Unbalanced data with very few samples for special abnormal conditions frequently occur in actual production processes, which can make accurate monitoring of the process state challenging. This paper proposes a multi-modal few-shot learning method (MMFSL) within a fault diagnosis framework for unbalanced data modelling of industrial bearings. MMFSL can handle two modes of data and therefore contains two data generation channels. The first channel deals with time series data and the second deals with images. The two modes of generated data are then evaluated using the MMFSL evaluation modules, IEI (image evaluation index) and TEI (time series evaluation index), to guarantee the quality of the generated data. In addition, the time series data are analyzed in the time-frequency domain to ensure the consistency of the frequency distribution. Original unbalanced and insufficient data samples are expanded so that the fault diagnosis model can be trained adequately. Through experiments using data from Case Western Reserve University(CWRU) and Pardborn University(PU), generated data by using the MMFSL model can increase fault detection accuracy from 80.9% to 97.5%. At the same time, false alarms rate can be reduced significantly from 4.6% to 0.1%. Moreover, fault classification model accuracy can reach 98.1% compared to 95.4% for the original time series dataset, and 99.3% compared with 96.5% for the image data extension. Various visualizations of comparison results are also provided to show the reliability of the MMFSL framework.

For high-precision positioning and navigation systems, the positioning precision of receiver is jeopardized by multipath interference. Multipath suppression methods based on data processing have drawn much attention recently. The critical step of data processing-based method is to estimate multipath parameters. However, most multipath suppression methods falling into the category of data processing-based methods are limited to Gaussian noises, which means the performance of these methods may be degraded in non-Gaussian noises which are encountered quite often in reality. Besides, only static multipath case is studied in most existing literature, which is not sufficient for potential applications since the occurrence and the disappearance of multipath are always changeable along the movement of receiver. To address these problems, the maximum correntropy criterion(MCC) and the generalized maximum correntropy criterion(GMCC) are integrated into the traditional adaptive multipath estimation(AME) algorithm, named as MCC-AME and GMCC-AME, to handle the dynamic multipath estimation problem in non-Gaussian noises. Furthermore, MCC-AME and GMCC-AME are further improved by adopting forgetting factor, named as RMCC-AME and RGMCC-AME, to improve estimation accuracy and reduce time consumption in a recursive way. The four proposed algorithms also address the problems that the formerly proposed entropy-based multipath estimation algorithms are sensitive to the initial estimation and that the assumption of fixed number of multipath is required. The performance of the four proposed algorithms are analyzed and compared. The analytical results show that GMCC-AME outperforms MCC-AME regarding convergent speed, estimation accuracy and robustness, and RGMCC-AME performs even better than RMCC-AME in the same regard.

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NO_x emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NO_x emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NO_x emissions by combining different weight coefficients as needed.

In this paper, a generalized correntropy filter-based fault diagnosis (FD) and tolerant control (FTC) strategy is proposed for stochastic systems with heavy-tailed distributed noises. In order to deal with the non-Gaussian noises involved in the stochastic systems, a generalized correntropy criterion (GCC) is reviewed firstly. Then, based on this criterion, an output feedback controller is designed for the healthy stochastic system to make the system output track the set-point as closely as possible. Considering the sensor fault, a GCC filter-based FD method is established, where residuals are used to detect and isolate the sensor fault. Moreover, when the sensor fault is diagnosed, normal signals are used to reconstruct the system so that the system can maintain run normally. Finally, the wind energy conversion system (WECS) with three types of sensor faults is used to verify the feasibility and efficiency of the proposed FD & FTC method.

Multipath is one of the dominant error sources for high-precision positioning systems, such as global navigation satellite systems (GNSS). The minimum mean square error (MSE) criterion is usually employed for multipath estimation under the assumption of Gaussian noise. For non-Gaussian noise as appeared in most practical applications, alternative solutions are required for multipath estimation. In this work, a multipath estimation algorithm is proposed based on the minimum error entropy (MEE) criterion under Gaussian or non-Gaussian noises. A key advantage of using MEE is that it can minimize the randomness of error signals, however, the shift-invariance characteristics in MEE may lead to a bias of the estimation result. To mitigate such a bias, an improved estimation strategy is proposed by integrating the second-order central moment of the estimation error together with the prior information of multipath parameters as a constraint. The multipath estimation problem is thus formulated as a constrained optimization problem. A modified ε-constrained rank-based differential evolution (εRDE) algorithm is developed to find the optimal solution. The effectiveness of the proposed algorithm, in terms of reducing the multipath estimation error and minimizing the randomness in the error signal, has been examined through case studies with Gaussian and non-Gaussian noises.