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Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots

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JOURNAL OF MANUFACTURING SYSTEMS 2021年 61卷 736-745页

作者： Long, Jianyu Mou, Jindong Zhang, Liangwei Zhang, Shaohui Li, Chuan Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China

Monitoring the transmission status of multi-joint industrial robots is very important for the accuracy of the robot motion. The fault diagnosis information is an indispensable basis for the collaborative maintenance of the robots in industry 4.0. In this paper, an attitude data-based intelligent fault diagnosis approach is proposed for multi-joint industrial robots. Based on the analysis of the transmission mechanism, the attitude change of the last joint is employed to reflect the transmission fault of robot components. An economical data acquisition strategy is performed by only installing one attitude sensor on the last joint of the multi-joint robot. Considering the characteristics of attitude data, a hybrid sparse auto-encoder (SAE) and support vector machine (SVM) approach, namely SAE-SVM, is subsequently presented to construct an intelligent fault diagnosis model by learning from the attitude dataset with multiple fault information. Experimental results show that the proposed fault diagnosis approach has promising performance in identifying different faults related to the reducer of a 6-axial multi-joint industrial robot accurately and reliably.

关键词： Fault diagnosis Multi-joint industrial robot sparse auto-encoder Support vector machine Attitude data

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Intelligent classification model of land resource use using deep learning in remote sensing images

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ECOLOGICAL MODELLING 2023年 475卷

作者： Liao, Qingtao Chongqing Ind Polytech Coll Inst Elect & Internet things Engn Chongqing 401120 Peoples R China

Accurate and effective remote sensing image classification and analysis methods can provide reliable support for the sustainable development of land resources, but the current methods can't support the precise processing of complex images. To solve this problem, this paper proposes a new classification and analysis method of remote sensing image land resources based on deep learning. Firstly, this method first uses ENVI software to realize image preprocessing on the collected data set, and realizes reliable data set;At the same time, based on the sparse auto-encoder network model, the sample features of the image data set are deeply mined to make a better reference for category determination;And based on ELM classifier network, convolution layer and sub sampling layer are alternately connected to extract the depth feature of remote sensing image, which can greatly reduce the number of network parameters and improve the computational. Finally, the actual collected data set is used for simulation verification, and the experimental results show that the accuracy of complex image analysis reaches 98.15%, which shows excellent performance of image feature classification and analysis.

关键词： Remote sensing image Classification of land resources Ecological environment sparse auto-encoder ELM classifier Data preprocessing

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Novelty detection on a laboratory benchmark slender structure using an unsupervised deep learning algorithm

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2023年第9期20卷

作者： Finotti, Rafaelle Piazzaroli da Silva, Clayton Felicio Oliveira, Pedro Henrique Eveling Barbosa, Flavio de Souza Cury, Alexandre Abrahao Silva, Rafael Cerqueira Univ Fed Juiz De Fora Programa Posgrad Engn Civil Juiz De Fora MG Brazil Univ Brasilia Programa Posgrad Geotecn Brasilia DF Brazil Univ Fed Juiz De Fora Programa Posgrad Modelagem Computac Juiz De Fora MG Brazil

The process that involves the continuous monitoring and analysis of a structure's behavior and performance is known as Structural Health Monitoring (SHM). SHM typically involves the use of sensors and other monitoring devices to collect data, such as displacements, strains, accelerations, among others. These data are analyzed using advanced algorithms and machine learning techniques to identify any signs of abnormal behavior or deterioration. This paper presents a numerical and experimental study of a slender frame subjected to five levels of structural changes under impact loading. The dynamic responses were recorded by four accelerometers and used to build models based on sparse auto -encoders (SAE). Such models can identify each of the five structural states in an unsupervised way. A new strategy to define the hyperparameters of the SAE is presented, which proved to be adequate in the experiments conducted. Finally, the experimental data set is made available to the scientific community to serve as a benchmark for validating SHM methodologies to identify structural changes from dynamic measurements.

关键词： Structural Health Monitoring Damage Detection Vibration Signals Unsupervised Deep Learning sparse auto-encoder

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Cost-Sensitive and sparse Ladder Network for Software Defect Prediction

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2020年第5期E103D卷 1177-1180页

作者： Sun, Jing Ji, Yi-mu Liu, Shangdong Wu, Fei Nanjing Univ Posts & Telecommun NJUPT Coll Comp Nanjing 210003 Peoples R China NJUPT Coll Automat Nanjing 210003 Peoples R China

