Advantages Of Non-contact Measurement Research Articles

A recursive multi-head self-attention learning for acoustic-based gear fault diagnosis in real-industrial noise condition

Acoustic-based diagnosis (ABD) is a promising method for rotating machinery fault detection in real-industrial fields due to its advantage of non-contact measurement by air-couple. However, most ABD methods, which rely on single function module of denoising or anti-noise diagnosis, lacks sufficient ability against the strong and highly non-stationary background noise interference in practical industrial application. To deal with the obstacle, a novel two-stage ABD system, which performs denoising and anti-noise diagnosis in a step-by-step fashion, is proposed in this paper. In our method, the denoising sub-system can adaptively track and suppress real-industrial background noise from collected multi-channel signal by our designed recursive multi-head self-attention (RMHSA) mechanism, which take into account the global and long-range information of signal to recursively exclude noise component at each position in signal sequence for overcoming the limitation of traditional method in balancing denoising performance and computational complexity and discontinuity in diagnosis procedure. Subsequently, a novel anti-noise method is explored to automatically track the residual noise components after initial suppression and estimating the noise interference probability within time-frequency (T-F) unit of each signal sample by our designed spatial multi-head self-attention (SMHSA) mechanism in recursive manner. Simultaneously, the anti-noise protocol interacts with diagnosis model based on estimated probability in anti-noise diagnosis sub-system to further improve the diagnosis performance of ABD system under the interference of background noise. Experiment result in both real-industrial noise condition and simulated noise conditions with extremely worse SNRs indicate that the proposed two-stage ABD system is effective in dealing with gear fault diagnosis task under noise condition.

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

The mechanical fault of gas-insulated switchgear (GIS) seriously threatens the security of the power grid. Recently, acoustic-based fault diagnosis methods, which have the advantage of non-contact measurement, have been applied to the GIS mechanical fault diagnosis, but vulnerable to the interference of the background noise. To improve the capacity of the acoustic-based GIS fault diagnosis under noise background, by simulating the sound feature extraction ability and anti-noise ability of human auditory system, a novel GIS mechanical fault diagnosis method based on saliency feature of auditory brainstem response (SFABR) is proposed. First, an auditory saliency model, which considers both the auditory periphery and the auditory nerve center was constructed by combining the deep auditory model and the saliency model. After processing GIS emitted acoustic signal, the auditory brainstem response (ABR) was obtained, and the saliency features of the ABR were extracted to obtain the SFABR. Then, the characteristic frequency distribution of the auditory saliency model was adjusted to make it more suitable for the spectral characteristics of the GIS sound signal. Finally, the SFABR was mapped to a two-dimensional CNN to train a model for GIS mechanical fault diagnosis. This method simulates the process of auditory response extraction and the selection effect of auditory attention on sound elements. The 110 kV three-phase GIS fault simulation experiment shows that for GIS mechanical faults, the diagnosis method based on SFABR can obtain 96.1% fault identification accuracy. In different noise environments, compared with the traditional acoustic-based fault diagnosis methods, this method has stronger anti-noise performance, and can more effectively realize the identification of GIS mechanical failure types. In future research, the method can be further extended to fault diagnosis of more types of power equipment.

Highly sensitive HF detection based on absorption enhanced light-induced thermoelastic spectroscopy with a quartz tuning fork of receive and shallow neural network fitting

Due to its advantages of non-contact measurement and high sensitivity, light-induced thermoelastic spectroscopy (LITES) is one of the most promising methods for corrosive gas detection. In this manuscript, a highly sensitive hydrogen fluoride (HF) sensor based on LITES technique is reported for the first time. With simple structure and strong robustness, a shallow neural network (SNN) fitting algorithm is introduced into the field of spectroscopy data processing to achieve denoising. This algorithm provides an end-to-end approach that takes in the raw input data without any pre-processing and extracts features automatically. A continuous wave (CW) distributed feedback diode (DFB) laser with an emission wavelength of 1.27 µm was used as the excitation source. A Herriott multi-pass cell (MPC) with an optical length of 10.1 m was selected to enhance the laser absorption. A quartz tuning fork (QTF) with resonance frequency of 32,767.52 Hz was adopted as the thermoelastic detector. An Allan variance analysis was performed to demonstrate the system stability. When the integration time was 110 s, the minimum detection limit (MDL) was found to be 71 ppb. After the SNN fitting algorithm was used, the signal-to-noise ratio (SNR) of the HF-LITES sensor was improved by a factor of 2.0, which verified the effectiveness of this fitting algorithm for spectroscopy data processing.

