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An intelligent grinding burn detection system based on two-stage feature selection and stacked sparse autoencoder

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第5-8期103卷 2837-2847页

作者： Guo, Weicheng Li, Beizhi Shen, Shouguo Zhou, Qinzhi Donghua Univ Coll Mech Engn Shanghai 201600 Peoples R China

Grinding burn is a typical quality defect that negatively affects microstructures and mechanical properties of parts. In this article, an intelligent grinding burn detection system that consists of processing of grinding signals, extraction of signal features, selection of optimal feature subset, and classification of grinding burn is presented. During the grinding of maraging steel 3J33, physical signals are collected by a dynamometer, accelerometer, and acoustic emission sensor. A large amount of preliminary features in time domain and frequency domain are extracted after wavelet packet decomposition and ensemble empirical mode decomposition. To reduce the dimensionality of feature space and increase relevancy of feature to grinding burn, a two-stage feature selection strategy combining filter and wrapper method is proposed. The filter ranks individual features by ReliefF, while the wrapper evaluates different feature subsets according to the model performance. A deep learning network, stacked sparse autoencoder (SSAE), is adopted to develop the classification model of grinding burn and is compared with other machine learning models. The results demonstrate that the two-stage feature selection method is able to provide the optimal feature subset for the model and SSAE model obtains the best training and testing classification rate of grinding burn with least features among all comparative models.

关键词： Grinding burn Feature selection ReliefF Deep learning sparse autoencoder

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KSVM-Based Fast Intra Mode Prediction in HEVC Using Statistical Features and sparse autoencoder

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IEEE ACCESS 2024年 12卷 48846-48852页

作者： Nair, Preethi S. Nair, Madhu S. Univ Kerala Dept Comp Sci Thiruvananthapuram 695581 Kerala India Cochin Univ Sci & Technol Dept Comp Sci Artificial Intelligence & Comp Vis Lab Kochi 682022 Kerala India

High Efficiency Video Coding (HEVC) is designed to deliver a video communication with better quality at reduced bit rate. For intra coding, HEVC employs an effective hierarchical quad tree partitioning and an exhaustive optimal mode search which increases the time complexity. Aiming this issue, we propose a Support Vector Machine (SVM)-based method to effectively predict the intra mode. Compared to the standard HEVC encoder HM-15.0, the proposed method could reduce 57.6% of encoding time at a bit-rate penalty of 3.3% at an average PSNR decline of only around 0.09 dB.

关键词： HEVC intra mode prediction RMD SVM Hu moments sparse autoencoder

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Network intrusion detection using sparse autoencoder with swish-PReLU activation Model

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-13页

作者： Kannari, Phanindra Reddy Shariff, Noorullah C. Biradar, Rajkumar L. Rao Bahadur Y Mahabaleswarappa Engn Coll Dept Elect & Commun Engn Ballari India SECAB Inst Engn & Technol Dept Elect & Commun Engn Vijayapur India G Narayanamma Inst Technol & Sci Dept Elect & Telemat Engn Hyderabad India

In computer networks, the massive amount of data increases the challenges for intrusion detection systems, because of its high dimensionality. To overcome this problem, a four-phase system is developed for intrusion detection based on encoding techniques and deep learning. In the first phase, input data are collected from NSL-KDD, Canadian Institute for Cybersecurity-Intrusion Detection System 2017 (CIC-IDS2017), and Aegean Wi-Fi Intrusion Dataset (AWID). The collected data are converted into the machine-readable form by using label and one hot encoding technique that reduces the human intervention process and increases the accuracy of data classification. Next, the top percentile and recursive features are selected utilizing second percentile methodology and recursive feature elimination. The undertaken feature selection techniques;second percentile method and recursive feature elimination selects the relevant or active features from the pre-processed data that effectively diminishes the computational time and complexity of the proposed model. In the final phase, sparse autoencoder with swish-PReLU activation model is proposed to classify the normal and traffic types in the NSL-KDD, CIC-IDS2017, and AWID datasets. In the experimental phase, the proposed sparse autoencoder with swish-PReLU activation model achieved effective performance in intrusion detection in light of false alarm rate, detection rate and classification accuracy. From the experimental result, the proposed model showed the maximum of 4.77% improvement in classification accuracy compared to the existing models;correlation-based feature selection with bat algorithm, artificial neural network, chi-square and information gain-random tree, and sequential Search-Bayesian network.

