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autoencoder-Based fault detection using building automation system data

ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： El Mokhtari, Karim McArthur, J. J. Toronto Metropolitan Univ Dept Architectural Sci Toronto ON Canada

This paper explores the application of autoencoder algorithms in Automated Fault Detection (AFD) for Heating, Ventilation, and Air Conditioning (HVAC) systems, specifically focusing on Fan Coil Units (FCUs). The begins by reviewing the current state of Fault Detection and Diagnostics (FDD), emphasizing the limitations the potential of unsupervised learning techniques like autoencoders and transfer learning to fill these gaps. data from a full-scale building case study featuring five Fan Coil Units (FCUs), the research develops and uates autoencoder-based AFD models that models effectively compress multivariate inputs into a reduced space, enabling accurate and efficient fault detection. The paper makes two novel contributions: (1) It introduces a methodology to distinguish between equipment-level and system-level faults;and (2) It demonstrates generalizability of the approach across different types of FCUs through cross-testing and transfer learning. results indicate that autoencoders outperform other dimensionality reduction algorithms and separate predictors in fault detection accuracy and efficiency. The paper concludes by discussing the implications of these findings for future research and practical applications in building management.

关键词： Fault detection autoencoder Building automation system HVAC

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autoencoder-Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

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IEEE ACCESS 2023年 11卷 18868-18886页

作者： Kohne, Jonas Henning, Lars Guhmann, Clemens Tech Univ Berlin Chair Elect Measurement & Diagnost Technol D-10623 Berlin Germany IAV GmbH D-10587 Berlin Germany

This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become increasingly important due to growing availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training Condition-based Maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data, which could be used for training a CbM model, would emerge. Therefore, we introduce an algorithm that uses a recurrent neural network (RNN) based autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems.

关键词： Clustering algorithms Mechatronics Labeling Data models Transient analysis Maintenance engineering Terminology Condition-based maintenance multivariate time-series data change point detection unsupervised clustering autoencoder segmentation subsequence clustering

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autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithms

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JOURNAL OF BIG DATA 2023年第1期10卷 14页

作者： Garouani, Moncef Ahmad, Adeel Bouneffa, Mourad Hamlich, Mohamed Univ Littoral Cote dOpale LISIC Lab Informat Signal & Image Cote Opale UR 4491 F-62100 Calais E France Univ Hassan 2 CCPS Lab ENSAM Casablanca Morocco HESTIM Study & Res Ctr Engn & Management CERIM Casablanca Morocco

The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the configuration of its hyperparameters is among the most complicated tasks while applying ML to new problems. It necessitates well awareness and knowledge of ML algorithms. The algorithm selection problem (ASP) is defined as the process of identifying the algorithm (s) that can deliver top performance for a particular problem, task, and evaluation measure. In this context, meta-learning is one of the approaches to achieve this objective by using prior learning experiences to assist the learning process on unseen problems and tasks. As a data-driven approach, appropriate data characterization is of vital importance for the meta-learning. Nonetheless, the recent literature witness a variety of data characterization techniques including simple, statistical and information theory based measures. However, their quality still needs to be improved. In this paper, a new autoencoder-kNN (AeKNN) based meta-model with built-in latent features extraction is proposed. The approach is aimed to extract new characterizations of the data, with lower dimensionality but more significant and meaningful features. AeKNN internally uses a deep autoencoder as a latent features extractor from a set of existing meta-features induced from the dataset. From this new features vectors the computed distances are more significant, thus providing a way to accurately recommending top-performing pipelines for previously unseen datasets. In an application on a large-scale hyperparameters optimization task for 400 real world datasets with varying schemas as a meta-learning task, we show that AeKNN offers considerable improvements of the classical kNN as well as traditional meta-models in terms of performance.

