Vibration Monitoring Techniques Research Articles

Robust Structural Health Monitoring of CFRP Structures under Varying Loads using NSFD and Machine Learning with Combined Usage of Acceleration and Strain Data

Structural health monitoring (SHM) of objects in situ often requires a suitable combination of different methods and measurement techniques. In the field of aerospace applications, the previous research project "Combined acoustic and modal structure monitoring" dealt with the development of a robust SHM system for damage detection in carbon-fibre-reinforced polymer (CFRP) structures under varying realistic loads. The methods combined within the project were based on guided waves, acoustic emission, different vibration monitoring techniques and strain measurement. The present paper deals with the application of the nullspace-based fault detection algorithm (NSFD) combined with a machine learning approach based on strain measurements to classify different load conditions of the structure. Due to the high sensitivity to changes in the statistical properties of the measurement data, the NSFD algorithm reacts very sensitively to structural changes, but also to changes in the environmental conditions, such as changes in the external loads and the resulting stress state of the structure. In the paper, the impact of varying external loads on the NSFD damage indicator is analysed and shown. To prevent the NSFD algorithm from causing false alarms due to changes in the load conditions, but still being sensitive to small structural changes due to impact damages, the strain sensor data is used to predict the external loads on the structure. For this, machine learning is used to create clusters of similar stress states. For every cluster, a baseline for the NSFD algorithm must be made. The resulting approach is robust against changes in external loads and sensitive to small changes in the structure due to damages at the same time. The theory and the algorithms are successfully tested with measured data sets from a CFRP-airplane structure.

An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks

This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques.

Monitoring of ball bearings via vibration analysis and envelope technique for predictive maintenance purposes

Bearings are one of the primary and most crucial machine components in the industry and transportation systems, since they are critical components in these systems, their failure can lead to various losses and significant damages. Despite the advanced stages of development and manufacturing processes, approximately 90% of unplanned machine shutdowns are attributed to bearing failures. These failures can occur in various ways, making it essential to monitor the bearing's deterioration to replace it before it reaches a critical condition for the industry, machine or vehicle's operation. To monitor ball bearing failures, a range of vibration monitoring techniques are employed, encompassing envelope analysis, kurtosis, bi-spectrum, and wavelet analysis. In this case, frequency graphs based in the signals of the bearings inner race will be generated, through a new signal acquisition system, using of envelope analysis by frequency filters, Hilbert Transform and Fast Fourier Transform (FFT). However, detecting and analyzing these signals can be challenging due to weak signals and noise masking vibration patterns. Although current techniques are effective, they have limitations, such as requiring expert analysis, difficulty in detecting early-stage faults, and inability to differentiate between different fault types. Current techniques, such as envelope and wavelet analysis, are effective but have limitations. New technologies and methods, are being explored to improve fault detection and classification, providing early detection of faults and differentiation between different fault types, ultimately reducing the impact of ball bearing failures on machines and industries. This paper proposed to present a study of the ball bearing failure through vibration analysis from early-stage to advanced-stage of damage for predictive maintenance purposes, applying the envelope and FFT together with programming, to enable the identification of defects in the bearing, especially in the inner race, through a signal acquisition system that can explain the presence of the defect through frequency graphs. Thus, obtaining results that show the presence of defects in three different bearings, with gradual defect magnitudes, differentiating these data from an ideal bearing. The next step we will intend to explore the new technologies like machine learning and artificial intelligence, to also analyze all variants of defects in a bearing.