关键词： Intrusion detection Label encoder One hot encoder Recursive feature elimination Second percentile method sparse autoencoder

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Learning a referenceless stereopair quality engine with deep nonnegativity constrained sparse autoencoder

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PATTERN RECOGNITION 2018年 76卷 242-255页

作者： Jiang, Qiuping Shao, Feng Lin, Weisi Jiang, Gangyi Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Zhejiang Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

This paper proposes a no-reference (NR)/referenceless quality evaluation method for stereoscopic three-dimensional (S3D) images based on deep nonnegativity constrained sparse autoencoder (DNCSAE). To address the quality issue of stereopairs whose perceived quality is not only determined by the individual left and right image qualities but also their interactions, a three-column DNCSAE framework is customized with individual DNCSAE module coping with the left image, the right image, and the cyclopean image, respectively. In the proposed framework, each individual DNCSAE module shares the same network architecture consisting of multiple stacked NCSAE layers and one Softmax regression layer at the end. The contribution of our model is that hierarchical feature evolution and nonlinear feature mapping are jointly optimized in a unified and perceptual-aware deep network (DNCSAE), which well resembles several important visual properties, i.e., hierarchy,, sparsity, and non-negativity. To be more specific, for each DNCSAE, by taking a set of handcrafted natural scene statistic (NSS) features as inputs in the visible layer, the features in hidden layers are successively evolved to deeper levels producing increasingly discriminative quality-aware features (QAFs). Then, QAFs in the last NCSAE layer are summarized to their corresponding quality score by Softmax regression. Finally, three individual yet complementary quality scores estimated by each DNCSAE model are combined based on a Bayesian framework to obtain an overall 3D quality score. Experiments on three benchmark databases demonstrate the superiority of our method in terms of both prediction accuracy and generalization capability. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Image quality assessment No-reference/referenceless Stereoscopic 3D image Deep learning Nonnegativity constrained sparse autoencoder

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An intelligent fault diagnosis framework based on piecewise aggregate approximation, statistical moments, and sparse autoencoder

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2023年第4期237卷 686-702页

作者： Prasad, Akash Dantreliya, Chirag Chande, Mayank Chauhan, Vedant Rai, Akhand Ahmedabad Univ Sch Engn & Appl Sci Commerce Six Rd Ahmadabad 380009 Gujarat India

Rotating machines (RMs) have vast applicability in almost all the industries in mechanical domain. Rolling element bearings (RBs) are the key elements to ensure that the RMs perform efficiently. RBs are highly prone to wear and tear which could have devastating consequences such as massive economic losses and accidents. In the past, many time-domain based condition-indicators such as root mean square (RMS), skewness and kurtosis, etc. have been proposed by researchers to diagnose the bearing faults and prevent RM failures. However, they are often insensitive to early stage faults, affected by outliers and possess poor degradation tracking characteristics. To overcome these shortcomings, this paper proposes a novel statistical feature extraction technique called as multiscale statistical moment (MSM) analysis, in combination with sparse autoencoder to detect the incipient faults as well as track the progression of wear. Firstly, the vibration signal are acquired from the bearings to be monitored. Secondly, the MSM features are extracted from the vibration signals. Thirdly, the MSM features corresponding to normal conditions are utilized to train the sparse autoencoder network. Fourthly, the MSM features corresponding to test conditions are supplied to the pre-trained sparse autoencoder model. The MSM technique offers the advantage that it extracts the fault properties contained in multiple time-scales of the vibration signals instead of a single time-scale only. Finally, the dissimilarity between the actual and predicted output is measured to obtain the bearing health indicator (BHI). The experimental results demonstrate that the suggested BHI detects the faults at early stages, possess better sensitivity and trends the bearing degradation more accurately as compared to the traditional techniques such as RMS, kurtosis, and BHI obtained with statistical moment features at single-scale only.

