Vibration-based analysis of a sandwich gori composite curved beam in thermo-electro-magnetic environment

Wind energy production relies heavily on the efficiency of wind turbine systems. The routine condition monitoring and maintenance of these systems are necessary to maintain healthy operation, reduce maintenance costs, minimize downtime, and extend the lifespan. Vibration based analysis is an essential technique for wind turbine condition monitoring that enables early detection of mechanical faults, abnormal behavior and degradation mechanisms, and lessens the risk of unexpected failures. This review paper explores an intensive review of various vibration based techniques of condition monitoring, their advancements, challenges, and trends. This review paper reveals that this technique of condition monitoring is effective and essential to ensure the efficiency of wind energy systems. The review paper identifies future research prospects and potential technological advancements to ensure wind energy systems' reliability, safety, and optimal performance.

Among different failure modes observed in structures, loss of stability due to buckling is a major concern. Buckling may be induced because of overload or as a consequence of other types of failures in the structure. This paper examines two techniques, namely, vibration based analysis, and stress wave propagation analysis for detecting this onset of instability. The responses of a bar and a plate are used to illustrate the effectiveness of the two approaches. These analyses were performed through finite element simulations and limited experiments. Changes in vibration frequencies and mode shapes are found to provide good indications of the impending failure as well as its progress. Changes in the wave propagation characteristics showed some limited success in detecting the incipient buckling.

The vibration-based analysis is an effective technique for planetary gearbox fault diagnosis, but a difficult task is how to accurately identify fault features from noisy vibration signals. In this paper, a nonconvex wavelet thresholding total variation (WATV) denoising method is proposed for planetary gearbox fault diagnosis, which combines wavelet-domain sparsity and total variation (TV) regularization. The TV regularization algorithm is employed to modify the retained wavelet coefficients so that the occurrence of oscillations caused by wavelet thresholding is suppressed. To overcome the underestimation shortcoming of L1-norm regularization, nonconvex penalty function regularization is used to strongly promote the sparsity of estimation while guaranteeing that the global optimal solutions are obtained even though the objective function is nonconvex. Then, the split augmented Lagrangian shrinkage (SALSA) method is developed to solve the nonconvex WATV denoising problem. Two experimental studies are performed to verify the performance and effectiveness of the proposed method. Comparisons with the soft thresholding and basis pursuit denoising (BPD) methods show that the proposed method can accurately estimate the fault features from vibration signals, which means that the proposed method is an effective and promising tool for planetary gearbox fault diagnosis.

Identifying and categorising gear faults forms an important aspect in predictive maintenance and industrial safety. Traditional methods of fault detection, such as vibration-based analysis, are restricted in terms of sensor placement, high sensitivity to environmental noise, and sheer incapacity to identify subtle gear anomalies. To overcome these challenges, the present study employs acoustic data for gear fault diagnosis, transforming temporal sound pressure signals into image representations. Hence, this proposed method gives a systematic analysis of various gear conditions, including cracks, misalignment (under load and no-load conditions), broken teeth, and normal operational conditions, under varying RPM and load conditions. The methodology consists of converting acoustic time-series data to two-dimensional arrays and normalizing them to 8-bit grayscale images, after which the data are categorized based on types of fault. Data diversity is enhanced on these augmented images through image data augmentation using resizing, rotation, flipping, color jittering, and normalization. It is then used to train deep learning models, EfficientNetB0 and EfficientNetB3 that are superior on feature-extraction and computational efficiency. The comparative analysis indicates that EfficientNetB3 outperforms EfficientNetB0 based on all four metrics of accuracy, precision, recall, and overall classification performance. Model validation is conducted using k-fold cross Validation to ensure robustness and generalizability. This research proves that acoustic-based fault analysis combined with advanced deep learning models achieves capture of efficiency, compared to conventional vibration-based diagnostics. The proposed method improves early fault detection, provides an accurate classification coupled with a non-intrusive, scalable solution for industrial gear health monitoring and thus takes a step forward toward advancement in predictive maintenance strategies in mechanical systems.