Experimental Detection of Lubrication Characteristics in a Solid–Liquid Two-Phase Flow Supported Bearing-Rotor System Using Vibration Monitoring Techniques

This work aims to investigate the friction and vibration behaviors of a liquid–solid two-phase flow supported bearing-rotor system for the early monitoring and assessing of oil contamination resulting from externally ingested particles. Based on a Jeffcott rotor test rig, the lubricating oil in journal bearing was mixed with micron-scale silica particles of different sizes and concentrations. The vibration monitoring techniques utilizing eddy current, photoelectric, and piezoelectric sensors were conducted to detect the rotor displacement amplitude and bearing acceleration online. According to the quantitative evaluation, the vibration frequency spectrum, shaft trajectory, and the bearing surface characteristics were analyzed and classified at different conditions of particle parameters. Furthermore, the lubricating mechanism at bearing interface which possesses a strong interactive coupling effect with rotor vibration was deduced and illustrated. The results showed that although the sizes of ingested micron particles were less than the minimum oil-film thickness, they usually exhibited detrimental effects on the flow stability and fluid support. As the particle size or concentration increased, the interaction between particles and bearing surfaces led to the burrs in time-domain waveform, reverse displacements in rotor orbit, higher frequency harmonics in vibration spectrum, and the scratches on the bearing surface. The occurrence of continuous three-body friction could induce misalignment, blockage, oil starvation, and rub-impact fault. Furthermore, the detectability of particle contamination and damage microstructure of bearing surface was described for reference.

Vision-based virtual vibration sensor using error calibration convolutional neural network with signal augmentation

In this study, we propose a methodology for using image-based virtual vibration sensors to replace acceleration sensors in the vibration monitoring of targets. Most image-based vibration-monitoring techniques typically involve the installation of a non-contact-capture camera at a location unaffected by disturbances to photograph the outside of a target. However, these methods have several drawbacks, including the additional cost of installing sensors and the need to select appropriate installation locations. The proposed virtual vibration sensor addresses these limitations by combining signal extraction based on a contact-capture camera installed inside the target with a convolutional neural network for error calibration. By employing a camera using the contact-capture method, the installed cameras can be reused to integrate the functions of other sensors and reduce the total number of sensors required. Moreover, in the proposed method, a calibration convolutional neural network (CCNN) improves the limited accuracy of contact-capture cameras. Furthermore, the calibration performance of the CCNN was improved via signal augmentation even with the availability of limited data. The experimental results obtained using a quadcopter platform demonstrated the improved accuracy and effectiveness of the proposed virtual sensor compared to that of the actual sensors.

A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery

Machine failure in modern industry leads to lost production and reduced competitiveness. Maintenance costs represent between 15% and 60% of the manufacturing cost of the final product, and in heavy industry, these costs can be as high as 50% of the total production cost. Predictive maintenance is an efficient technique to avoid unexpected maintenance stops during production in industry. Vibration measurement is the main non-invasive method for locating and predicting faults in rotating machine components. This paper reviews the techniques and tools used to collect and analyze vibration data, as well as the methods used to interpret and diagnose faults in rotating machinery. The main steps of this technique are discussed, including data acquisition, data transmission, signal processing, and fault detection. Predictive maintenance through vibration analysis is a key strategy for cost reduction and a mandatory application in modern industry.

Acoustic-Based Machine Condition Monitoring—Methods and Challenges

The traditional means of monitoring the health of industrial systems involves the use of vibration and performance monitoring techniques amongst others. In these approaches, contact-type sensors, such as accelerometer, proximity probe, pressure transducer and temperature transducer, are installed on the machine to monitor its operational health parameters. However, these methods fall short when additional sensors cannot be installed on the machine due to cost, space constraint or sensor reliability concerns. On the other hand, the use of acoustic-based monitoring technique provides an improved alternative, as acoustic sensors (e.g., microphones) can be implemented quickly and cheaply in various scenarios and do not require physical contact with the machine. The collected acoustic signals contain relevant operating health information about the machine; yet they can be sensitive to background noise and changes in machine operating condition. These challenges are being addressed from the industrial applicability perspective for acoustic-based machine condition monitoring. This paper presents the development in methodology for acoustic-based fault diagnostic techniques and highlights the challenges encountered when analyzing sound for machine condition monitoring.

