Nadom K. Mutlib^1,*, Muna N. Ismael¹, Shahrizan Baharom²

Structural Durability & Health Monitoring, Vol.15, No.3, pp. 227-246, 2021, DOI:10.32604/sdhm.2021.010725

Abstract Structural health monitoring employs different tools and techniques to provide a prediction for damages that occur in various structures. Damages such as debond and cracks in concrete-filled steel tube column (CFST) are serious defects that threaten the integrity of the structural members. Ultrasonic waves monitoring applied to the CFST column is necessary to detect damages and quantify their size. However, without appropriate signal processing tools, the results of the monitoring process could not be crucial. In this research, a monitoring process based on a Multiphysics numerical simulation study was carried out. Two signal processing tools: short time Fourier transform (STFT)… More >

Vinay Arora¹, Karun Verma¹, Rohan Singh Leekha², Kyungroul Lee³, Chang Choi^4,*, Takshi Gupta⁵, Kashish Bhatia⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4151-4168, 2021, DOI:10.32604/cmc.2021.019178

Abstract The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an {efficient} technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual… More >

Majid Hussain^1,2, Dileep Kumar¹, Imtiaz Hussain Kalwar³, Tayab Din Memon^4,5, Zubair Ahmed Memon⁶, Kashif Nisar^7,*, Bhawani Shankar Chowdhry¹

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 869-883, 2021, DOI:10.32604/iasc.2021.017790

Abstract Induction motors (IMs) are the workhorse of the industry and are subjected to a harsh environment. Due to their operating conditions, they are exposed to different kinds of unavoidable faults that lead to unscheduled downtimes and losses. These faults may be detected early through predictive maintenance (i.e., deployment of condition monitoring systems). Motor current signature analysis (MCSA) is the most widely used technique to detect various faults in industrial motors. The stator winding faults (SWF) are one of the major faults. In this paper, we present an induction motor fault detection and identification system using signal processing techniques such as… More >

Oakyoung Han¹, Jaehyoun Kim^{2, *}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 553-566, 2020, DOI:10.32604/cmc.2020.09362

Abstract The processing of sound signals is significantly improved recently. Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques. This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music. For analysis and process part, we represented normalized waveforms for each of input data via frequency domain signals. Then we looked into shorted segment to see the difference wave pattern for different singers. Fourier transform is applied to get spectrogram of the music signals. Filterbank is applied to represent the… More >