The hydrocracking process converts heavy oil into high-quality finished oil during petroleum refining, with high conversion rates and good selectivity. Accurate and timely production operation optimization is crucial for the production efficiency and product quality of hydrocracking. Heuristic evolutionary algorithms have been widely used due to their advantages of simple operation and high performance. However, traditional evolutionary algorithms carry too much mutation information due to individual mutation crossover operations and retain too little genetic information, which limits the algorithm's optimization ability and convergence speed. This article proposes a dual mutation improvement strategy for the traditional evolutionary algorithm family, which adjusts the dimensions beyond the range in the individual and introduces new mutation operations to enhance the inheritance ability of the population's genetic information. Finally, the proposed method was applied to the optimization of an actual hydrocracking production operation, and the results showed that the improved evolutionary algorithm family has better optimization ability and faster convergence speed.

Rolling bearings are key components in mechanical systems. Failure of bearings lead to equipment downtime, safety accidents, and significant economic losses. Predicting the remaining life of a bearing can improve equipment reliability, reduce maintenance costs, and increase productivity. A segmented method for predicting the remaining life of bearings using Long Short-Term memory network (LSTM) is proposed in this paper to improve the prediction accuracy. First, time domain, frequency domain and time-frequency features are extracted from the vibration signals, and effective features are screened by constructing comprehensive evaluation indexes. Second, t-SNE algorithm and DBSCAN clustering are used to adaptively classify bearing life into healthy and degraded stages. Finally, a segmented LSTM model is established for predicting RUL at different stages. The experiment is based on the bearing dataset from Xi'an Jiaotong University. The results of the comparative experiments show that the average absolute error of the prediction after stages is reduced by 5.12% compared with the traditional method, which verifies the effectiveness of the proposed method.

The frequent occurrence of motor faults has been a great disturbance to the development of production in various fields. Traditional fault diagnosis methods primarily use 1-D data or 2-D data. However, 3-D data hold significant promise for motor fault diagnosis due to its voluminous data and unique spatial information. This article aims to explore a motor fault diagnosis method leveraging 3-D data. Nonetheless, motor fault 3-D data exhibit the limitation of lacking geometric structure. To address this limitation, this article proposes a fault diagnosis method named SP-PointCNN. This method uses the proposed spherical projection (SP) method to mitigate the limitations present in the 3-D data of the motor. Meanwhile, a neural network based on PointCNN is constructed to enable the utilization of 3-D data in fault diagnosis. A series of experiments demonstrated the validity of the proposed method. After tested, the diagnosis accuracy of SP-PointCNN can reach 99.36%.

In the face of continuous dynamic changes in working conditions, the static deep learning bearing fault diagnosis model gradually loses its effectiveness. To this end, a novel bearing fault diagnosis method based on deep incremental learning is proposed in this paper. Firstly, a one-dimensional signal is converted into a two-dimensional image by Gramian angle field (GAF) to realize the extraction of global features of the input signal. The image is dimensionally transformed and input into a selectable classification network, which can improve the robustness and generalization ability of the model. Secondly, a parameter importance estimation method based on the change of loss value is designed. It can effectively alleviate the catastrophic forgetting problem in incremental learning caused by inaccurate estimation of the importance of the parameters of old working conditions. Then, the gradient clipping method using Huber regularization eliminates the gradient explosion induced by the quadratic terms in the regularization process. Finally, the validity of the method was verified with a dataset from the bearing data center of Case Western Reserve University. The results show that when dealing with incremental faults, the method can achieve diagnostic accuracies of 97.75% and 98.25% on different classification networks, respectively. And it can well overcome the stability-plasticity dilemma.

Existing fault diagnosis methods for rolling bearings are mostly applied in static domains and cannot adapt to continuous and dynamic industrial data in practice. In domain incremental scenarios, achieving continuous learning and avoiding catastrophic forgetting is a major challenge for fault diagnosis. In this article, a novel domain incremental learning method based on filtering columns and knowledge base (F&K-DIL) is proposed to achieve bearing fault diagnosis in dynamic domains. It consists of filtering columns, knowledge bases, and parameter update modules. In the filtering columns, two L1-norm are used to select representative samples, which can protect previously learned knowledge in each domain while learning new knowledge. The correctness of knowledge delivery is guaranteed. Sparsity parameters that can adjust the number of representative samples flexibly are introduced to improve storage efficiency. Representative samples in different domains are stored in the knowledge base to realize knowledge sharing. In the parameter update module, cross entropy and elastic weight consolidation loss function are used to realize constraints on important parameters in old domains. It can further avoid catastrophic forgetting. Through experiments on three rotating machinery datasets, F&K-DIL is used for fault diagnosis in domain incremental scenarios with an accuracy of up to 99.6% and 37% improvement in memory rate.

Most of the mechanical fault diagnosis methods of induction motors (IMs) are based on vibration signals. However, vibration sensors are expensive and require direct contact with IMs. This article proposes a data-driven mechanical fault diagnosis method for IMs using stator current signals, which is more convenient and low cost, since no additional sensors are required. Aiming at the weak representation of mechanical faults in current signals, an intelligent noise elimination method based on noise reconstruction model is proposed to improve the signal-to-noise ratio. Through the automatic feature extraction and classification of the residual current envelope spectrum, high diagnosis accuracy can be obtained even if some differences have existed among samples. The effectiveness of the proposed method is verified by high accuracy diagnosis results on two experimental platforms. The results show that the average diagnostic accuracy of the proposed algorithm for one bearing fault and two eccentricity faults can reach 96%. Even if the fault type and the fault degree are distinguished at the same time, an accuracy of 90% can be achieved for six kinds of bearing faults.