Non-invasive Detection of Rotor Inter-turn Short Circuit of a Hydrogenerator Using AI-Based Variational Autoencoder

This paper presents a non-intrusive technique for detecting the Rotor Inter-Turn Short Circuit (RITSC) of a hydrogenerator using an Artificial Intelligence (AI) based Vari- ational AutoEncoder (VAE). The technique is applied to a large hydrogenerator of 74 MVA and 76 poles, to test its health monitoring and classification potential. The model is trained and validated based on the acquisition of real vibratory data collected in situ from a healthy machine. The frequency pattern of the fault in the vibration signal is obtained based on Finite Element Methods (FEM). Then, to test the sensitivity of the model in early fault detection, the signature is injected into another set of real healthy vibration signals, and the results are compared to those obtained using the traditional vibration monitoring technique. Furthermore, clustering in the latent space of the model is explored. The obtained results prove the ability of this technique and its potential in detecting anomalies at earlier stages as well as its capacity to cluster different degrees of severity of the fault in a 3D user-friendly space.

Using e-bikes and private cars in dynamic road pavement monitoring

The high cost and time involved with traditional road pavement monitoring approaches as well as the high costs of pavement monitoring equipment necessitate the need for introducing a cheap, accurate and straightforward road pavement monitoring method. Pavement monitoring systems are considered as an important preventive step to maintain the quality of road pavements. Road pavement conditions should be accurately monitored to measure the severity of road pavement degradations. This paper presents a new technique for road pavement monitoring. The proposed road pavement monitoring technique applies road pavement vibration measurements from an e-bike and a private sedan car equipped with vibration data and video recordings. In addition, a pavement performance indicator named Present Serviceability Rating (PSR) is used to detect the level of pavement degradation and overall pavement condition depending on visual inspections. The results from the proposed vibration monitoring technique and the PSR are compared to identify the accuracy of the proposed technique to measure the road pavement condition. The results of the case study road show that the e-bike and the sedan car are appropriate and accurate as test vehicles in pavement monitoring. They also show that travel speed and the number of monitoring iterations have a significant impact on the accuracy of pavement vibration data and consequently, on the accuracy of the road pavement monitoring of both test vehicles.

Investigations of Antifriction bearing defects using Vibration Signatures

Antifriction bearing failure is a major factor in the failure of rotating machinery. As a fatal defect is detected, it is common to shut down the machinery as soon as possible to avoid catastrophic damages. Performing such action typically results in substantial time and economic losses. Therefore it is important to monitor the conditions of antifriction bearings and to know the details of the severity of defects before they cause serious catastrophic consequences. The vibration monitoring technique is the most suitable method to analyze various defects in bearing. This technique can provide early information about progressing malfunctions. Time-domain analysis and frequency domain analysis have been employed to identify different defects in bearing running at three different speeds. Defect-free and defective bearing are used for testing. Time-domain and frequency-domain signals acquired for both good and defective bearings Time waveform indicated the severity of vibration in defective bearings. The frequency spectrum is used to identify the amplitudes corresponding to fault frequencies and enables to predict of the defect presence. The roller bearing is considered for analysis. Three types of defects namely outer race, inner race, and rolling element defect are considered. Experimental data of both defect-free and defective bearings are processed to show the effectiveness of the fault diagnosis using this method. In fault monitoring and diagnostics of bearings, real-time processing and fast on line fault indication are becoming increasingly important. Towards this goal, attention has focused on a search for the use of effective signal processing and signatures extracting methods to realize immediate fault detection.

Glass powder admixture effect on the dynamic properties of concrete, multi-excitation method

In this work, the dynamic properties of a high performance concrete containing glass powder (GP) was studied. The GP is a new cementitious material obtained by recycling waste glass presenting pozzolanic activity. This eco-friendly material was incorporated in concrete mixes by replacing 20 and 30% of cement. The mechanical properties of building materials highly affect the response of the structure under dynamic actions. First, the resonant vibration frequencies were measured on concrete plate with free boundary conditions after 14, 28 and 90 curing days by using an alternative vibration monitoring technique. This technique measures the average frequencies of several excitations done at different points of the plate. This approach takes into account the heterogeneity of a material like concrete. So, the results should be more precise and reliable. For measuring the bending and torsion resonant frequencies, as well as the damping ratio. The dynamic properties of material such as dynamic elastic modulus and dynamic shear modulus were determined by modelling the plate on the finite element software ANSYS. Also, the instantaneous aroused frequency method and ultrasound method were used to determine the dynamic elastic modulus for comparison purpose, with the results obtained from vibration monitoring technique.

