Fast concept drift detection using singular vector decomposition

© 2017 IEEE. Data stream mining is widely used in online applications such as sensor networks, financial transactions, etc. Such systems generate data at high velocity and their underlying distributions may change over time. This is referred to as concept drift problem and it is considered to be the root cause of performance degradation of online machine learning models. To tackle this problem, a reliable and fast drift detection method is required to achieve real time responsiveness to the drifts. This paper presents a fast and accurate drift detection method, namely KS-SVD test - KSSVD, to monitor the distribution changes of the data stream. Our method employs the SVD technique to first check the direction change of the data, followed by a KS test on each direction to detect the univariate distribution changes. Experiments show that our method is efficient and accurate, especially in high dimension situation

Dynamic parameters of structures extracted from ambient vibration measurements: an aid for the seismic vulnerability assessment of existing buildings in moderate seismic hazard regions

During the past two decades, the use of ambient vibrations for modal analysis of structures has increased as compared to the traditional techniques (forced vibrations). The Frequency Domain Decomposition method is nowadays widely used in modal analysis because of its accuracy and simplicity. In this paper, we first present the physical meaning of the FDD method to estimate the modal parameters. We discuss then the process used for the evaluation of the building stiffness deduced from the modal shapes. The models considered here are 1D lumped-mass beams and especially the shear beam. The analytical solution of the equations of motion makes it possible to simulate the motion due to a weak to moderate earthquake and then the inter-storey drift knowing only the modal parameters (modal model). This process is finally applied to a 9-storey reinforced concrete (RC) dwelling in Grenoble (France). We successfully compared the building motion for an artificial ground motion deduced from the model estimated using ambient vibrations and recorded in the building. The stiffness of each storey and the inter-storey drift were also calculated

Semi-Supervised Learning for Diagnosing Faults in Electromechanical Systems

Safe and reliable operation of the systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Machine learning techniques are widely used for designing data-driven diagnostic models. The training procedure of a data-driven model usually requires a large amount of labeled data, which may not be always practical. This problem can be untangled by resorting to semi-supervised learning approaches, which enables the decision making procedure using only a few numbers of labeled samples coupled with a large number of unlabeled samples. Thus, it is crucial to conduct a critical study on the use of semi-supervised learning for the purpose of fault diagnosis. Another issue of concern is fault diagnosis in non-stationary environments, where data streams evolve over time, and as a result, model-based and most of the data-driven models are impractical. In this work, this has been addressed by means of an adaptive data-driven diagnostic model