A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring

Roy, Mohendra; Bose, Sumon Kumar; Kar, Bapi; Gopalakrishnan, P. K.; Basu, Arindam

doi:10.1109/ssci.2018.8628810

Cited by 27 publications

(12 citation statements)

References 32 publications

(28 reference statements)

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“…The latest development of feature extraction methods based on self-encoders provides methods for them, but they are mainly limited to the field of image and audio processing. Based on self-encoders and online sequential learning networks, Roy et al developed an automatic feature extraction method for online status monitoring 33 . Experiments show that the method performs well.…”

Section: Discussionmentioning

confidence: 99%

Research on multi-effect evaporation salt prediction based on feature extraction

Chen

Hua

Zhu

et al. 2020

Sci Rep

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In the multi-effect evaporation salt making process, the smooth operation of the salt making process is crucial. As the salt production process continues, many unstable factors will cause the salt production process not to proceed smoothly. These factors can be discovered in advance by predicting the salt production data, thus, it is of great significance to predict the multi-effect evaporation salt production data. In the process of multi-effect evaporation and salt production, the multiple salt-making devices make the influence between the parameters closer, and the influence of a single parameter on itself is sometimes ductile. Therefore, the data of multi-effect evaporation and salt production have the characteristics of high dimensions, high complexity and temporal information. If the historical salt production data is used for data prediction directly, the prediction model will take a long time and the prediction effect is not good. Thus, how to predict the multi-effect evaporation salt production data is the main research problem of this paper. In view of the above problems, according to the characteristics of multi-effect evaporation salt production data, this paper analyzes and improves the self encoder for feature extraction of multi effect-evaporation salt production data, so as to solve the problem of high dimensions and high complexity of salt production data. On this basis, combined with the time-series information contained in the salt production data, a multi-effect evaporation salt production data prediction model is proposed based on long-term and short-term memory cycle neural network to solve the prediction problem of time-series salt production data. Experiments show that the prediction model can predict and prevent the problems in salt production line in advance. It has a certain theoretical research value and application value in the intelligent production process and production line optimization of salt chemical industry.

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Section: Discussionmentioning

confidence: 99%

Research on multi-effect evaporation salt prediction based on feature extraction

Chen

Hua

Zhu

et al. 2020

Sci Rep

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“…With the introduction of the concept of deep learning, traditional AEs were extended as SAE by adding multiple layers, usually larger than 2 layers, to form the deep structure of an AE. The detailed content of SAE can be found in [19,20]. x  .…”

Section: Autoencoder-based Network For Clusteringmentioning

confidence: 99%

A Hybrid Autoencoder Network for Unsupervised Image Clustering

Chen

Huang

2019

Algorithms

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Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision. While traditional clustering methods, such as k-means or the agglomerative clustering method, have been widely used for the task of clustering, it is difficult for them to handle image data due to having no predefined distance metrics and high dimensionality. Recently, deep unsupervised feature learning methods, such as the autoencoder (AE), have been employed for image clustering with great success. However, each model has its specialty and advantages for image clustering. Hence, we combine three AE-based models—the convolutional autoencoder (CAE), adversarial autoencoder (AAE), and stacked autoencoder (SAE)—to form a hybrid autoencoder (BAE) model for image clustering. The MNIST and CIFAR-10 datasets are used to test the result of the proposed models and compare the results with others. The results of the clustering criteria indicate that the proposed models outperform others in the numerical experiment.

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“…This implementation ensures a real-time, low-cost solution due to the parallel processing capability of FPGA. A stacked autoencoder neural networkbased automated feature extraction method for anomaly detection in online condition monitoring was presented in [20]. The authors showed that the proposed method could achieve very high detection accuracy for determining the bearing health states, and their simple design method is promising for easy hardware implementation.…”

Section: Introductionmentioning

confidence: 99%

FPGA-Embedded Anomaly Detection System for Milling Process

et al. 2021

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The main goal of this work is to design a supervising controller able to detect an anomaly in the milling process and implement the soultion in Field Programmable Gate Array (FPGA) chip. Executing this task, the controller continuously monitors the vibration signal coming from the acceleration sensor, installed on the milling machine, and striving to isolate new vibration patterns which are different from typical patterns recorded for the correct milling process. The detection method relies on determining selected signal features in the frequency domain and applying an auto-associative neural network (AANN) for novelty detection. It has been shown that by exercising the frequency spectrum of the vibration signal, extracting specific features of the signal's spectrum, and using an auto-associative neural network, it is possible to detect anomalies in a milling process with relatively high efficiency. The accuracy, sensitivity, specificity, precision, and false alarm rate are equal to 94.3, 100, 91.2, 88.9, and 8.8 percent. All necessary calculations can be accomplished by the developed single-chip FPGA embedded supervising controller. The controller allows high-speed calculations under low power consumption. It characterizes high reliability and low price compared to typically encountered solutions.

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A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring

Cited by 27 publications

References 32 publications

Research on multi-effect evaporation salt prediction based on feature extraction

Research on multi-effect evaporation salt prediction based on feature extraction

A Hybrid Autoencoder Network for Unsupervised Image Clustering

FPGA-Embedded Anomaly Detection System for Milling Process

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