Application of DIC method to modal vibration study for structure health monitoring of WT tower

ABSTRACT In recent years, wind power generation has been widely promoting by government policy. If the structural health of wind turbines can be screened effectively and quickly, the cost for operation and maintenance will be reduced. Such techniques are desirable since a vast number of wind turbines are scheduled to be operational in the next decade in Taiwan. This certainly sets the stage for research on developing the vibration analysis and monitoring techniques for the supporting structure of wind turbine (WT) systems. The authors demonstrated in a previous study that the defect location can be discovered using the frequency displacement mode output of the numerical model. In the current study, a PVC pipe with a mass attached at the top has been applied to validate the results of the numerical simulation. The displacement in three axes of each target of equal spacing on the PVC pipe is determined by using a technique based on digital image correlation. The resulted first modal vibration pattern in the frequency domain can be linked to the dynamic behaviour of this sized-reduced model of the wind turbine tower. The experimental data show that the proposed method has a good potential for damage assessment.

Research review of transformer vibration monitoring technique

Transformer vibration monitoring technique has been known to be safe, stable and resistant to interference. Based on the theories of transformer vibration and its origins, this study focuses on the equivalent mathematical model of transformer vibration, signal characteristics, as well as monitoring system for transformer vibration, to summarize research findings over recent years. A series of intelligent technologies have been adopted to reach certain diagnoses and monitoring outcomes; however, their applications are to some extent limited by the experience of users and other variants. The stability and reliability of techniques for monitoring transformer vibration need further improving.

Modal identification of building structures using vision-based measurements from multiple interior surveillance cameras

Surveillance cameras are ubiquitous within modern building systems, providing a wealth of data that, to date, has not been utilized for structural health monitoring and condition assessment. In this paper, a vision-based vibration-monitoring technique is introduced, which leverages existing cameras within building structures to extract modal information and assess for damage. Cameras already installed within buildings, such as interior surveillance cameras, are used to measure structural responses (inter-story drifts and rotations) by tracking the cameras’ motion relative to the floor below. By synchronizing vision-based inter-story measurements captured at multiple stories, a complete three-dimensional representation of the building’s motion is recovered. Output-only system identification (frequency domain decomposition) is applied to these vision-based measurements to extract modal information—frequencies and mode shapes. An experimental parametric study on a lab-scale three-story structure is conducted to assess the efficacy of the vision-based monitoring technique for structural modal identification. The primary set of structure/camera geometries considered is representative of actual surveillance camera installations. Broadband white noise is used to excite the structure uni- and bidirectionally, and the resulting ambient and transient responses are measured. The accuracy of the modal information extracted by means of the proposed vision-based technique is assessed through comparison with modal data from accelerometer-based measurements, quantified by the modal assurance criteria. Moreover, the precision, robustness, and repeatability of the method as well as its shortcomings are established and are described in this paper.

An Efficient Approach for Identification of the Inlet Distortion of Engine Based on Acoustic Emission Technique

Effective and accurate diagnosis of engine health is key to ensuring the safe operation of engines. Inlet distortion is due to the flow or the pressure variations. In the paper, an acoustic emission (AE) online monitoring technique, which has a faster response time compared with the ordinary vibration monitoring technique, is used to study the inlet distortion of an engine. The results show that with the deterioration of the inlet distortion, the characteristic parameters of AE signals clearly evolve in three stages. Stage I: when the inlet distortion J ≤ 30%, the characteristic parameters of the AE signal increase as J increases and the amplitude saturates at J = 23%, faster than the other three parameters (the strength, the root mean square (RMS), and the average signal level (ASL)). Stage II: when the inlet distortion 30% < J ≤ 43.64%, all the parameters saturate with only slight fluctuations as J increases and the engine works in an unstable statue. Stage III: when the inlet distortion J > 43.64%, the engine is prone to surge. Furthermore, an intelligent recognition method of the engine inlet distortion based on a unit parameter entropy and the back propagation (BP) neural network is constructed. The recognition accuracy is as high as 97.5%, and this method provides a new approach for engine health management.