Multi-probe linear fitting and time of arrival linear correction method to analyze blade vibration based on blade tip timing without once-per-revolution

Blade vibration monitoring can ensure the safe operation of aero-engine rotor blades. Among the methods of blade vibration monitoring, Blade Tip Timing (BTT) method has attracted more and more attention because of its advantages of non-contact measurement. However, it is difficult to install the Once-Per-Revolution (OPR) probe in the confined space of aero-engine, and the failure and instability of the OPR signal will reduce the reliability of the blade vibration analysis results, which directly affects the accuracy of the blade vibration parameters identification. The Multi-Probe linear fitting and Time of Arrival (ToA) Linear Correction method based on the BTT (MP-LC-BTT) without OPR is proposed to reduce the errors of single probe linear fitting method for blade vibration displacement analysis. The proposed method can also correct the calculation error of blade vibration displacement due to the nonlinear change of rotation speed, which can improve the analysis accuracy of the blade vibration displacement. A new blade vibration model conforming to the actual vibration characteristics is established, and the effectiveness of the proposed method is verified by numerical simulation. Finally, the reliability and accuracy of the MP-LC-BTT method have been verified by the experiments which include two high-speed blade test-benches and an industrial axial fan. This method can be used in the actual aero-engine monitoring instead of the BTT method with OPR.

Ultra-high temperature video extensometer: System development and experimental validation.

The video extensometer has been widely advocated for tensile/compressive strain measurement in high-temperature material testing due to its advantages of non-contact measurement, wider measuring range, and larger applicable temperature over traditional clip-on mechanical extensometers. However, existing video extensometers, despite the adoption of active imaging devices, cannot adapt to the rapidly changing thermal radiation from the heated sample and surrounding heating elements during the high-temperature tests. This is because due to the significantly intensified thermal radiation, the decorrelated images degrade the measuring accuracy or even destroy the analysis. To address this problem, we developed an ultra-high temperature video extensometer that can automatically adjust the camera exposure time for reliable thermal strain measurement. Based on the pre-established image quality evaluation criteria, the camera will choose an appropriate exposure time according to the detected thermal radiation within the region of interest, thus ensuring high-quality images for real-time strain measurement. Static tests of tungsten-molybdenum alloy samples at different temperatures were performed to evaluate the noise level of the established ultra-high temperature video extensometer. The effectiveness and accuracy of the developed ultra-high temperature video extensometer were validated through the tensile tests of tungsten-copper, tungsten-potassium, and tungsten-molybdenum alloy samples at high temperatures up to 2000 °C.

Improved Data-Driven Stochastic Subspace Identification with Autocorrelation Matrix Modal Order Estimation for Bridge Modal Parameter Extraction Using GB-SAR Data

With the advantage of non-contact measurement, ground-based synthetic aperture radar (GB-SAR) has been widely used to obtain the dynamic deflection of various bridges. Data-driven stochastic subspace recognition (Data-SSI), a popularized time-domain technique, is commonly used for modal parameter identification of bridges. To improve the computational efficiency and accuracy of the Data-SSI method for bridge modal parameter estimation using GB-SAR, this paper proposes an improved Data-SSI method. First, boxplot data filtering is applied to screen out the error points to generate a Hankel matrix. Second, the Hankel matrix compression method is presented to reduce the ill-conditioned vectors in the column vectors of the Hankel matrix to improve calculation efficiency. Finally, the exact modal order (EMO) modal estimation algorithm based on the autocorrelation matrix is adopted to reduce the generation of false modes and improve the calculation efficiency. The results of simulation and field experiments show that the natural frequency values for the improved Data-SSI method are 2.3208 and 2.3189 and the damping ratio coefficient values are 8.10 and 8.08, under windows 1 and 2, respectively. The operation times using the improved Data-SSI method are 2.02 s and 7.61 s under windows 1 and 2, respectively. This proves that the proposed improved Data-SSI method has higher accuracy and computational efficiency.

