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  • Open Access

    ARTICLE

    Application of Machine Learning for Tool Condition Monitoring in Turning

    A. D. Patange1,2, R. Jegadeeshwaran1,*, N. S. Bajaj2, A. N. Khairnar2, N. A. Gavade2

    Sound & Vibration, Vol.56, No.2, pp. 127-145, 2022, DOI:10.32604/sv.2022.014910

    Abstract

    The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of

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  • Open Access

    ARTICLE

    Optimization of Transducer Location for Novel Non-Intrusive Methodologies of Diagnosis in Diesel Engines

    S. Narayan1,*, M. U. Kaisan2, Shitu Abubakar2, Faisal O. Mahroogi3, Vipul Gupta4

    Sound & Vibration, Vol.55, No.3, pp. 221-234, 2021, DOI:10.32604/sv.2021.016539

    Abstract The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects More >

  • Open Access

    ARTICLE

    A Novel Method Based on UNET for Bearing Fault Diagnosis

    Dileep Kumar1,*, 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

    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 >

  • Open Access

    ARTICLE

    Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

    Gopi Krishna Durbhaka1, Barani Selvaraj1, Mamta Mittal2, Tanzila Saba3,*, Amjad Rehman3, Lalit Mohan Goyal4

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

    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 >

  • Open Access

    ARTICLE

    Condition Monitoring of an Industrial Oil Pump Using a Learning Based Technique

    Amin Ranjbar1, Amir Abolzafl Suratgar1,*, Saeed Shiry Ghidary2, Jafar Milimonfared3

    Sound & Vibration, Vol.54, No.4, pp. 257-267, 2020, DOI:10.32604/sv.2020.05055

    Abstract This paper proposes an efficient learning based approach to detect the faults of an industrial oil pump. The proposed method uses the wavelet transform and genetic algorithm (GA) ensemble for an optimal feature extraction procedure. Optimal features, which are dominated through this method, can remarkably represent the mechanical faults in the damaged machine. For the aim of condition monitoring, we considered five common types of malfunctions such as casing distortion, cavitation, looseness, misalignment, and unbalanced mass that occur during the machine operation. The proposed technique can determine optimal wavelet parameters and suitable statistical functions to More >

  • Open Access

    ARTICLE

    Comparative Study on Tree Classifiers for Application to Condition Monitoring of Wind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

    A. Joshuva1,*, V. Sugumaran2

    Structural Durability & Health Monitoring, Vol.13, No.4, pp. 399-416, 2019, DOI:10.32604/sdhm.2019.03014

    Abstract Wind energy is considered as a alternative renewable energy source due to its low operating cost when compared with other sources. The wind turbine is an essential system used to change kinetic energy into electrical energy. Wind turbine blades, in particular, require a competitive condition inspection approach as it is a significant component of the wind turbine system that costs around 20-25 percent of the total turbine cost. The main objective of this study is to differentiate between various blade faults which affect the wind turbine blade under operating conditions using a machine learning approach… More >

  • Open Access

    ARTICLE

    Integrated Condition Monitoring of Large Captive Power Plants and Aluminum Smelters

    J.K. Mohanty1, A. Adarsh2, P.R. Dash1, K. Parida1, P.K. Pradhan1,*

    Sound & Vibration, Vol.53, No.5, pp. 223-235, 2019, DOI:10.32604/sv.2019.07737

    Abstract Condition monitoring is implementation of the advanced diagnostic techniques to reduce downtime and to increase the efficiency and reliability. The research is for determining the usage of advanced techniques like Vibration analysis, Oil analysis and Thermography to diagnose ensuing problems of the Plant and Machinery at an early stage and plan to take corrective and preventive actions to eliminate the forthcoming breakdown and enhancing the reliability of the system. Nowadays, the most of the industries have adopted the condition monitoring techniques as a part of support system to the basic maintenance strategies. Major condition monitoring… More >

  • Open Access

    ARTICLE

    Condition Monitoring of Roller Bearing by K-Star Classifier and K-Nearest Neighborhood Classifier Using Sound Signal.

    Rahul Kumar Sharma*1, V. Sugumaran1, Hemantha Kumar2, Amarnath M3

    Structural Durability & Health Monitoring, Vol.11, No.1, pp. 1-16, 2017, DOI:10.3970/sdhm.2017.012.001

    Abstract Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone)… More >

  • Open Access

    ABSTRACT

    Development of On-line Structural Condition Monitoring System in Korean Nuclear Power Plant

    Jin-Ho Park1, Joon-Hyun Lee2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.13, No.4, pp. 65-66, 2009, DOI:10.3970/icces.2009.013.065

    Abstract The on-line structural integrity monitoring systems and the related techniques have been developed in Korean nuclear power plant for the purpose of Condition Based Maintenance(CBM). There are four different kinds of systems such as IVMS(Internal Vibration Monitoring System), LPMS(Loose Part Monitoring System), ALMS(Acoustic Leak Monitoring System), and RCP-VMS(Reactor Coolant Pump Vibration Monitoring system). The purpose of the IVMS is to monitor and diagnose the axial preload of the core support barrel and the abnormality of the motion of the fuel bundles inside the reactor pressure vessel. The LPMS is being operated to detect and alarm… More >

  • Open Access

    ARTICLE

    A Comparative Study of Bayes Classifiers for Blade Fault Diagnosis in Wind Turbines through Vibration Signals

    A. Joshuva1, V. Sugumaran2

    Structural Durability & Health Monitoring, Vol.11, No.1, pp. 69-90, 2017, DOI:10.3970/sdhm.2017.012.069

    Abstract Renewable energy sources are considered much in energy fields because of the contemporary energy calamities. Among the important alternatives being considered, wind energy is a durable competitor because of its dependability due to the development of the innovations, comparative cost effectiveness and great framework. To yield wind energy more proficiently, the structure of wind turbines has turned out to be substantially bigger, creating conservation and renovation works troublesome. Due to various ecological conditions, wind turbine blades are subjected to vibration and it leads to failure. If the failure is not diagnosed early, it will lead… More >

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