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Modularity and high reliability from redundancy are the two attractive advantages of modular multilevel converters (MMCs). This paper elaborates a switch open-circuit fault diagnosis and a fault-tolerant operation scheme for MMCs with distributed control. The proposed fault diagnosis and fault-tolerant control method can significantly improve the reliability of the MMC while maintaining the modularity of its software implementation. By distributing fault diagnosis into submodules, its local controller is capable of identifying the switching devices in open-circuit fault without extra hardware circuitry. Based on the real-time measurements of submodule terminal voltage and arm current, single, or multiple faulty switches can be identified within 3.5 ms without triggering faulty alarms. Furthermore, a new fault-tolerant operation is proposed to maintain the output current, internal dynamics, and switching harmonics unchanged after the faulty submodule is bypassed. This is achieved by resetting the period and phase registers in the local controller according to the information of bypassed submodules. The control loops of the MMC are not influenced by the proposed fault diagnosis and fault-tolerant operation, making the operation transition seamless and reliable. Experimental results show that fault identification and system reconfiguration can be completed within 5 ms, and the MMC can seamlessly and smooth ride through the switch open-circuit faults without severe malfunction and catastrophic damages. [ABSTRACT FROM AUTHOR]

Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this article, a real-time early fault diagnosis scheme for lithium-ion batteries is proposed. By applying both the discrete Fréchet distance and local outlier factor to the voltage and temperature data of the battery cell/module that measured in real time, the battery cell that will have thermal runaway is detected before thermal runaway happens. Compared with the widely used single parameter based diagnosis approach, the proposed one considerably improve the reliability of the fault diagnosis and reduce the false diagnosis rate. The effectiveness of the proposed method is validated with the operational data from electric vehicles with/without thermal runaway in daily use. [ABSTRACT FROM AUTHOR]

Root-cause identification of faults in a distribution network is conducive to fault inspection as well as reducing injuries and damage. In this article, a novel method based on correlation dimension and average resistance is proposed to identify the root cause of permanent single line-to-ground fault in urban small current grounding systems. First, the reliability of the grounding fault and whether the fault occurred on a dry or wet surface can be identified by the correlation dimension. Then, the average resistance of the fault is used to classify the specific root cause, including wet land, dry land, wet concrete, dry concrete, and water. Experimental results show that this method can realize the classification of root causes and is robust to sampling rate, harmonics, and line impedance. [ABSTRACT FROM AUTHOR]

Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. First, the acquired sensor signals are converted to time–frequency distribution (TFD) by wavelet transform. Then, a deep CNN is applied to learning discriminative representations from the TFD images. Since then, a fully connected layer in deep architecture gives the prediction of induction motor condition based on learned features. In order to verify the effectiveness of the designed deep model, experiments are carried out on a machine fault simulator where both vibration and current signals are analyzed. Experimental results indicate that the proposed method outperforms traditional fault diagnosis methods, hence, demonstrating effectiveness in induction motor application. Compared with conventional methods that rely on delicate features extracted by experienced experts, the proposed deep model is able to automatically learn and select suitable features that contribute to accurate fault diagnosis. Compared with single-signal input, the multi-signal model has more accurate and stable performance and overcomes the overfitting problem to some degree. [ABSTRACT FROM AUTHOR]

Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold–Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time–frequency ridge is separated from the time–frequency representation (TFR) of the vibration signal using a peak search method; 2) according to the time–frequency ridge, GVKF parameters corresponding to all the fault characteristic frequencies (FCFs) are estimated; 3) the fault feature components are obtained using the generalized demodulation transform (GDT) and the VKF with the GVKF parameters; and 4) the spectra obtained by the fast Fourier transform (FFT) are used to fault detection. The main contributions of the proposed method are as follows: 1) the influence of speed fluctuations and the unrelated harmonic components are removed through the integration of the GDT and the VKF and 2) the tachometerless GVKF parameters are defined and calculated to quantitatively detect different fault types, which avoids missed diagnosis and misdiagnosis. The proposed multifault diagnosis algorithm is verified by both simulation and experiment data. Comparison with other commonly used techniques has shown the advantage of the new method. [ABSTRACT FROM AUTHOR]