In intelligent fault diagnosis, transfer learning can reduce the requirement of sufficient labeled data and the same data distribution. However, for the diagnosis of a new machine, there are still some limitations, such as low accuracy or the demand for some labeled data with fault information in the new machine. In this article, we propose a stack autoencoder transfer learning algorithm based on the class separation and domain fusion (SAE-CSDF) to solve these problems. According to the characteristics of bearing faults, the proposed weighted domain fusion strategy can ensure the direction and balance in the transfer process. The proposed class separation degree can improve the accuracy of the target domain indirectly by extending the differences between the classes in the source domain. The effectiveness of the SAE-CSDF is verified via the mutual transfer of two public datasets and one laboratory dataset. The results show that the accuracy of the algorithm can reach 97% in the transfer between different machines, even if there is no labeled fault data in the new machine.

Impedance-source inverters provide a promising single-stage power conversion with boost-buck capabilities and inverter legs short-circuit immunity. To achieve superior thermal and output harmonic performance, two novel discontinuous modulation strategies for the three-phase quasi-Z source inverter, called the maximum-boost discontinuous space vector modulation (SVM) and the simple-boost discontinuous SVM respectively, are proposed in this article. By properly arranging the shoot-through states and clamping a certain switch to the positive or negative dc-link rail during one-third of the fundamental period, reduced thermal stresses and superior total harmonic distortion performance can be achieved. The proposed modulation strategies are analyzed and compared to the conventional ones in terms of current stresses, power losses, thermal stresses, and output harmonics. Finally, simulations and experimental tests are carried out to validate the performance of the proposed modulation strategies and verify the presented analysis.

Fault diagnosis is an important technology in the development of modern industrial safety. Vibration information is commonly used to determine the state of bearings. Driven by big data, deep learning brings new opportunities to fault diagnosis. As an unsupervised deep learning algorithm, a stacked autoencoder (SAE) can relieve the pressure of labelling data. Due to the diversity and variability of the actual fault diagnosis distribution, an optimized transfer learning (TL) algorithm is proposed to solve the domain adaptation. By directly inheriting features obtained from the pre-training process in the source domain and changing only the fine-tuning process, the complexity of the algorithm is reduced. Considering the data reconstruction ability and robustness, a sparse stacked denoising autoencoder (SSDAE) is proposed for feature extraction, which can indirectly improve the diagnostic accuracy in the target domain. The results for data from the Case Western Reserve University Bearing Data Center show that the proposed SSDAE-TL algorithm is feasible and easy to implement for the fault diagnosis of bearings. (C) 2019 Elsevier Ltd. All rights reserved.

The way of using optimal control theory to implement the optimal fuel control strategy of hybrid excavator is limited,because its computational complexity is large and the working conditions must be known in advance for global optimization.A real-time optimal fuel control strategy was proposed in this paper to solve the problem.An engine model of"speed-power-fuel consumption rate"was established. Under the constraint of DC bus voltage stability,the power compensation of the energy storage system was calculated as a control instruction,which can make the engine work efficiently.Finite control set model predictive control algorithm was proposed to follow the instruction speedily and flexibly.Through the sim-ulation,the effectiveness of the proposed approach was demonstrated.Engineering practice results indicate that the fuel consumption is 82.2% and 77.6% of the prediction of the traditional model with flat light load and heavy load,respectively.

The reliability of power modules in motor drive is closely related to its thermal cycling. Especially in low speed and high output power condition, the thermal performance can be even worse. In this paper, a fundamental frequency region-based thermal control strategy is presented. Firstly, the model for high power drive thermal cycling calculation is established. With the electro-thermal model, power switch junction temperature and its number of power cycles can be mapped with fundamental frequency. Then, the fundamental frequency region can be divided by the junction temperature fluctuation. Based on the obtained frequency region, a switching frequency control method is proposed to improve the thermal fluctuation and reliability. A comparison results with and without the proposed control method are given to validate the switching frequency hysteresis control strategy and the improvement on system power cycling capability.

High-frequency link matrix rectifier (HFLMR) consisting of a three-phase to single-phase AC/AC matrix converter, a high frequency link and asingle-phase rectifier circuit has achieved profound development since it was proposed. This paper presents typical HFLMR topologies and focuses on its high frequency modulation strategies. Bipolar current space vector modulation shows better overall performance. A survey of state-of-the-art control methods and commutation strategies is presented. Key issues to be solved urgently are also pointed out. Finally, its application prospects are proposed. Conclusions provide essentially theoretical and practicalsupports to HFLMR.

针对开关电源输入级存在谐波污染、功率因数低、效率低等问题,研究了一种基于高频矩阵变换器的新型开关电源.结合高频矩阵变换器和高频变压器,经二极管全桥整流,输出稳定的直流电压,将3级结构简化为2级结构.采用双极性电流空间矢量调制算法,可保证单位功率因数输入.采用输出电压闭环控制,在输入电压波动、负载变化的情况下均可保证输出电压稳定,显示了其良好的稳定性、动态性以及抗扰性.通过Matlab/Simulink仿真验证了调制和控制策略的正确性.