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- Fault diagnosis for wind turbine gearboxes by using deep enhanced fusion networkPublication . Pu, Ziqiang; Li, Chuan; Zhang, Shaohui; Bai, YunThe gearbox will directly affect the safety and reliability of the wind turbine, whose failure leads to low processing accuracy and certain economic losses. To address this issue, a deep enhanced fusion network (DEFN) is proposed for the fault diagnosis of the wind turbine gearbox with the experimental vibration data. In the proposed DEFN, three sparse autoencoders are first applied to extract deep features of three-axial vibration signals, respectively. Second, a feature enhancement mapping is developed to minimize the intraclass distance of the deep features in the three-axial vibration. Finally, the fused three-axis features are put into an echo state network for fault classification. The results of the experiment carried out in a wind turbine show that the proposed DEFN has a good fault diagnosis accuracy compared with other peer models.
- Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different VariablesPublication . Bai, Yun; Bezak, Nejc; Zeng, Bo; Li, Chuan; Sapac, Klaudija; Zhang, JinAccurate forecasts of daily runoff are essential for facilitating efficient resource planning and management of a hydrological system. In practice, daily runoff is needed for various practical applications and can be predicted using precipitation and evapotranspiration data. To this end, a long short-term memory (LSTM) under a cascade framework (C-LSTM) approach is proposed for forecasting daily runoff. This C-LSTM model is composed of a 2-level forecasting process. (1) In the first level, an LSTM is established to learn the relationship between the precipitation and evapotranspiration at present and to learn several meteorological variables one day in advance. (2) In the second level, an LSTM is constructed to forecast the daily runoff using the historical and simulated precipitation and evapotranspiration data produced by the first LSTM. Through cascade modeling, the complex features of the numerous targets in the different stages can be sufficiently extracted and learned by multiple models in a single framework. In order to evaluate the performance of the C-LSTM approach, four mesoscale sub-catchments of the Ljubljanica River in Slovenia were investigated. The results indicate that based on the root-mean-square error, the Pearson correlation coefficient, and the Nash-Sutcliffe model efficiency coefficient, the proposed model yields better results than two other tested models, including the normal LSTM and other neural network approaches. Based on the results of this study, we conclude that the LSTM under the cascade architecture is a valuable approach and can be regarded as a promising model for forecasting daily runoff.
- A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality predictionPublication . Bai, Yun; Sun, Zhenzhong; Zeng, Bo; Long, Jianyu; Li, Lin; Valente de Oliveira, JOSÉ; Li, ChuanManufacturing quality prediction model, as an effective measure to monitor the quality in advance, has been developed using various data-driven techniques. However, multi-parameter in multi-stage of the modern manufacturing industry brings about the curse of dimensionality, leading to the difficulties for feature extraction, learning and quality modeling. To address this issue, three dimension reduction techniques are investigated in this paper, i.e., principal component analysis (PCA), locally linear embedding (LLE), and isometric mapping (Isomap). Specifically, the PCA is a linear dimension reduction technique, the LLE is a nonlinear reduction technique with local perspective, and the Isomap is a nonlinear reduction technique from global perspective. After getting the low-dimensional information from the PCA, the LLE, and the Isomap methods respectively, a support vector machine (SVM) is utilized for modeling. To reveal the effectiveness of the dimension reduction techniques and compare the difference of the three dimension reduction techniques, two experimental manufacturing data are collected from a competition about manufacturing quality control in Tianchi Data Lab of China. The comparison experiments indicate that the dimension reduction techniques have capacity for improving the SVM modeling performance indeed, and the Isomap-SVM model with the nonlinear global dimension reduction outperforms all the candidate models in terms of qualitative and quantitative analysis.