Condition Monitoring and Fault Diagnosis of Induction Motor

An induction motor is at the heart of every rotating machine and hence it is a very vital component. Almost in every industry, around 90% of the machines apply an induction motor as a prime mover. It is a very important driving unit of the machine. Hence, it is necessary to monitor its condition to avoid any catastrophic failure and stoppage of production. The breakdown of the induction motor would not be affordable due to remarkable financial loss, unpredicted shutdown, and the associated repair cost. Vibration is a manifestation of induction motor due to the issues in alignment, balancing, and clearances. Bearing, the most vulnerable to failure due to continuous working under fatigue loading leads to defects. These defects cause changes in the vibration signature over time. The vibration monitoring techniques helps to effectively diagnose mechanical faults such as bearing defect and stator rotor rub. The purpose of this review paper is to summarize the major faults in induction motor, recent diagnostics methods augmented with advanced signal processing techniques, and real-life applications in electric vehicles. It also discusses possible research gaps and opportunities to contribute based on the review findings in the field of condition monitoring. This article presents a detailed review of recent trends in the research of condition monitoring and fault diagnosis of the induction motor. The emphasis is given on the major faults in the induction motor covering time-domain, frequency-domain, and time–frequency domain methods along with an application of artificial intelligence techniques for fault detection. This article presents a comprehensive review of literature which highlights the development and new propositions by researchers in the field of diagnostic techniques for the different faults of induction motor in the last decade. Researchers documented applications of the different conventional methods, advanced signal processing techniques, and soft computing techniques for fault identification of induction motor. This review is carried out for fault identification of induction motor used in machines in general and in particular for identifying the faults in an induction motor of an electric vehicle. A dedicated discussion on the review findings, research gaps, future trends in the field of condition monitoring of induction motor is presented. Condition monitoring of the induction motor in an electric vehicle is also discussed in this paper. It is observed that the vibration-based techniques are reported to be effective for the identification of mechanical faults while motor current signature analysis is effective for electrical fault in an induction motor. The review presented to analyze the suitability of various condition monitoring techniques for the induction motor fault identification in general and particularly its application in an electric vehicle. It is observed that the diagnosis of faults at the incipient level without using the signal processing technique is challenging. Fault diagnosis of induction motor has witnessed the changes from traditional diagnosis techniques to advanced techniques with a hybrid application of signal processing and artificial intelligence techniques. Still, there is a potential of improvement in reliability, efficiency, robustness, computational time, and real-time diagnostics of faults in IM.

Corrosion assessment of ductile iron pipes using high-speed camera technique: Microstructural validation

Corrosion in steel pipelines is a common problem that happens due to environmental conditions over time which, if left unchecked, can lead to catastrophic failures of the system. This study involves a multi-scale assessment of corroded pipe sections with diverse characterization modalities at multiple length scales using techniques such as motion magnification with high-speed video, X-ray diffraction, and nanoindentation to bridge the gap between shifts in vibration behavior, and changes in the steel chemical structure, nano-structure, and material properties. Here, vibration monitoring technique coupled with chemical characterization is used to detect and assess the extent of corrosion damage in pipelines.

Design and Validation of Android Smartphone Based Wireless Structural Vibration Monitoring System.