Application of Territorial Laser Scanning in 3D Modeling of Traditional Village: A Case Study of Fenghuang Village in China

Historical villages bear historical, cultural, architectural, aesthetic, and landscape values, but they are facing a series of dangers and problems during the process of urbanization. Digital survey for traditional villages plays a crucial role in the preservation, planning, and development of this kind of heritage. The introduction of the terrestrial laser scanning technique is essential for heritage surveying, mapping, and modeling due to its advantages of noncontact measurement, accurate sensing of complex objects, and efficient operation. In recent years, TLS and related processing software (“SCENE”) have been widely presented as effective techniques for dealing with the management and protection of historical buildings in Fenghuang village. Thus, this paper highlights the process of using laser scanning to obtain architectural data, process point clouds, and compare the characteristics of historical buildings in Fenghuang village. The cloud-to-cloud registration technique is applied to build point clouds. As a result of model construction, some architectural patterns are summarized in this village, such as the spatial sequence of ancestral halls, the dominant position of memorial halls, and the character of building decorations and roof slopes. Furthermore, a BIM model is also explained to fulfill the statistical function for architectural components. In the future, more research can be fulfilled based on the built point cloud model, which will be beneficial for the development of the whole village.

A framework combining acoustic features extraction method and random forest algorithm for gas pipeline leak detection and classification

Monitoring the operation status of the gas pipeline is of great significance to ensure the safe and stable operation of the pipeline. A new framework combining the acoustic features extraction method and Random Forest (RF) algorithm is proposed for gas pipeline leak detection and classification under the strong background noise. Acoustic signal has the advantages of non-contact measurement and insensitive to structural resonances, but it is easy to collect signals with low Signal-to-Noise Ratio (SNR) under strong background noise. To improve SNR and extract useful acoustic features, the acoustic features extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Correlation Coefficient (CC) analysis is proposed. CC analysis is used to select appropriate Intrinsic Mode Functions (IMFs), which are decomposed by EEMD algorithm. For each selected IMF, Mel-Frequency Cepstral Coefficients (MFCC), time-domain features, waveform features are extracted to form a feature matrix. Because RF has the characteristic of a small training set requirement, which provides a solution to the difficulty of obtaining enough leak acoustic signals in the industrial environment, RF algorithm is employed for leak detection and classification. Experiments were conducted to verify the effectiveness of the proposed framework. When the sampling distance is set to 1 m and 6 m, the average accuracies of RF with extracted acoustic features are higher than the results with all features. The proposed framework is general and can be applied to various acoustic-based industrial equipment condition monitoring challenges.

Research on Speed Measurement Signal Denoising Processing of Laser Doppler Vibrometer

Speed is an important parameter to reflect the quality of products and equipment, and it plays an important role to monitor and diagnose its operating status by measuring speed. The laser Doppler vibrometer measures the speed with the advantages of non-contact measurement, high sensitivity, small non-linear error and fast dynamic response, and can measure motor speed more accurately. However, the speed data during the measurement process contains a lot of noise, so the measurement data must be denoised. This paper introduces the principle of measuring motor speed with a surface laser Doppler vibrometer, and builts the speed measurement experimental system. The improved wavelet basis denoising threshold algorithm is designed, and the speed measurement data are processed by the improved wavelet basis denoising threshold algorithm and the Kalman filtering algorithm, and the measurement data noise is filtered out, the measurement accuracy is improved. The results show that the average square root value after Kalman filter is closer to the motor rated speed value, the variance is small, the relative error is small, and the denoising effect is better.