- A one-class generative adversarial detection framework for multifunctional fault diagnosesPublication . Pu, Ziqiang; Cabrera, Diego; Bai, Yun; Li, ChuanIn this article, fault diagnosis is of great significance for system health maintenance. For real applications, diagnosis accuracy suffers from unbalanced data patterns, where normal data are usually abundant than anomaly ones, leading to tremendous diagnosis obstacles. Therefore, it is challenging to use only normal data for fault diagnosis under this imbalanced condition. In addition, a single fault diagnosis model can only conduct one fault diagnosis task in most of cases. Accordingly, a one-class generative adversarial detection (OCGAD) framework based on semisupervised learning is proposed to learn one-class latent knowledge for dealing with multiple semisupervised fault diagnosis tasks, i.e., fault detection using only normal knowledge learning, novelty detection from unknown conditional data, and fault classification with unlabeled data. A bi-directional generative adversarial network (Bi-GAN) is first trained with only normal data. A one-class support vector machine is then established using features exacted by Bi-GAN from signals acquired from an attitude sensor for multifunctional fault detection. The presented OCGAD model is validated using an industrial robot with experiments of three fault detection tasks. The results demonstrate that the present model has good performance for dealing with multiple semisupervised diagnosis problems.
- Generative adversarial one-shot diagnosis of transmission faults for industrial robotsPublication . Pu, Ziqiang; Cabrera, Diego; Bai, Yun; Li, ChuanTransmission systems of industrial robots are prone to get failures due to harsh operating environments. Fault diagnosis is of great significance for realizing safe operations for industrial robots. However, it is difficult to obtain faulty data in real applications. To migrate this issue, a generative adversarial one-shot diagnosis (GAOSD) approach is proposed to diagnose robot transmission faults with only one sample per faulty pattern. Signals representing kinematical characteristics were acquired by an attitude sensor. A bidirectional generative adversarial network (Bi-GAN) was then trained using healthy signals. Inspired by way of human thinking, the trained encoder in Bi-GAN was taken out to perform information abstraction for all signals. Finally, the abstracted signals were sent to a random forest for the one-shot diagnosis. The performance of the present technique was evaluated on an industrial robot experimental setup. Experimental results show that the proposed GAOSD has promising performance on the fault diagnosis of robot transmission systems.
- A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledgePublication . Bai, Yun; Xie, Jingjing; Wang, Dongqiang; Zhang, Wanjuan; Li, ChuanManufacturing quality prediction is one of the significant concerns in modern enterprise production management, which provides data support for reliability assessment and parameter optimization, thus improving the intelligent management level of enterprises and helping achieve high-quality products at lower costs. In this paper, an ensemble learning framework using rough knowledge is proposed for manufacturing quality prediction. The proposed model consists of three elements: (1) significant parameters in different production stages are selected based on attribute reduction and decision rule extraction of rough set theory (RS), (2) long short-term memory network (LSTM) is utilized for building the relationship between the significant parameters and manufacturing quality, and (3) the learning performance of the LSTM is reinforced by AdaBoost approach. To estimate the effectiveness of the proposed model, a competition dataset about manufacturing quality control is applied and six models are investigated. The comparison experiments show that the proposed model overwhelms all the comparison models in terms of root-mean-square error, threshold statistics and residuals analysis. In addition, the proposed model has statistically significant difference from all the comparative models. It is recommended from this work that the ensemble learning technique integrating the rough knowledge synchronously improves the sensitivity and regression capacity of the model.
- Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variantsPublication . Bai, Yun; Xie, Jingjing; Liu, Chao; Tao, Ying; Zeng, Bo; Li, ChuanEffective electricity consumption forecasting is extremely significant for enterprises' electricity planning which can provide data support for production decision, thus improving the level of enterprises' clean production. In recent years, recurrent neural network (RNN) and its variants have led to extensive research for time series forecasting. However, the performance and selection of these models in enterprise electricity forecasting have not been reported. With this study, we attempted to back some of these solutions with experimental results. This paper focused on a comparison for daily enterprise electricity consumption forecasting using different RNN models, i.e, standard RNN, long short-term memory-based RNN (LSTM), and gated recurrent unit-based RNN (GRU). To test their regression performance, three Chinese enterprises with different scales of electricity consumption are investigated. The comparison results show that the LSTM and the GRU models are slightly better than that of the RNN in terms of normalized root-mean-square error, mean absolute percentage error and threshold statistic. Moreover, the GRU model with the simplest structure is significantly different from the RNN, but not from LSTM in terms of Friedman testing. Hence the GRU model can be regarded as the first candidate for the enterprise electricity consumption forecasting in the future work.
- Forecasting bus passenger flows by using a clustering-based support vector regression approachPublication . Li, Chuan; Wang, Xiaodan; Cheng, Zhiwei; Bai, YunAs a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.