Home / Journals / SDHM / Vol.11, No.2, 2017
  • Open Access

    REVIEW

    A Review of Structural Health Monitoring Techniques as Applied to Composite Structures

    Amafabia, Daerefa-a Mitsheal1, Montalvão, Diogo2, David-West, Opukuro1, Haritos, George1
    Structural Durability & Health Monitoring, Vol.11, No.2, pp. 91-147, 2017, DOI:10.3970/sdhm.2017.011.091
    Abstract Structural Health Monitoring (SHM) is the process of collecting, interpreting and analysing data from structures in order to determine its health status and the remaining life span. Composite materials have been extensively use in recent years in several industries with the aim at reducing the total weight of structures while improving their mechanical properties. However, composite materials are prone to develop damage when subjected to low to medium impacts (i.e. 1-10 m/s and 11-30 m/s respectively). Hence, the need to use SHM techniques to detect damage at the incipient initiation in composite materials is of high importance. Despite the availability… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Analysis of the Properties and Anti-Seepage Mechanism of PBFC Slurry in Landfill

    Guozhong Dai1,*, Jia Zhu2, Guicai Shi3, Yanmin Sheng4, Shujin Li5
    Structural Durability & Health Monitoring, Vol.11, No.2, pp. 169-190, 2017, DOI:10.3970/sdhm.2017.011.169
    Abstract As the landfill leachate has strong pollution on the underground water, surface water and soil. This paper develops the formula of impervious slurry with low permeability, good durability, strong adsorption and retardant based on the bentonite which is modified by polyvinyl alcohol. Through the simulation experiment, the optimum formula of polyvinyl alcohol is 0.2%. Its osmotic coefficient for 28 days is 0.53×10-8~1.86×10-8 cm/s and compressive strength is 0.5~1.5 MPa as well. This paper study on the retardant rule of the consolidation of slurry against the pollution in the leachate by self-made percolation instrument. The experiment shows that the retardant rate… More >

  • Open Access

    ARTICLE

    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 >

Share Link

WeChat scan