Software defect prediction (SDP) plays a vital role in allocating testing resources reasonably and ensuring software quality. When there are not enough labeled historical modules, considerable semi-supervised SDP methods have been proposed, and these methods utilize limited labeled modules and abundant unlabeled modules simultaneously. Nevertheless, most of them make use of traditional features rather than the powerful deep feature representations. Besides, the cost of the misclassification of the defective modules is higher than that of defect-free ones, and the number of the defective modules for training is small. Taking the above issues into account, we propose a cost-sensitive and sparse ladder network (CSLN) for SDP. We firstly introduce the semi-supervised ladder network to extract the deep feature representations. Besides, we introduce the cost-sensitive learning to set different misclassification costs for defective-prone and defect-free-prone instances to alleviate the class imbalance problem. A sparse constraint is added on the hidden nodes in ladder network when the number of hidden nodes is large, which enables the model to find robust structures of the data. Extensive experiments on the AEEEM dataset show that the CSLN outperforms several state-of-the-art semi-supervised SDP methods.

关键词： semi-supervised learning software defect prediction ladder network cost-sensitive learning sparse auto-encoder

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Fault diagnosis for rotor based on multi-sensor information and progressive strategies

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第6期34卷 065111-065111页

作者： Hu, Xiao Wei, Daohong Liu, Dong Xiao, Zhihuai Xia, Xiangchen Malik, O. P. North China Univ Water Resources & Elect Power Coll Energy & Power Engn Zhengzhou 450045 Peoples R China Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China China Natl Water Resources& Elect Power Mat & Equi Beijing 100040 Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

Fault diagnosis is an effective tool to ensure safe operation of machinery and avoid serious accidents. As most currently used fault diagnosis methods usually employ mapping relationship established by training samples and their labels to achieve classification of testing samples, it is difficult for them to achieve fault diagnosis under the condition of incomplete training sample types. In addition, previous studies usually focus on feature extraction of single-channel vibration signal, which cannot get complete fault feature information. To solve the above problems, a progressive fault diagnosis method is investigated in this paper. First, the preliminary fault detection for the rotor is performed by studying reconstruction error of a sparse auto-encoder. Second, if a fault exists in the rotor, the outlier detection is implemented by the support vector data description method. Finally, if there are no outlier samples, the well -trained support vector machine is used to confirm the type of fault samples and complete the diagnosis. The performance of the proposed method was verified using the data obtained from a rotor laboratory bench. The types of rotor states investigated include normal, contact-rubbing, unbalance and misalignment. The experimental results verify the effectiveness and superiority of the proposed method in reducing the incidents of fault omission and fault misunderstanding.

关键词： rotor fault diagnosis sparse auto-encoder support vector data description progressive strategies

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Lithium batteries State of Health estimation based on optimized SAE-Transformer network 23

Lithium batteries State of Health estimation based on optimi...

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Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering

作者： Liefa Liao Haoqiang Ding E Tang Jiangxi University of Science and Technology China

ISBN: (纸本)9798400708831

Accurate prediction of the State of Health (SOH) of lithium batteries is of paramount importance in mitigating the risks associated with battery usage. The emergence of artificial intelligence and deep learning has introduced innovative data-driven methodologies for forecasting the SOH of lithium-ion batteries. In this paper, we present a deep learning framework that leverages sparse autoencoders (SAE) and Transformer networks for SOH prediction in lithium-ion batteries. Prior to model training, we employ the Particle Swarm Optimization (PSO) algorithm to fine-tune the model parameters. Subsequently, the sparse autoencoder is utilized to extract and integrate degradation features from lithium-ion batteries. These amalgamated features serve as inputs, while battery capacity serves as the output, thereby establishing a comprehensive predictive model. To validate our methodology, we conducted experiments using a battery capacity dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland. We benchmarked the performance of our method against existing algorithms, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results demonstrate the superior predictive accuracy of our proposed approach, with a Mean Absolute Error (MAE) of 0.0186 and a Root Mean Square Error (RMSE) of 0.0268, affirming the effectiveness of our research approach.

关键词： Lithium-ion battery sparse auto-encoder State of health Transformer

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A stacked auto-encoder with scaled conjugate gradient algorithm for Malayalam ASR

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International Journal of Information Technology (Singapore) 2021年第4期13卷 1473-1479页

作者： Pillai, Leena G. Mubarak, D. Muhammad Noorul Department of Computer Science University of Kerala Thiruvananthapuram India