关键词： Rotating machinery fault diagnosis piecewise aggregate approximation multiscale statistical moments sparse autoencoder

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Stacked sparse autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

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IEEE ACCESS 2017年 5卷 15066-15079页

作者： Qi, Yumei Shen, Changqing Wang, Dong Shi, Juanjuan Jiang, Xingxing Zhu, Zhongkui Soochow Univ Sch Urban Rail Transportat Suzhou 215131 Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

As a breakthrough in the field of machine fault diagnosis, deep learning has great potential to extract more abstract and discriminative features automatically without much prior knowledge compared with other methods, such as the signal processing and analysis-based methods and machine learning methods with shallow architectures. One of the most important aspects in measuring the extracted features is whether they can explore more information of the inputs and avoid redundancy to be representative. Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper. The penalty term of the SAE can help mine essential information and avoid redundancy. To help the constructed diagnosis network further mine more abstract and representative high-level features, the collected non-stationary and transient signals are preprocessed with ensemble empirical mode decomposition and autoregressive (AR) models to obtain AR parameters, which are extracted based on the intrinsic mode functions (IMFs) and regarded as the low-level features for the inputs of the proposed diagnosis network. Only the first four IMFs are considered, because fault information is mainly refiected in high-frequency IMFs. Experiments and comparisons are complemented to validate the superiority of the presented diagnosis network. Results fully demonstrate that the stacked SAE-based diagnosis method can extract more discriminative high-level features and has a better performance in rotating machinery fault diagnosis compared with the traditional machine learning methods with shallow architectures.

关键词： sparse autoencoder ensemble empirical mode decomposition autoregressive model fault diagnosis

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Adaptive slow feature analysis-sparse autoencoder based fault detection for time-varying processes

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2023年 142卷

作者： Tan, Shuai Zhou, Xinjin Shi, Hongbo Song, Bing East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Background: Fault detection and diagnosis technology is of great significance for practical industrial processes. Industrial process characteristics change with time due to various reasons such as changing working conditions. This will cause false alarm or missing alarm of process monitoring. Methods: In this paper, an adaptive slow feature analysis (SFA) - sparse autoencoder (SAE) algorithm is proposed to establish an adaptive model for time-varying process monitoring. Model update index is built based on timevarying characteristics extracted using SFA model. Process monitoring index is built based on sparse characteristics extracted using SAE model. Through online adaptive update strategy, updated monitoring model is realized to adapt to the time-varying characteristics of the process. Significant findings: The proposed algorithm has good performance on penicillin fermentation process data set and can realize the task of adaptive process monitoring.

关键词： Adaptive process monitoring Slow feature analysis sparse autoencoder Time-varying process

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Self-adaptive weighted synthesised local directional pattern integrating with sparse autoencoder for expression recognition based on improved multiple kernel learning strategy

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IET COMPUTER VISION 2020年第3期14卷 73-83页

作者： Du, Lingshuang Wu, Yongbo Hu, Haifeng Wang, Weixuan Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China

This study presents a novel method for solving facial expression recognition (FER) tasks which uses a self-adaptive weighted synthesised local directional pattern (SW-SLDP) descriptor integrating sparse autoencoder (SA) features based on improved multiple kernel learning (IMKL) strategy. The authors' work includes three parts. Firstly, the authors propose a novel SW-SLDP feature descriptor which divides the facial images into patches and extracts sub-block features synthetically according to both distribution information and directional intensity contrast. Then self-adaptive weights are assigned to each sub-block feature according to the projection error between the expressional image and neutral image of each patch, which can highlight such areas containing more expressional texture information. Secondly, to extract a discriminative high-level feature, they introduce SA for feature representation, which extracts the hidden layer representation including more comprehensive information. Finally, to combine the above two kinds of features, an IMKL strategy is developed by effectively integrating both soft margin learning and intrinsic local constraints, which is robust to noisy condition and thus improve the classification performance. Extensive experimental results indicate their model can achieve competitive or even better performance with existing representative FER methods.