SCBSS Signal De-Noising Method of Integrating EEMD and ESMD for Dynamic Deflection of Bridges Using GBSAR

Ground-based synthetic aperture radar (GBSAR) technology, as a new measurement technology, has the advantages of noncontact measurements, high precision, and all-weather measurement capability, and it has been widely used for bridge dynamic deflection measurements. In order to reduce the influence of noise in dynamic deflection of bridges obtained using GBSAR, this article proposes a single-channel blind source separation signal (SCBSS) de-noising method to obtain the denoised dynamic deflection of bridges. First, the extreme-point symmetric mode decomposition (ESMD) method and the ensemble empirical mode decomposition (EEMD) method are used to decompose the obtained dynamic deflection—as the original observation signal—into a series of intrinsic mode functions (IMFs) and a residual R. Second, the Spearman's Rho of each IMF with the original observation signal is calculated to remove the dominant IMFs of high-frequency noise. Third, the remaining IMFs and R decomposed by ESMD and EEMD are reconstructed into two sets of new signals, which form a new virtual multichannel data with the original observation signal. Finally, blind source separation is performed on the new virtual multichannel signal to obtain separated signal components. The separate signal components are converted in the frequency domain using the fast Fourier transform algorithm, and the noise signal components are identified using a spectrum analysis, to achieve further removal of noise information. The results of both simulated and on-site experiments show that the SCBSS signal de-noising method has a powerful signal de-noising ability.

A Morphology Filter-Assisted Extreme-Point Symmetric Mode Decomposition (MF-ESMD) Denoising Method for Bridge Dynamic Deflection Based on Ground-Based Microwave Interferometry

Bridge dynamic deflection is an important indicator of structure safety detection. Ground-based microwave interferometry is widely used in bridge dynamic deflection monitoring because it has the advantages of noncontact measurement and high precision. However, due to the influences of various factors, there are many noises in the obtained dynamic deflection of bridges obtained by ground-based microwave interferometry. To reduce the impacts of noise for bridge dynamic deflection obtained with ground-based microwave interferometry, this paper proposes a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) for the signal denoising of bridge dynamic deflection obtained by ground-based microwave interferometry. First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. Finally, the residual noises in the reconstructed signal were further eliminated using the morphological filter method. The results of both the simulated and on-site experiments showed that the proposed MF-ESMD method had a powerful signal denoising ability.

A Non-Contact Fault Diagnosis Method for Rolling Bearings Based on Acoustic Imaging and Convolutional Neural Networks

Rolling bearing fault diagnosis is conventionally performed by vibration-based diagnosis (VBD). However, VBD is restrained in some cases because vibration measurement usually requires the contact with the machine. Acoustical-based fault diagnosis (ABD) has the advantage of non-contact measurement over VBD. However, ABD has received little attention and rarely applied in bearing fault diagnosis. In this paper, a new non-contact ABD method for rolling bearings using acoustic imaging and convolutional neural networks (CNN) is proposed. Firstly, a microphone array is used to acquire the acoustic field radiated by rolling bearings. Then, acoustic imaging is performed with the wave superposition method (WSM). The reconstructed acoustic images can depict the spatial distribution of the acoustic field, which add a new spatial dimension in the acoustic data representation for fault diagnosis and makes it possible to localize the sound sources. Finally, CNN is applied to accomplish bearing fault diagnosis, which can overcome the problems of handcrafted feature extraction in traditional ABD methods. Experimental results verify the effectiveness of the proposed ABD method. Comparisons with peer state-of-the-art ABD methods further validate that the proposed method can mitigate the drawbacks of the existing ABD methods, and obtain more accurate and reliable diagnosis results.

An Improved Second-Order Blind Identification (SOBI) Signal De-Noising Method for Dynamic Deflection Measurements of Bridges Using Ground-Based Synthetic Aperture Radar (GBSAR)

Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR—especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles—an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.