automatic speech recognition (ASR) is entitled to automate natural speech perception and the processing mechanism through analysis in the linguistic and acoustic features of the speech signal. ASR for children is highly challenging due to their developing physical aspects and rapidly changing articulation features. Therefore, ASR for children is still at its infant level. In this work, a stacked multilayer auto-encoder (AE) network is designed for ASR of the Malayalam vowel, articulated by children in the age group of five to ten. The proposed network structured with an unsupervised pre-training followed by supervised training. The pre-training coupled with two layers of sparse auto-encoders and scaled conjugate gradient (SCG) algorithm used for back-propagation. The auto-encoders are used to pre-train the network in an unsupervised (self- supervised) manner with 40,500 features that include Mel frequency cepstral coefficients (MFCC) and its derivatives, spectrogram formants and zero crossing rate (ZCR). In the softmax layer, the pre-trained network retrained in a supervised manner with bottleneck features. Fine-tuning has been applied in the trained network to enhance its performance. The unsupervised and supervised layers are stacked together to form a comprehensive network. The designed network has shown an average accuracy of 97% in training and 89.5% accuracy in the test data-set. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： automatic speech recognition (ASR) Deep neural network Mel frequency cepstral coefficients (MFCC) Scaled conjugate gradient (SCG) sparse auto-encoder Spectrogram Unsupervised learning Zero crossing rate (ZCR)

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An intelligent fault diagnosis method for an electromechanical actuator based on sparse feature and long short-term network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第9期32卷 095102-095102页

作者： Yang, Jing Guo, Yingqing Zhao, Wanli Northwestern Polytech Univ Sch Power & Energy Xian 710072 Peoples R China

Electromechanical actuators (EMAs), as the new generation of actuators, have an important impact on the safety of aircraft. With the development of measurement technology, a large amount of data provides a broad prospect for the data-based fault diagnosis method. However, the existence of redundant data increases the burden of software and hardware. Therefore, a semi-supervised sparse auto-encoder (SSAE) is employed to prune observed data based on sparsity analysis. Moreover, temporal and spatial relationships are explored by a multi-channel long short-term network to build a time series model, so as to perform fault detection and isolation based on the difference between its estimated and observed values. Due to its sparse feature extraction capability, the SSAE can improve the fault isolation accuracy while pruning observed data. Verification results confirm that the proposed method can effectively diagnose EMA faults.

关键词： electro-mechanical actuator sparse auto-encoder long-short memory network sparse feature fault diagnosis

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Transfer fault diagnosis based on local maximum mean difference and K-means

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第PartA期172卷

作者： Zhang, Xue-yang He, Lang Wang, Xiao-kang Wang, Jian-qiang Cheng, Peng-fei Shenzhen Univ Inst Big Data Intelligent Management & Decis Makin Coll Management Shenzhen 518060 Peoples R China Cent South Univ Sch Business Changsha 410083 Peoples R China Hunan Univ Sci & Technol Hunan Engn Res Ctr Intelligent Decis Making & Big Xiangtan 411201 Peoples R China

Existing feature-based transfer learning methods have achieved great performance in the transfer fault diagnosis with unlabeled data. While most of them are global alignment methods based on maximum mean difference (MMD), which ignore the differences between different faults and pay little attention to the structural infor-mation in the unlabeled target samples. This paper proposes a transfer sparse auto-encoder (SAE) based on local maximum mean difference (LMMD) and K-means to solve the above problems. Firstly, we build a deep network based on SAE and LMMD for learning a common latent feature space where source and target subdomains are aligned. Subsequently, to fully explore the target domain information, we put forward the K-means-based method which can obtain final diagnosis results by synthesizing the source and target domain information in the latent feature space. Lastly, a case study is conducted to verify the robustness and effectiveness of the proposed methods. The experimental result demonstrates that the proposed methods outperform the MMD-based methods in the transfer fault diagnosis problem.

关键词： Fault diagnosis K-means Local maximum mean difference sparse auto-encoder Transfer learning

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Intrusion Detection Method based on Deep Learning 19

Intrusion Detection Method based on Deep Learning

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19th IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA)

作者： Wu, Zhijun Liang, Cheng Li, Yuqi Civil Aviat Univ China Sch Safety Sci & Technol Tianjin Peoples R China Civil Aviat Univ China Sch Elect & Informat & Automat Tianjin Peoples R China

ISBN: (纸本)9781665435741

In response to the issue that the classification accuracy of small proportion attack data is low due to the imbalance of classification in existing intrusion detection methods, this paper mainly focuses on two aspects. First, we put forward a hybrid intrusion detection method founded on AdaBoost algorithm and convolutional neural network (CNN). It utilizes CNN to extract network traffic data, and utilizes AdaBoost algorithm to classify network attack data;Second, the improved sparse autoencoder is combined with softmax classifier to increase the classification preciseness of the model by continuously adjusting the parameters of the sparse autoencoder. This method uses KDD99 data set to test the code in pycharm environment. The test results show that this method has advantages over the existing methods in the detection rate (DR) and false positive rate (FPR) of small proportion attack data. This method is suitable for the analysis of continuous and classified attack data, improves the detection effect of imbalanced data classification, and solves the defects of existing intrusion detection models with large classification error and slow calculation speed.

关键词： Intrusion detection deep learning CNN AdaBoost sparse auto-encoder KDD99 dataset component

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