Traditional Vibration-based Methods Research Articles

Calibration of triaxial accelerometers by constant rotation rate in the gravitational field

We extend the use of the intrinsic properties calibration method for triaxial accelerometers that we reported previously from discrete angular steps to using a constant rotation rate to produce a time varying sinusoidal excitation in the earth’s gravitational field. We show that this extension yields the low frequency calibration response of the device under test. Whereas traditional vibration-based methods using shakers generally exhibit an increased measurement uncertainty with decreased excitation frequency, we show that this approach does not. We report results obtained from a commercial triaxial digital accelerometer from DC up to a 0.5 Hz rotation rate. The maximum rotation rate that we report is limited by our rotation stage; but we expect that the method can be extended to higher rotation rates with an upper limit constrained by what can be tolerated as a maximum centripetal acceleration.

A Novel Slip Sensory System for Interfacial Condition Monitoring of Steel-Concrete Composite Bridges

Steel-concrete composite (SCC) beams are widely employed in bridge decks. The interfacial shear transfer between the top concrete slab and the supporting steel beams significantly affects the overall load carrying capacity and performance of a bridge deck. The inaccessibility of the connection system makes the visual inspection difficult, and the traditional vibration-based methods are insensitive to this type of local damage. In this study, a novel interlayer slip monitoring system has been developed for interfacial condition assessment of SCC beams. The monitoring system is mainly based on the Ultra-flat Industrial Potentiometer Membrane (UIPM). The sensor film that is glued on a steel base is mounted on the concrete slab, and the wiper is installed on the steel beam. The interlayer slip between the concrete slab and steel beam is monitored by the relative displacement between the sensor film and the wiper. An experimental study has been carried out on a 6-m long composite bridge model in the laboratory. In the model, the concrete slab and the steel beams are bolt-connected, and the bolts could be loosened to simulate the defects in the shear connection system. Seven slip sensors are evenly installed along the bridge model. The sensors are calibrated using the testing machine before they are installed on the bridge model. Three damage scenarios are simulated by loosening bolts at different locations. Different loadings are also applied on the bridge to simulate the operational conditions. Undamaged and damaged scenarios have been considered within load increments, and data are collected and interpreted to find out how the slip changes. The results show that this system is reliable and efficient to monitor the interlayer slip for assessing the interface condition of composite structures.

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

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.

An Unsupervised Multiview Sparse Filtering Approach for Current-Based Wind Turbine Gearbox Fault Diagnosis

Gearboxes are critical components in wind turbines, and their fault diagnosis has gained increasing and considerable attention. Compared to traditional vibration-based methods, current-based fault diagnosis has significant advantages in terms of cost, implementation, and reliability. However, it is quite challenging to extract informative fault-related features from raw current signals due to the presence of dominant current fundamental component and harmonic component as well as electrical noise. In order to address this challenge, this article presents a novel unsupervised feature learning approach based on a two-layer sparse filtering algorithm for current-based gearbox fault diagnosis. Specifically, a multiview sparse filtering (MVSF) method is proposed to automatically extract useful and complementary features under different views from raw current signals. The proposed method can fuse multiview feature representations learned concurrently to improve the fault diagnosis performance. The effectiveness of the proposed MVSF method is verified through experiments on a wind turbine gearbox test rig. Experimental results demonstrate that the proposed approach can effectively recognize the health state of the gearbox and exhibits superior performance in feature learning and diagnosis compared with traditional feature extraction approaches.

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.

Electrical Signature Analysis-Based Detection of External Bearing Faults in Electromechanical Drivetrains

An electrical signature analysis (ESA)-based method to detect bearing defects in electromechanical drivetrains (e.g., wind turbines) is proposed in this paper. The novelty of the proposed technique is justified by its capability to detect a defect that occurs in a drivetrain bearing, external to the machine. Unlike the traditional vibration-based method, the electrical measurements of the terminal machine are utilized in this case to perform the fault detection. The proposed ESA-based method is developed utilizing a new electrical signal model of bearing defect, proposed in this paper, based on the excitation of torsional resonances. The proposed ESA-based method and the signal model are validated through experimental tests. The proposed technique demonstrates excellent fault detection capability even at low operating speed, which is advantageous over the traditional vibration-based method in terms of sensor cost, installation complexity, and detection reliability.