Early Detection of Induction Machines Fault Using Vibration-Based Analysis under COMSOL Multiphysics within a Preventive Maintenance Framework

Bearings are key components in modern power machines. Effective diagnosis of bearing faults is crucial for normal operation. Recently, the deep convolutional neural network (DCNN) with 2D visualization technology has shown great potential in bearing fault diagnosis. Traditional DCNN-based fault diagnosis mostly adopts a single learner with one input and is time-consuming in sample and network construction to obtain a satisfied performance. In this paper, a scheme combining diverse DCNN learners and an AdaBoost tree-based ensemble classifier is proposed to improve the diagnosis performance and reduce the requirement of sample and network construction simultaneously. In this scheme, multiple types of samples can be constructed independently and employed for diagnosis simultaneously; next, the same number of DCNN learners are built for underlying features extraction and the obtained results are integrated and finally fed into the ensemble classifier for fault diagnosis. An illustration based on the Case Western Reserve University datasets is given, which proves the superiority of the proposed scheme in both accuracy and robustness. Herein, we present a universal scheme to improve the diagnosis performance, and give an example for practical application, where the signal preprocessing and image sample construction methods can also be applied in other vibration-based analysis.

Time domain vibration analysis techniques for condition monitoring of rolling element bearing: A review

Novelty detection is the identification of abnormal system behaviour, in which a model of normality is constructed, with deviations from the model identified as “abnormal”. Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Given the complexity of such systems, the number of possible failure modes is large, many of which may not be characterised sufficiently to construct a. traditional multi-class classifier [22]. Thus, novelty detection is particularly suited to such cases, which allows previously-unseen or poorly-understood modes of failure to be correctly identified.This chapter describes recent advances in the application of novelty detection techniques to the analysis of data from gas-turbine engines.Whole-engine vibration-based analysis will be illustrated, using data measured from casemounted sensors, followed by the application of similar techniques to the combustor component. In each case, the investigation described by this chapter shows how advances in prognostic condition monitoring are being made possible in a principled manner using novelty detection techniques.

ABSTRACTOn 11 November 2019, an Mw 4.9 earthquake struck the middle Rhône valley (South-East France) producing moderate to severe damage in the town of Le Teil and its surroundings. This unexpected event stressed the vulnerability of the French cultural built heritage to a moderate seismic hazard. Commonly applied to modern civil engineering structures, passive seismic methods are still lacking on historic constructions to understand properly the different factors driving their dynamic behavior. In this article, the results of a two-month seismic monitoring survey carried out shortly after the Le Teil mainshock in a historic masonry tower are presented and discussed. Located only 5 km south of the epicenter, the Gate Tower of Viviers (eleventh century) was instrumented with four highly sensitive seismic nodes. Ambient vibrations, as well as aftershocks and quarry blasts from the nearby Le Teil quarry, were recorded and used in the analysis. Through vibration-based analysis, the article addresses three relevant aspects of the dynamic response of ancient masonry structures. We discuss first the differences in the building’s response induced by the three reported types of vibrations, focusing on the particular signal characteristics of shallow aftershocks and quarry blasts. Then, we apply the Random Decrement Technique (RDT) to track the dynamic behavior variations over two months and to discuss the role of the environmental conditions in the slight fluctuations of the structural modal parameters (natural frequencies, damping coefficients) of unreinforced masonry structures. We also show evidence of the nonlinear elastic behavior under both weak seismic and atmospheric loadings. The correlation between the presence of heterogeneities in the construction materials and the nonlinear threshold supports the relevance of such types of monitoring surveys as a valuable tool for future modeling works and conservation efforts.

Roads serve as vital parts of our infrastructure, providing as crucial conduits for people's mobility and connectivity. However, the growing number of vehicles on the road has resulted in an increase in pavement strain and degradation, which has a substantial impact on the entire riding experience. To achieve a high-quality surface, roadways must be consistently monitored and maintained. In recent years, transportation infrastructure agencies and governments have shown a rising interest in leveraging new technologies to monitor road pavements. This interest derives from the difficult and time-consuming nature of manual and instrumented techniques. Automated technologies have arisen as a response to these issues, notably in recognizing pavement deterioration, such as the common problem of potholes. The objective of this research is to identify potholes using two low-cost automated techniques: a vibration-based method that uses the G-Sensor Logger application and a vision-based way that uses image processing. On the same roads, both approaches were employed and compared, with manual surveying utilized to validate the results. The results showed that vision-based strategies were more effective than vibration-based methods. Finally, although vibration-based analysis is appropriate for routine monitoring, vision-based analysis provides a more comprehensive and in-depth examination of road conditions. These discoveries will help future efforts to better monitor and maintain road surfaces, ensuring a smooth and safe travel experience for everybody.