关键词： image representation feature extraction image classification image texture face recognition learning (artificial intelligence) self-adaptive weighted synthesised local directional pattern sparse autoencoder improved multiple kernel learning strategy facial expression recognition tasks novel SW-SLDP feature descriptor facial images extracts sub-block features directional intensity contrast self-adaptive weights sub-block feature expressional image expressional texture information discriminative high-level feature feature representation IMKL strategy high-level features

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Fault Diagnosis of Rotating Machinery Using Denoising-Integrated sparse autoencoder Based Health State Classification

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IEEE ACCESS 2023年 11卷 15174-15183页

作者： Yang, Jing Xie, Guo Yang, Yanxi Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China

The diagnostic study on single-fault with distinguishing features based on monitoring data analysis is mature and fruitful in recent years. However, the early fault signals collected by practical monitoring systems often possess the following characteristics: 1) Fairly weak signal strength;2) Submerged in powerful background noise;3) Coupling of different fault data. These features not only increase the diagnostic difficulty, but also make the existing methods hardly to get the desired results. Consequently, the early compound faults diagnosis commonly in industrial systems is still a thorny and urgent problem. Therefore, in order to solve this problem and provide technical support for the practical industrial machinery fault diagnosis, a denoising-integrated sparse autoencoder (DISAE) model for early compound faults diagnosis is proposed in this paper. The innovation points of this study mainly include: 1) A feature-enhanced and denoising solution based on fault sensitivity degree (FSD) is designed, and the reconstructed diagnostic signals are acquired. 2) A disassociation framework is formulated, and the data coupling is solved. 3) A weight constraint term of SAE is constructed to improve the effectiveness and diversity of feature learning. 4) An adaptive loss function and a DISAE model is formed, and the early compound faults diagnosis is achieved. Finally, different trials and comparison results display the effectiveness and superiority of the designed DISAE based scheme.

关键词： Compounds Fault diagnosis Vibrations Monitoring Noise reduction Adaptation models Ensemble learning Encoding Early compound faults EEMD (Ensemble Empirical Mode Decomposition) fault diagnosis fault sensitivity degree signal denoising sparse autoencoder

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An Improved sparse autoencoder and Multilevel Denoising Strategy for Diagnosing Early Multiple Intermittent Faults

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第2期52卷 869-880页

作者： Xie, Guo Yang, Jing Yang, Yanxi Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China

Currently, the diagnosis of single fault marked by distinct characteristics has received mass concern, and the related research achievements are remarkable. However, the diagnosis of early multiple intermittent (EMI) faults commonly existing in industrial systems is still an intractable problem, owing to: 1) the scarcity of fault data and the faintness of features and 2) the mutual interaction of components and the coupling of fault characteristics. In order to address the problem, this article proposes an improved sparse autoencoder and multilevel denoising strategy (MDS-ISAE) for diagnosing EMI faults. First, a relational constraint term is constructed to mitigate the effect of data correlation. Second, a multilevel denoising strategy is designed to enhance the robustness of AE feature learning with strong background noise. Third, a resizable resampling strategy is planned to tackle the skew distribution of diagnosis data. Based on the above strategies, an MDS-ISAE-based model is constructed to achieve EMI faults diagnosis. Further, the evaluation criteria of diagnosis performance are proposed and the applicability of the method is tested. Finally, the effectiveness and practicability of the proposed method are verified by artificial damage and real fault experiments, respectively.

关键词： Electromagnetic interference Fault diagnosis Feature extraction Gears Noise reduction Vibrations Noise measurement Early multiple intermittent (EMI) faults fault diagnosis multilevel denoising relational constraint term resizable resampling sparse autoencoder

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