Junyu Chen¹, Yunwen Feng^1,*, Cheng Lu^1,2, Chengwei Fei²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 1013-1027, 2021, DOI:10.32604/cmes.2021.016980 - 08 October 2021

Abstract As the key component in aeroengine rotor systems, the health status of rolling bearings directly influences the reliability and safety of aeroengine rotor systems. In order to monitor rolling bearing conditions, a fusion fault diagnosis method, namely empirical mode decomposition (EMD)-Mahalanobis distance (E2MD) and improved wavelet threshold (IWT) (E2MD-IWT) for vibrational signals and acoustic emission (AE) signals is developed to improve the diagnostic accuracy of rolling bearings. The IWT method is proposed with a hard wavelet threshold and a soft wavelet threshold. Moreover, it is shown to be effective through numerical simulation. EMD is utilized… More >

Wei Zhang^1,2, Qihui Ling^1,2,*, Qiancheng Zhao^1,2, Hushu Wu³

Energy Engineering, Vol.118, No.5, pp. 1515-1535, 2021, DOI:10.32604/EE.2021.014162 - 16 July 2021

Abstract The condition monitoring and fault diagnosis have been identified as the key to achieving higher availabilities of wind turbines. Numerous studies show that the open-circuit fault is a significant contributor to the failures of wind turbine converter. However, the multiple faults combinations and the influence of wind speed changes abruptly, grid voltage sags and noise interference have brought great challenges to fault diagnosis. Accordingly, concerning the open-circuit fault of converters in direct-driven PMSG wind turbine, a diagnostic method for multiple open-circuit faults is proposed in this paper, which is divided into two tasks: The first… More >

Dileep Kumar^1,*, Imtiaz Hussain Kalwar², Tanweer Hussain¹, Bhawani Shankar Chowdhry¹, Sanaullah Mehran Ujjan¹, Tayab Din Memon³

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 393-408, 2021, DOI:10.32604/cmc.2021.014941 - 04 June 2021

Abstract Reliability of rotating machines is highly dependent on the smooth rolling of bearings. Thus, it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach. In the recent past, Deep Learning (DL) has become applicable in condition monitoring of rotating machines owing to its performance. This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images. The proposed method is the UNET model that is a recent development in DL models. The model More >

Ibrahim B.M. Taha¹, Diaa-Eldin A. Mansour^2,*

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 739-752, 2021, DOI:10.32604/iasc.2021.017703 - 20 April 2021

Abstract Power transformer is one of the more important components of electrical power systems. The early detection of transformer faults increases the power system reliability. Dissolved gas analysis (DGA) is one of the most favorite approaches used for power transformer fault prediction due to its easiness and applicability for online diagnosis. However, the imbalanced, insufficient and overlap of DGA dataset impose a challenge towards powerful and accurate diagnosis. In this work, a novel fault diagnosis for power transformers is introduced based on DGA by using data transformation and six optimized machine learning (OML) methods. Four data… More >

Gopi Krishna Durbhaka¹, Barani Selvaraj¹, Mamta Mittal², Tanzila Saba^3,*, Amjad Rehman³, Lalit Mohan Goyal⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131 - 26 November 2020

Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have More >

R. Uma Maheswari^1,*, R. Umamaheswari²

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 479-488, 2020, DOI:10.32604/iasc.2020.013924

Abstract To enhance the predictive condition-based maintenance (CBMS), a reliable automatic Drivetrain fault detection technique based on vibration monitoring is proposed. Accelerometer sensors are mounted on a wind turbine drivetrain at different spatial locations to measure the vibration from multiple vibration sources. In this work, multi-channel signals are fused and monocomponent modes of oscillation are reconstructed by the Multivariate Empirical Mode Decomposition (MEMD) Technique. Noise assisted methodology is adapted to palliate the mixing of modes with common frequency scales. The instantaneous amplitude envelope and instantaneous frequency are estimated with the Hilbert transform. Low order and high More >

Gulsah Karaduman^*, Mehmet Karakose, Ilhan Aydin, Erhan Akin

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 455-463, 2020, DOI:10.32604/iasc.2020.013922

Abstract The use of railways has continually increased with high-speed trains. The increased speed and usage wear on the rails poses a serious problem. In recent years, to detect wear and cracks in the rails, image-based detection methods have been developed. In this paper, wears on the surface of railheads are detected by contactless image processing and image analysis techniques. The shadow removal algorithm with a minimal entropy method is implemented onto the noise-free images to eliminate the light variations that can occur on the rail. The Hough transform is applied on the noise and shadow More >

Suhas S. Aralikatti¹, K. N. Ravikumar¹, Hemantha Kumar^1,*, H. Shivananda Nayaka¹, V. Sugumaran²

Structural Durability & Health Monitoring, Vol.14, No.2, pp. 127-145, 2020, DOI:10.32604/sdhm.2020.07595 - 23 June 2020

Abstract The state of cutting tool determines the quality of surface produced on the machined parts. A faulty tool produces poor surface, inaccurate geometry and non-economic production. Thus, it is necessary to monitor tool condition for a machining process to have superior quality and economic production. In the present study, fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique. Cutting force and vibration signals were acquired to monitor tool condition during machining. A set of four tooling conditions namely healthy, worn flank, broken insert and extended… More >

Xiaoping Zhao^{1, 4}, Yifei Wang^{2, *}, Yonghong Zhang², Jiaxin Wu¹, Yunqing Shi³

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 571-587, 2020, DOI:10.32604/cmc.2020.06363 - 08 April 2019

Abstract Stochastic resonance can use noise to enhance weak signals, effectively reducing the effect of noise signals on feature extraction. In order to improve the early fault recognition rate of rolling bearings, and to overcome the shortcomings of lack of interaction in the selection of SR (Stochastic Resonance) method parameters and the lack of validation of the extracted features, an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed. compared with the existing methods, the AGSR (Adaptive Genetic Stochastic Resonance) method uses genetic algorithms to optimize the system parameters, and further optimizes More >

Kun Zhu¹, Shi Ying^{1, *}, Nana Zhang¹, Rui Wang¹, Yutong Wu¹, Gongjin Lan², Xu Wang²

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1161-1185, 2020, DOI:10.32604/cmc.2020.05247

Abstract SaaS software that provides services through cloud platform has been more widely used nowadays. However, when SaaS software is running, it will suffer from performance fault due to factors such as the software structural design or complex environments. It is a major challenge that how to diagnose software quickly and accurately when the performance fault occurs. For this challenge, we propose a novel performance fault diagnosis method for SaaS software based on GBDT (Gradient Boosting Decision Tree) algorithm. In particular, we leverage the monitoring mean to obtain the performance log and warning log when the… More >