Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features.

Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults' classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components' sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback-Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology's performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs' variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved 100% classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications.

An active disturbance rejection sensorless control strategy based on sliding mode observer for marine current turbine.

Marine current energy attracts much attention as a source of inexhaustible green energy. However, marine current turbines operate in a very harsh underwater environment, making it difficult to operate and maintain mechanical sensors. Therefore, the application of a sensorless control strategy is fully justified to improve the reliability of system electrical energy production. In this paper, an active disturbance rejection sensorless control strategy based on compensation sliding mode observer is proposed for the marine current turbine system. The proposed control method consists of two parts. The first part is to design an active disturbance rejection controller for the marine current turbine that will improve the system's anti-interference abilities. The second part proposes a time-delay compensation sliding mode observer based on the Smith predictor to conduct real-time delay compensation of the system. The entire system is ensured to be globally stable through the Lyapunov approach analysis. The proposed control strategy is verified by simulation, which can not only effectively suppress the lumped disturbance and eliminate the system time-delay, but also improve the power extraction capability of the system.

Imbalance Fault Detection Based on the Integrated Analysis Strategy for Marine Current Turbines under Variable Current Speed.

The conversion of marine current energy into electricity with marine current turbines (MCTs) promises renewable energy. However, the reliability and power quality of marine current turbines are degraded due to marine biological attachments on the blades. To benefit from all the information embedded in the three phases, we created a fault feature that was the derivative of the current vector modulus in a Concordia reference frame. Moreover, because of the varying marine current speed, fault features were non-stationary. A transformation based on new adaptive proportional sampling frequency (APSF) transformed them into stationary ones. The fault indicator was derived from the amplitude of the shaft rotating frequency, which was itself derived from its power spectrum. The method was validated with data collected from a test bed composed of a marine current turbine coupled to a 230 W permanent magnet synchronous generator. The results showed the efficiency of the method to detect an introduced imbalance fault with an additional mass of 80-220 g attached to blades. In comparison to methods that use a single piece of electrical information (phase current or voltage), the fault indicator based on the three currents was found to be, on average, 2.2 times greater. The results also showed that the fault indicator increased monotonically with the fault severity, with a 1.8 times-higher variation rate, as well as that the method is robust for the flow current speed that varies from 0.95 to 1.3 m/s.

An imbalance fault detection method based on data normalization and EMD for marine current turbines.

This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method.

FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine.

As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs.

[Specifics of tuberculosis in chronically dialysed black Africans]. / Particularités de la tuberculose chez le dialysé chronique Noir africain.

UNLABELLED: Infectious diseases are frequent in chronic dialysis patients. Our study had for objective to describe the prevalence and the clinical presentation of tuberculosis in the chronic dialysis patient. This observational study was conducted over a period of 21 months in chronically hemodialyzed patients. RESULTS: Among 118 patients chronically dialysed and during the study period, seven (5.9%) presented tuberculosis. An average lag time of 13,7 months was observed between the beginning of the dialysis and the appearance of the signs of tuberculosis, and of 10,4 weeks between the first signs and the beginning of treatment. Extrapulmonary localization was observed in six cases among seven peritoneal involvements. CONCLUSION: Extrapulmonary localisation is a characteristic of tuberculosis in the chronic dialysis patients. Systematic screening of tuberculosis is recommended in order to start chemotherapy without any delay.