Continuous monitoring of temperature of electron beam heated metal evaporation surface using controlled gas purge at viewing port in a vacuum chamber

In Electron beam gun (EB-gun) assisted metal evaporator, monitoring of surface temperature is essential for characterisation. Pyrometers having the advantage of non-contact measurement are used for monitoring the temperature of EB heated surface. The opacity introduced by the deposition of metal vapour on the viewing port during evaporation process hinders the monitoring. Indirect viewing using methods like periscope viewing has been reported to have poor accuracy of measurement due to uncertain value of reflectivity of in-situ coated metal mirrors. Multiple collisions of approaching atomic vapour with purge gas molecules and scattering the atoms is an attractive technique for long duration monitoring of surface temperature. In this method a controlled leak of inert gas is introduced to locally increase the pressure, without significantly increasing the pressure of process chamber. This approach increases the collision probability of the approaching atoms with gas molecules due to higher molecular number density, and scatters the atoms away from optics window.In the current study this technique has been successfully tested for monitoring of temperature of electron beam heated surface, using direct viewing optics port, with high evaporation rate. Successful monitoring of the surface temperature had been demonstrated for more than an hour.

Automatic Sorting Machine Based on Vision InspectionAutomatic Sorting Machine Based on Vision Inspection

This work describes detailed an automatic vision system for quality control of small conveyed mechatronic components. The vision measurement has the advantages of non-contact measurement, high precision and high automation degree, thus it has become one of the important trends of development in modern measurement technique. The system highlighted in this paper sorts automatically the components inspected depending on the fabrication fault type detected by means of an electro mechanic system controlled through a serial port. The proposed system has been tested for a series of 608ZZ bearings which have been evaluated through a completely automated process. The proposed system can improve the control efficiency and can be integrated in to an existing fabrication line.

Analysis of an Aero-Engine Gas Path Fault Monitoring Sensor Model

A new aero-engine gas path fault monitoring technology is presented in this paper, which is based on the capacitive sensor. The working conditions of the aero-engine gas path components can be monitored through the sensor, which is the main source of the aero-engine faults such as the gas path debris. The capacitive sensor has advantages of non-contact measurement, high sensitivity and simple structure, which can effectively solve the problems of the traditional aero-engine gas path fault diagnosis technology, such as the poor operability, the weak real-time performance and the high cost. Through the analysis of the capacitive sensor model, the feasibility of the capacitive sensor in the aero-engine gas path fault monitoring is verified.

Optical Stylus using Periodically Vibrating Objective Lens.

Roughness sensors which operate by means of optical stylus have advantages of noncontact measurement, relatively long working distance, high re solution and high accuracy. However, they also have some weak points. The range of measurement is insufficient and the allowable amount of local inclination of the surface is not large enough. It is established that the error due to the inclination can be compensated better by the null method than by the usual method. In the present paper, a new method to realize the null method by oscillating the objective lens and detecting the zero-crossing point of the periodically changing focal error signal is presented. The trial sensor has accuracy of about 20 nm and a measuring range of 20μm. By measurillg the time interval of zero-crossing points of focal error signal as an alternative to its voltage, the possible dynamic range of the sensor is expected to be enlarged by more than 80 dB.

Laser Tools for Diesel Engine Diagnosis

Two portable laser instruments are described which allow the engineer to simply ‘stand and point’ laser beams at a target surface in order to obtain a measurement of time-resolved vibration velocity. The first supplements the accelerometer and measures normal surface velocity but offers the advantage of non-contact measurement. Its use is demonstrated in detecting reduced nozzle pressures in fuel injectors. The second instrument is a laser torsional vibrometer which measures torsional oscillations of rotating machinery parts and in particular crankshaft oscillation. Comparisons are made with the more standard technique which utilizes a slotted disc. The new instrument provides advantages of non-contact measurement, insensitivity to shaft radial motion and the ability to measure oscillations of shafts of arbitrary cross-section.