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  • Open Access


    Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

    Alamelu Manghai T Marimuthu1, Jegadeeshwaran Rakkiyannan2,*, Lakshmipathi Jakkamputi1, Sugumaran Vaithiyanathan1, Sakthivel Gnanasekaran2

    Structural Durability & Health Monitoring, Vol.16, No.4, pp. 383-396, 2022, DOI:10.32604/sdhm.2022.011396

    Abstract The requirement of fault diagnosis in the field of automobiles is growing higher day by day. The reliability of human resources for the fault diagnosis is uncertain. Brakes are one of the major critical components in automobiles that require closer and active observation. This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis. Vibration signals of a rotating element contain dynamic information about its health condition. Hence, the vibration signals were used for the brake fault diagnosis study. The study was carried out on a brake fault diagnosis experimental setup. The vibration signals… More >

  • Open Access


    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 through histogram features. In this… More >

  • Open Access


    Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

    P. S. Anoop1, V. Sugumaran2

    Structural Durability & Health Monitoring, Vol.11, No.2, pp. 191-208, 2017, DOI:10.3970/sdhm.2017.011.191

    Abstract Tyre pressure monitoring system (TPMS) is compulsory in most countries like the United States and European Union. The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data. A difference in wheel speed would trigger an alarm based on the algorithm implemented. In this paper, machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer. The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process. The… More >

  • Open Access


    Feature-Based Vibration Monitoring of a Hydraulic Brake System Using Machine Learning

    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 >

  • Open Access


    Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

    A. Joshuva1, V. Sugumaran2

    Structural Durability & Health Monitoring, Vol.13, No.2, pp. 181-203, 2019, DOI:10.32604/sdhm.2019.00287

    Abstract Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The models are built based on… More >

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