Vibration monitoring is one of crucial functions of structural health monitoring (SHM) systems. Traditional structural vibration monitoring usually relies on specialized sensors, data transmission and acquisition equipment, which are expensive and may not be easily available in urgently needed situations like post-disaster structural evaluation. Therefore, developing an affordable and efficient structural vibration monitoring technique becomes an important topic in SHM research. In this paper, the authors developed an android system APP that can easily convert multiple android smartphones into a wireless structural vibration monitoring system. To make the designed system reliable and easy to use, the server/client architecture is adopted. One smartphone is designated as the serve of the system to remotely control all other smartphones, which function as sensors to measure structural vibration. An efficient method is proposed herein to establish the smartphone-based structural vibration monitoring network, allowing the server smartphone to quickly and easily connect multiple sensor smartphones to form the wireless network for structural vibration monitoring. Additionally, a synchronization method is also proposed to synchronize different smartphones for simultaneously measuring structural vibration. To verify the time synchronization accuracy of the developed system, an experiment is designed and conducted. Moreover, a new analysis method of the time synchronization accuracy is also proposed, which verifies that the designed smartphone-based monitoring can achieve the millisecond-level time synchronization accuracy. Finally, a shaking table experiment is conducted on a three-story bench-scale structural model, the results of which demonstrate that the designed smartphone-based wireless structural vibration monitoring system can quite accurately identify the modal parameters of the tested structure.

Utilization of Fast Response Pressure Measurements to Non-Intrusively Monitor Blade Vibration in Axial Compressors

Abstract A novel non-intrusive method has been developed to monitor rotor blade vibration using unsteady casing pressure. The present blade vibration monitoring technique utilizes casing unsteady pressure sensors that can detect the pressure waves associated with blade vibration. Spinning mode theory was used to identify the specific frequencies and nodal diameters (NDs) of the spinning pressure waves associated with the blade vibration. A dual temporal-spatial analysis method has been developed to extract the specific frequency components using Fourier transforms, and the specific ND component was extracted using a circumferential mode-fitting algorithm. An experimental study was done in the Purdue 3-stage axial research compressor to verify the new rotor blade vibration monitoring method against the blade tip timing (BTT) method. During the experiment, the compressor was swept through the resonant crossing speed corresponding to the first torsion (1T) vibratory mode of the embedded rotor, while the unsteady casing pressure data and BTT data were simultaneously acquired. Utilizing as few as two sensors, the pressure wave due to blade forced vibration was extracted. A constant scaling factor between the resultant pressure wave strength and blade deflection amplitude was calculated for two different loading conditions. The close match between blade vibration-generated pressure wave strength and blade deflection amplitude through the resonant range provides the validation for the new rotor blade vibration monitoring method. This is the first time in the open literature that blade vibration-related pressure waves have been extracted from casing pressure sensor arrays and used to quantify blade vibration.

Fault Diagnosis of a Double-Row Spherical Roller Bearing for Induction Motor Using Vibration Monitoring Technique

Vibration monitoring techniques employing sophisticated instruments are widely used in industries for regular inspection and diagnosis of the health condition of rotating machinery. This paper discusses fault diagnosis of the spherical roller bearing for a 630-kW induction motor in a local industry through vibration monitoring technique using a fast Fourier transform analyzer. The main attraction of this technique is that it can be performed even while the equipment is in normal working condition, thus saving precious downtime and avoiding production loss. Accelerometer sensors are kept on drive and non-drive ends of the motor. Vibration data on motor housing are collected in the triaxial direction in the form of amplitude, spectrum and time waveform signals in full-load and no-load conditions. These signals are analyzed and faults identified from the vibration characteristics. From the data, it is found that the dominating amplitude peaks have appeared in the axial direction at 3960 cpm which is matching with harmonic frequencies of motor RPM. Theoretical calculations were also performed to find the bearing fault frequencies, and it is observed that the vibration readings are matching with sub-harmonics of bearing ball pass frequency of outer race. These investigations and calculations help the maintenance personnel in locating bearing faults and also in detecting the exact cause that leads to bearing deterioration so that a suitable remedy can be suggested, tested and implemented.