Home / Journals / SDHM / Vol.11, No.1, 2017
  • 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) is less than the cost… More >

  • Open Access

    ARTICLE

    Structural Damage Detection in Framed Structures using Under Foundation Settlement/ Rotation of Bases

    Siddesha H1, Manjunath N Hegde2
    Structural Durability & Health Monitoring, Vol.11, No.1, pp. 17-41, 2017, DOI:10.3970/sdhm.2017.012.017
    Abstract This paper describes the damage detection in framed structures due to the vertical support settlement and rotation of footing bases. The damage detection procedure proposed by Nobahari and Seyedpoor (2013) is used to detect the damage in the members of the frame. In the present study, instead of using the flexibility matrix (referred here as original flexibility matrix) method, the generalized flexibility matrix is used in the same algorithm and the results are compared. The algorithm uses flexibility matrix and strain energy concept to detect the damage in the members. The behaviour of the frame is discussed through changes observed… More >

  • Open Access

    ARTICLE

    Brake Fault Diagnosis Through Machine Learning Approaches – A Review

    Alamelu Manghai T.M.1, Jegadeeshwaran R2, Sugumaran V.3
    Structural Durability & Health Monitoring, Vol.11, No.1, pp. 43-67, 2017, DOI:10.3970/sdhm.2017.012.043
    Abstract Diagnosis is the recognition of the nature and cause of a certain phenomenon. It is generally used to determine cause and effect of a problem. Machine fault diagnosis is a field of finding faults arising in machines. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, thermal imaging, oil particle analysis, etc. Then these data are processed using methods like spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform, high-resolution spectral analysis, waveform analysis, etc., The results of this analysis are used in a root cause failure analysis in order… 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 to catastrophic damage to the… More >

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