Lithium-ion (Li-ion) batteries have been widely applied in industrial applications. It is desired to predict the health state of batteries to achieve optimal operation and health management. However, accuracy is the biggest bottleneck for battery health prediction. In this paper, a new hybrid ensemble data-driven method is proposed to accurately predict the state-of-health (SOH) and remaining-useful-life (RUL) of Li-ion batteries. A health indicator is selected as feature inputs to predict the degradation trend of battery, after the Pearson correlation analysis. Two random learning algorithms are integrated to extract the inherent relationship between the extracted health indicator and practical SOH due to their good learning performance. Based on the estimated SOH, the nonlinear autoregressive (NAR) structure is designed to reduce the RUL prediction error of each learning model since the NAR structure makes good use of historical and current information. Finally, in order to quantitatively evaluate the prediction interval of the RUL, a Bootstrap-based uncertainty management method is designed. Test results on two publicly available datasets show that the proposed hybrid data-driven method can accurately predict the SOH and RUL of batteries. The proposed method does not require any other additional hardware or system downtime, which makes it suitable for online practical applications, such as energy storage systems and electric vehicles.

This paper presents an online data-driven diagnosis method for multiple IGBTs open-circuit faults and current sensor faults in the three-phase PWM inverter. The Fast Fourier Transform (FFT) algorithm is used to extract the fault frequency spectrum features of the three-phase currents. Then, a feature selection method named ReliefF is introduced to select the most critical features by removing redundant and irrelevant features. In addition, as a novel fast learning technology, Random Vector Functional Link (RVFL) network is applied to learn the faulty knowledge from the historical dataset. Based on the well-learned model, the fault type and location of the converter can be accurately identified as long as the three-phase current signals are measured. Offline test results verify that the proposed method can identify the IGBT and sensor faults with an accuracy of 98.83% and outperforms state-of-the-art learning algorithms. Moreover, the real-time hardware-in-the-loop test results show that the proposed method can successfully identify the IGBT faults and current sensor faults within 22 milliseconds. It is robust to the DC-link voltage fluctuations, model parameters, and speed or load variations. The extensibility of the proposed method is also validated based on the test results in terms of other fault modes and drive systems.

Three-phase PWM inverter fed induction motor drive system is widely applied in high power drive applications. Sensor faults are very common in the drive system which, once occur, might result in degraded system performance or even system shutdown. In order to rapidly and accurately diagnose the sensor faults, this paper proposes an intelligent time-adaptive data-driven method to identify the fault location and fault type of sensors in the drive system. An emerging machine learning technology named extreme learning machine (ELM) is applied to learn the sensor fault dataset, an ensemble ELM classifier is then designed to improve diagnostic accuracy, based on which a time-adaptive fault diagnosis process is proposed to achieve a high and balanced diagnostic accuracy and speed. As a data-driven method, the proposed method only employs the phase current, DC-link voltage, and speed signals as the inputs to the ensemble ELM classifiers, and requires no additional sensors and other hardware. Simulated and experimental tests show that the proposed method can rapidly and accurately detect the fault sensor location and identify offset fault, stuck fault, and noise faults with an average diagnostic accuracy of 98% and average decision time of 10ms after the fault occurs.

The state-of-health (SOH) estimation of lithium-ion batteries (LIBs) is of great importance to the safety of systems. In this paper, a novel ensemble learning method is proposed to accurately estimate the SOH of LIBs. A feature defined as the duration of the same charging voltage range (DSCVR) is extracted as the key health indicator for the LIB. Pearson correlation analysis is performed to select four optimal indicators that are used as inputs of the prediction model. A random learning algorithm named extreme learning machine (ELM) is applied to extract the mapping knowledge relationship between the health indicators and SOH due to its fast learning speed and efficient tuning mechanism. Moreover, an ensemble learning structure is proposed to reduce the prediction error of the single ELM models. A reliable decision-making rule is then designed to evaluate the credibility of the output of each single ELM model and remove the unreliable outputs, thereby significantly improve the accuracy and reliability of the estimation results. The testing results on two public datasets show that the proposed method can accurately estimate the SOH in 1ms and is robust to the operating temperature and load profile. The average root-mean-square error (RMSE) is as low as 0.78%. The proposed method does not require any additional hardware or downtime of the system, which makes the method suitable for online practical applications.

The intermittent fault (IF) is usually overlooked in power electronic applications. In this letter, an intelligent diagnosis method based on a data-driven model is proposed for sensor IFs. First, the manifestation of IF in the time domain is discussed to explore its distinctive characteristics. Then, a signal predictor is constructed in a data-driven way by utilizing the nonlinear autoregressive exogenous structure with the extreme learning machine algorithm. In addition, the residual is generated online by comparing the output of the devised data-driven predictor and that of the real sensor. The fault diagnosis decision-making scheme is finally designed based on the residual evaluation to identify the sensor IF and permanent fault simultaneously. The feasibility and effectiveness of the proposed method are demonstrated by offline tests and real-time experimental tests.

The increasing importance of cascaded H-bridge multilevel converters (CHBMCs) in high-power applications requires a higher priority to detect and protect against fault conditions. In this article, a current residual-based diagnosis scheme is proposed for the CHBMCs without additional sensors. On the premise of filtering the sampled dc voltages, comparing the calculated actual values of output current with the expected values in the control system, normalized diagnostic variables are established to localize single or multiple open-circuit switches. Benefit from the diagnostic variables corresponding directly to the switches of each H-bridge, this method can be easily extended to the CHBMCs with arbitrary submodules. Furthermore, the parameter estimation and enhanced detection algorithm are introduced to ensure the immunity of the diagnostic method to the large parameter changes and measurement noises. The faulty component can be accurately diagnosed within one line cycle regardless of the faulty modules and operating conditions. The effectiveness of the proposed method is substantiated using experimental results from the hardware platform of the CHBMCs.