This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motorvibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is fairly incipient. Conventional induction motor condition-based maintenance methods usually involve current and voltage measurements, which could be expensive to collect, and vibration-based analysis is often only capable of detecting the fault when it is already quite significant. In contrast, the solution presented in this study provides a refreshing perspective by applying time synchronous averaging to remove the discrete frequency component, and amplitude demodulation to further enhance the signal with the help of kurtogram. Experimental results on a three-phase induction motor show that the method is also able to distinguish different fault severity levels.

This study develops a robust diagnostic framework for early fault detection in tapered roller bearings (TRBs) using vibration-based analysis. It explores the effect of operating parameters on vibration kurtosis to enable fault diagnosis in the 30205J2/Q, 30206J2/Q, and 30207J2/Q TRBs. The proposed Taguchi L27 orthogonal experimental design analyzes the effects of speed, load, unbalance, bearing type, and defect severity on kurtosis. Both the Inner Race Defect Model (IRDM) and Outer Race Defect Model (ORDM) demonstrate high predictive accuracy with R² values of 98.68% and 97.61% respectively. Analysis of variance (ANOVA) results indicate that the bearing type significantly impacts IRDM, while speed and defect geometry dominate ORDM. Time and frequency-domain analysis reveal distinct vibration patterns for effective fault identification. The interaction of speed, load, defect type, unbalance and bearing type significantly influenced kurtosis, highlighting the need for diagnostic strategies. Predictive kurtosis thresholds were established to enable effective condition monitoring and reducing failures.

This paper presents a complete study on the dynamic behavior of the 132 m high Cabril arch dam (Portugal). In operation for almost 70 years, horizontal cracks appeared on the downstream face after the first reservoir filling, and in the 1980s the dam started showing signs of concrete swelling. To address the concerns about its long-term safety, a pioneering continuous vibration monitoring system, designed for Seismic and Structural Health Monitoring, was installed in Cabril dam in 2008. In this work, the vibrations recorded under ambient/operational conditions for more than a decade are analyzed to estimate the modal parameters of the dam, which are then used to calibrate and validate a finite element model of the dam-reservoir-foundation system with the horizontal cracking. After that, the dynamic behavior of Cabril dam is simulated for the next decades, considering a computationally generated scenario of progressive damage due to concrete swelling. The results show that a) the dynamic behavior of Cabril dam is not being affected by the existing swelling phenomenon, and b) vibration-based analysis can be effective for detecting structural changes due to progressive damage. Lastly, the calibrated model is adapted for conducting non-linear seismic simulations, considering joint movements and tensile and compressive concrete damage. A method based on Endurance Time Analysis method is applied to assess the seismic safety of the dam with respect to the Operating Basis Earthquake and the Safety Evaluation Earthquake. The results confirm that Cabril dam can withstand accelerations several times greater than both earthquake levels, showing its adequate seismic capacity.

As a pivotal mechanical component of turbine drive-chain, gearbox suffers from various accidents and fault diagnosis could remarkably enhance its reliability and maintain running safety and lifetime. Vibration-based analysis on single-channel is mainly adopted in previous diagnostic, which cannot catch full vibration inner information in gearbox sensors and bring model performance down. In this paper, a novel method based on the fusion of multi-direction and multi-source signals and the convolutional neural network (MDMS-CNN) is presented. This method can obtain multi-dimensional feature maps from MDMS and preserve the time correlation among different signal sequences. First, features from MDMS are extracted via a corrosion conversion method that converts signals into two-dimensional (2-D) gray images. Then, homologous images are spliced into same channels, while heterogeneous images are superimposed to different channels. Finally, fusion features are input into CNN for fault classification. In experiments, the method is validated on a gearbox diagnosis case. Results have shown that the proposed method achieves 99.86% recognition accuracy and faster convergence speed compared to others.

Fault diagnosis for circuit-breakers using adaptive chirp mode decomposition and attractor’s morphological characteristics