Current-Based Gear Fault Detection for Wind Turbine Gearboxes

Gearbox faults contribute to a significant portion of the faults and downtime of wind turbines. Gearbox fault detection using the electrical signals acquired from generator terminals has advantages over traditional vibration-based methods in terms of cost, hardware complexity, implementation, and reliability. This paper analyzes the principle of using the nonstationary stator current signals of a generator for the fault detection of a multistage gearbox connected to the generator in varying-speed conditions. Based on the analysis, the characteristic frequencies of various gearbox faults in the frequency spectra of the generator stator currents are identified. A method is then proposed for the fault detection of the gearbox using the current signals. The method consists of an adaptive signal resampling algorithm to convert the nonstationary characteristic frequencies of gearbox faults in the current signals to constant values when the gearbox operates in varying-speed conditions, a statistical analysis algorithm to extract the fault features from the frequency spectra of the resampled stator current signals, and two fault detectors based on the extracted fault features. Experimental results on a two-stage gearbox connected to an electric generator are given to show the effectiveness of the proposed analysis and method for detection of a variety of gear faults in the gearbox.

A novel superposed waveform method for damage detection of composite laminates

A novel vibration testing method using a superposed waveform method (SWM) is proposed as a fast, easy and universal vibration-based technique for damage detection in structures. This method can be carried out conveniently over arbitrary frequency bands, especially in high-frequency ranges, which are more sensitive to small damages or defects, but are less convenient to use in general testing methods compared to low-frequency ranges. Unlike most published traditional vibration-based methods used to directly obtain mode shapes or operational deflection shapes (ODSs), the SWM presented in this paper calculates vibration parameters using propagating waves in structures. A comparison of natural frequencies and mode shapes of a cantilever obtained by the traditional vibration method and SWM validates the reliability of the SWM. We used the SWM to locate impact damage in composite laminates as proof-of-concept application. A set of piezoceramic transducer patches acted as actuators on composite laminates damaged by low velocity impact; propagating wave data were acquired by a scanning laser Doppler vibrometer. A modified signal actuated the propagating wave, which was used to calculate the frequency response functions and the ODSs at each frequency using the SWM. The mode shapes of high-frequency resonance clearly showed the damaged area, and the local resonance of damage was tested using the ODS of laminate at a certain frequency to identify the damage location. The SWM proposed in this paper enables efficient and effective vibration measurements, especially at high frequencies, and can be used to detect damage in composite laminates.

Imbalance Fault Detection of Direct-Drive Wind Turbines Using Generator Current Signals

Imbalance faults constitute a significant portion of all faults in wind turbine generators (WTGs). WTG imbalance fault detection using generator current measurements has advantages over traditional vibration-based methods in terms of cost, implementation, and system reliability. However, there are challenges in using current signals for imbalance fault detection due to low signal-to-noise ratio of the useful information in current signals and nonstationary characteristic frequencies of imbalance faults. This paper proposes a method of using generator stator currents for imbalance fault detection of direct-drive WTGs. In the proposed method, the variable shaft rotating frequency of a WTG is estimated from one phase stator current measured from the generator terminal by using a phase-locked loop method. The estimated shaft rotating frequency is then processed by using appropriate upsampling and variable-rate downsampling algorithms. Consequently, the variable characteristic frequencies of imbalance faults in the spectrum of the estimated shaft rotating frequency are converted to constant values. Therefore, the signatures of wind turbine imbalance faults can be clearly identified from power spectral density analysis of the converted shaft rotating frequency signal. Simulation and experimental results show that the proposed method is effective to detect various imbalance faults in direct-drive WTGs.