysis on the spectral feature of the unbalance vibration signal, a pre-filtering circuit is designed, then the signal extension method based on AR prediction model are discussed and used to prolong sampled signal.

With the extension method, sampled signal can be extended to required length to enhance the performance of refraining nearby frequency disturbance. The results of simulation and field experiments demonstrate the feasibility of the presented extension method.

Improved measurement efficiency of balancing machine and provided a method to trade off between measurement accuracy and measurement efficiency.

The paper presents a way to improve extraction accuracy and frequency resolution with limited cycles of unbalance vibration signal.

At present, one of the key equipment in pillar industries is a large rotating machinery. Conducting regular health monitoring is important for ensuring safe operation of the large rotating machinery. Because vibrations sensors play an important role in the workings of the rotating machinery, measuring its vibration signal is an important task in health monitoring. This paper aims to present these.

In this work, the contact vibration sensor and the non-contact vibration sensor have been discussed. These sensors consist of two types: the electric vibration sensor and the optical fiber vibration sensor. Their applications in the large rotating machinery for the purpose of health monitoring are summarized, and their advantages and disadvantages are also presented.

Compared with the electric vibration sensor, the optical fiber vibration sensor of large rotating machinery has unique advantages in health monitoring, such as provision of immunity against electromagnetic interference, requirement of less insulation and provision of long-distance signal transmission.

Both contact vibration sensor and non-contact vibration sensor have been discussed. Among them, the electric vibration sensor and the optical fiber vibration sensor are compared. Future research direction of the vibration sensors is presented.