T. Jayasree^1,*, R. Prem Ananth²

Sound & Vibration, Vol.54, No.1, pp. 57-74, 2020, DOI:10.32604/sv.2020.08573

Abstract Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by the combination of feature sets… More >

Mengling Zou¹, Zhengxuan Liu², Xianyi Chen^{3, *}

Journal on Big Data, Vol.1, No.3, pp. 151-158, 2019, DOI:10.32604/jbd.2019.09057

Abstract Image encryption (IE) is a very useful and popular technology to protect the privacy of users. Most algorithms usually encrypt the original image into an image similar to texture or noise, but texture and noise are an obvious visual indication that the image has been encrypted, which is more likely to cause the attacks of enemy. To overcome this shortcoming, many image encryption systems, which convert the original image into a carrier image with visual significance have been proposed. However, the generated cryptographic image still has texture features. In line with the idea of improving the visual quality of the… More >

Satya Prakash Yadav^{1, *}, Sachin Yadav²

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 303-321, 2020, DOI:10.32604/cmes.2020.08459

Abstract This paper presents a low intricate, profoundly energy effective MRI Images combination intended for remote visual sensor frameworks which leads to improved understanding and implementation of treatment; especially for radiology. This is done by combining the original picture which leads to a significant reduction in the computation time and frequency. The proposed technique conquers the calculation and energy impediment of low power tools and is examined as far as picture quality and energy is concerned. Reenactments are performed utilizing MATLAB 2018a, to quantify the resultant vitality investment funds and the reproduction results show that the proposed calculation is very quick… More >

Yanqiang Sun¹, Hongfang Chen^1,*, Liang Tang¹, Shuang Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 1011-1028, 2019, DOI:10.32604/cmes.2019.07950

Abstract A gear fault detection analysis method based on Fractional Wavelet Transform (FRWT) and Back Propagation Neural Network (BPNN) is proposed. Taking the changing order as the variable, the optimal order of gear vibration signals is determined by discrete fractional Fourier transform. Under the optimal order, the fractional wavelet transform is applied to eliminate noise from gear vibration signals. In this way, useful components of vibration signals can be successfully separated from background noise. Then, a set of feature vectors obtained by calculating the characteristic parameters for the de-noised signals are used to characterize the gear vibration features. Finally, the feature… More >

T. M. Alamelu Manghai¹, R. Jegadeeshwaran²

Structural Durability & Health Monitoring, Vol.11, No.2, pp. 149-167, 2017, DOI:10.3970/sdhm.2017.011.149

Abstract Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road. Therefore, monitoring the condition of the brake components is inevitable. The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches. The vibration signals were captured using an accelerometer sensor under a various fault condition. The acquired vibration signals were processed for extracting meaningful information as features. The condition of the brake system can be predicted using a feature… More >

He Xufei¹, Deng Zhongmin², Song Zhitao¹

Structural Durability & Health Monitoring, Vol.8, No.3, pp. 193-208, 2012, DOI:10.32604/sdhm.2012.008.193

Abstract This paper describes an information optimizing scheme which is developed by integrating rough set and hierarchical data fusion. The novel structural damage indices are extracted using the information from different sources and then imported into probabilistic neural network (PNN) for classification and health assessment. In order to enhance the accuracy of diagnosis, results from separate PNN classification are fused to achieve comprehensive decision. Rough set is employed to decrease the spatial dimension of data. The predictive accuracy of optimizing scheme is demonstrated on a helicopter, taken as an example, with varied sensors, for multiple damage identification. More >

G. Giridhara¹, S. Gopalakrishnan²

Structural Durability & Health Monitoring, Vol.5, No.1, pp. 1-32, 2009, DOI:10.3970/sdhm.2009.005.001

Abstract In this paper, a new damage measure in the frequency domain (FDI), which uses the definition of strain energy in the frequency domain, is proposed. The proposed damage index is derived using the definition of frequency domain strain energy. The base line responses and the strain energies are computed using Wavelet Spectral Finite elements, while the strain energies for the damaged structure is computed using four high fidelity experimental responses. The sensitivity of the damage measure in locating cracks of different sizes and orientation is demonstrated on a square plate, the rectangular plate and on a compressor blade. More >

S. Abdullah¹

Structural Durability & Health Monitoring, Vol.4, No.4, pp. 181-198, 2008, DOI:10.3970/sdhm.2008.004.181

Abstract This paper describes a technique to identify the important features in fatigue road spectrum loading, for which these features cause the majority of the total damage. Fatigue damaging events, called bump segments, are extracted from the spectrum loading using a wavelet-based algorithm, called Wavelet Bump Extraction (WBE). This algorithm is also used to produce a shortened mission signal that retains most of the fatigue damage whilst preserving the cycle sequences. The bump identification process has been evaluated by analysing two road spectrum loadings having a variable amplitude pattern. These data sets were obtained from the strain measurement on the lower… More >

RamanaM. Pidaparti¹

Structural Durability & Health Monitoring, Vol.2, No.3, pp. 131-148, 2006, DOI:10.3970/sdhm.2006.002.131

Abstract This paper provides an overview of the computational intelligence methods developed for the structural integrity assessment of aging aircraft structures. Computational intelligence techniques reviewed include artificial neural networks, inverse neural network mapping, wavelet based image processing methods, genetic algorithms, spectral element methods, and particle swarm optimization. Multi-site damage, corrosion, and corrosion-fatigue damage in aging aircraft is specifically discussed. Results obtained from selected computational intelligence methods are presented and compared to the existing alternate solutions and experimental data. The results presented illustrate the applicability of computational intelligence methods for assessing the structural integrity of aging aircraft structures and materials. More >

P. Horst¹

Structural Durability & Health Monitoring, Vol.1, No.1, pp. 49-66, 2005, DOI:10.3970/sdhm.2005.001.049

Abstract The paper presents a Monte Carlo Simulation method for the assessment of Multiple Site Damage (MSD) and a subsequent attempt to find a way to interpret intermediate results of the Monte Carlo Simulation with respect to the criticality of scenarios. The basic deterministic part of the model is based on the compounding method, which is used in order to gain an acceptable computational effort. Some examples illustrate features of MSD scenarios and this allows to check an approach for feature detection via Wavelet transforms. This Wavelet transform approach shows some positive results in the interpretation of MSD scenarios. More >