Numerous studies show that power semiconductors are significant contributors to the overall failure rate of PWM voltage source rectifier (VSR) systems. Aimed at this problem, this paper presents an improved diagnosis system for opencircuit faults of a three-phase PWM VSR based on AC current distortion characteristics, which consists of a diagnosis module, a register module and a decision module. The diagnostic module employs a hysteresis comparator to judge the change trend of the currents, and thereby diagnoses the single OC faults. The register module is employed to achieve multi-switch OC fault diagnosis, and the decision module is used to output the final result. The proposed method extends the previous work to multi-switch OC fault conditions as well as improves the diagnosis accuracy. The experimental results show that the proposed method can correctly achieve fault diagnosis, including 6 kinds of single switch faults and 15 kinds of different double switches faults. Additionally, the proposed method also shows superior antiinterference performance and high robustness on abrupt load transient conditions, different power factor conditions, unbalanced and/or distorted grid voltage conditions and different AC filter structure conditions. Furthermore, the average diagnostic time of this method is only 3.68 ms.

With the development of machine learning technology, numerous studies have been proposed to diagnose the open circuit (OC) faults in the Pulse Width Modulation (PWM) voltage source rectifier (VSR) systems. However, most methods require system signals of more than one current period, which show poor real-time performance. Aiming at this problem, this paper presents an improved diagnosis system based on Deep Belief Networks (DBN) and least square support vector machine (LSSVM). First, the Double chain quantum genetic algorithm (DCQGA) is employed to obtain the proper length of measured signals and DBN structure parameters. Then, the fault features are extracted from the signals through DBN. Finally, these features are used to train the LSSVM fault classifier to construct the diagnosis model. Experimental results show that the proposed method can achieve the fault diagnosis including 6 kinds of single switch faults and 15 kinds of different double switches faults correctly. Besides, the proposed method also shows superior anti-interference performance and high robustness on abrupt load transient conditions, unbalanced and/or distorted grid voltage conditions as well as different power factor conditions. Furthermore, the average diagnostic time of this method is only 2.57 ms.

In this paper, a novel and more effective fault diagnosis approach for wind turbine planetary gearbox (PGB) is proposed. In order to better detect the faults in the early stage of the faults of the wind turbine PGB, the corresponding maintenance measures can be carried out to prevent the faults from becoming more serious, so as to seriously affect the normal operation of the fan gearbox. The gear with lighter fault degree is used to simulate the early fault signal. Compared with the fault which has seriously affected the normal working condition, the fault characteristics of the early fault signal are more difficult to detect. So in this design, deep belief network (DBN) optimized by quantum particle swarm optimization (QPSO) algorithm is used to extract deeper and more identifiable features of slight fault signal. After optimization by QPSO algorithm, DBN can get a most suitable structure according to the actual working signal of fan gearbox. Then these extracted features are input into the least squares support vector machine (LSSVM) optimized by QPSO for fault diagnosis test. At the same time, the wireless sensor nodes using self-energy in vibration state are optimized. By using microcontroller unit (MCU) MSP430F149 and nRF24L01 radio frequency (RF) chip with lower energy consumption, the normal dormant state can be maintained, the power requirement of transmission mode can be met, the stability of the whole node can be improved, and the phenomenon of energy shortage caused by short-term fluctuation can be prevented. The comparative experiments in this paper show that this method has good effect on the fault diagnosis of wind turbine PGB.

This paper proposes a wind turbine planetary gearbox (PGB) fault diagnosis method based on self-powered wireless sensor. The proposed wireless sensor, which consists of piezoelectric energy harvester, power management circuit, microcontroller unit (MCU), radio frequency (RF) module and accelerometer, can acquire the vibration signals of wind turbine PGB by the accelerometer. A piezoelectric energy harvester utilizing vibration environment is optimized as power supply for the proposed wireless sensor, including MCU, RF module and accelerometer. An ac-dc converter combined with a low-dropout voltage regulator is developed to provide stable dc voltage for the proposed wireless sensor. Stacked denoising autoencoder (SDAE) shows excellent performance in learning robust features from noised signal. Thus, in this paper, SDAE method is adopted to learn robust and distinguishable features from measured signals. Then, the least squares support vector machine (LSSVM) is employed to classify features extracted by SDAE. Both the SDAE and LSSVM are optimized by quantum particle swarm optimization (QPSO). The experimental results show that the presented power supply can generate 3.3-V dc voltage, which ensures regular operation of the rest of the wireless sensor. The proposed wireless sensor can achieve a reliable communication distance of 40.8m in the test environment. Furthermore, the SDAE approach and LSSVM show excellent performance in feature extraction and fault diagnosis, respectively. Experimental results indicate that the proposed method is effective in terms of fault diagnosis for wind turbine PGB.

This work introduces a novel hybrid fault diagnosis method for power transformer. This method employs solar-powered radio frequency identification (RFID) sensor for transformer vibration signal acquisition and deep belief network (DBN) for feature extraction. The customized RFID sensor employs solar panel as power source, and a supercapacitor is adopted to be the stand-by power when the solar panel cannot work. A charging circuit is exploited to guarantee constant DC output voltage. The collected hybrid faults signal is characterized as nonlinear and nonstationary, moreover, it contains abundant noises and harmonic components, which makes it difficult to acquire succinct and robust features from the raw signals. Hence, the DBN is adopted to extract features from collected vibration signal. In order to obtain optimum feature extraction performance, the quantum particle swarm optimization algorithm (QPSO) is employed to determine the hidden layer structure and learning rate of the DBN model. The experiments indicate that the proposed RFID sensor is able to realize reliable data acquisition and transmission. Besides, the optimized DBN achieves remarkable results in feature extraction for the hybrid fault signal and achieves high diagnosis accuracy.

An incipient fault diagnosis method devised for Insulated Gate Bipolar Transistor (IGBT) drive circuit based on improved Stack auto-encoder (SAE) is recommended. Firstly, the Monte Carlo method is applied to extracting the time domain response signal of the circuit under test as sample data. Then, with SAE used to extract essential features of data, SAE is employed to extract features of sample data. Meanwhile, multi-classification Relevant Vector Machine (RVM) is involved for fault diagnosis of the acquired features. As the structure of the hidden layer in SAE and the learning rate could exert a significant effect on the feature extraction performance, in this paper, the Quantum Particle Swarm Optimization (QPSO) algorithm is used to optimize the above parameters. As revealed by the experimental results, the improved SAE method is effective in the extraction of the essential characteristics of the incipient faults for the IGBT drive circuit. Further with this, the incipient fault multi- classification RVM of the IGBT drive circuit is capable of achieving 100% diagnostic accuracy.