关键词： Algorithm selection AutoML Meta-learning Meta-features Data representation kNN autoencoder

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autoencoder-like semi-NMF multiple clustering

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INFORMATION SCIENCES 2021年 572卷 331-342页

作者： Yao, Shihong Hu, Chuli Wang, Tao Cui, Xinyou China Univ Geosci Sch Geog & Informat Engn Wuhan 430078 Peoples R China Cent China Normal Univ Sch Educ Informat Technol Wuhan 430079 Peoples R China Army Engn Univ PLA Ordnance NCO Coll Wuhan 430075 Peoples R China

Clustering is performed to partition samples into disjoint groups for facilitating the discovery of hidden patterns in the data. Many real-world applications involve various clustering methods, most of which only produce a single clustering. As a response to this issue, multiple clustering that aims to generate diverse and high-quality clustering, has emerged recently. This study proposes a novel autoencoder-like semi-nonnegative matrix factorization (NMF) multiple clustering (ASNMFMC) model that generates multiple non-redundant, high-quality clustering. The nonnegative property of the semi-NMF is utilized by the algorithm to enforce non-redundancy. Extensive experimental results demonstrate that the ASNMFMC is superior to the existing multiple clustering methods and can explore diverse high-quality clustering. (c) 2021 Elsevier Inc. All rights reserved.

关键词： autoencoder Semi-NMF Multiple clustering

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autoencoder Constrained Clustering With Adaptive Neighbors

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第1期32卷 443-449页

作者： Li, Xuelong Zhang, Rui Wang, Qi Zhang, Hongyuan Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning OPTIMAL Xian 710072 Peoples R China

The conventional subspace clustering method obtains explicit data representation that captures the global structure of data and clusters via the associated subspace. However, due to the limitation of intrinsic linearity and fixed structure, the advantages of prior structure are limited. To address this problem, in this brief, we embed the structured graph learning with adaptive neighbors into the deep autoencoder networks such that an adaptive deep clustering approach, namely, autoencoder constrained clustering with adaptive neighbors (ACC_AN), is developed. The proposed method not only can adaptively investigate the nonlinear structure of data via a parameter-free graph built upon deep features but also can iteratively strengthen the correlations among the deep representations in the learning process. In addition, the local structure of raw data is preserved by minimizing the reconstruction error. Compared to the state-of-the-art works, ACC_AN is the first deep clustering method embedded with the adaptive structured graph learning to update the latent representation of data and structured deep graph simultaneously.

关键词： Kernel Adaptive systems Clustering methods Clustering algorithms Sparse matrices Learning systems Neural networks Adaptive neighbors autoencoder deep clustering parameter-free similarity structured graph

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autoencoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2023年第8期131卷 1995-2014页

作者： Cao, Bing Bi, Zhiwei Hu, Qinghua Zhang, Han Wang, Nannan Gao, Xinbo Shen, Dinggang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Chongqing Univ Posts & Telecommun Chongqing Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China Shanghai Clin Res & Trial Ctr Shanghai Peoples R China

Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods.

关键词： Multimodal collaborative learning Medical image synthesis autoencoder Self-representation

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autoencoder-based anomaly detection of industrial robot arm using stethoscope based internal sound sensor

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第3期34卷 1427-1444页

作者： Yun, Huitaek Kim, Hanjun Jeong, Young Hun Jun, Martin B. G. Purdue Univ Sch Mech Engn W Lafayette IN 47906 USA Purdue Univ Indiana Mfg Competitiveness Ctr MaC W Lafayette IN 47906 USA Kyungpook Natl Univ Sch Mech Engn Daegu 41566 South Korea

Sound and vibration analysis are prominent tools for machine health diagnosis. Especially, neural network (NN) strategies have focused on finding complex and nonlinear relationships between the sensor signal and the machine status to detect machine faults. However, it is difficult to collect enough amount of fault data as much as normal status data for training general NN models. To resolve the issue, this paper proposes the autoencoder-based anomaly detection framework for industrial robot arms using an internal sound sensor. The autoencoder uses signals in the normal state of the robots for training the model. It reconstructs the input signals as output, and anomalous states are found from high reconstruction error. Two stethoscopes were attached to the surface of the robot joint as sensors, and the sounds were recorded by USB microphone attached to the outlet of the stethoscopes. Features were extracted from STFT spectrogram images of the gathered sound, then used to train and test an autoencoder model. The reconstruction errors of the autoencoder were compared to distinguish the abnormal status from normal one. The experimental results suggest that the stethoscopes prevent the interference of noise, and the collected sound signals can be utilized for detecting machine anomalies.

关键词： Sound spectrogram autoencoder Neural network Stethoscope Industrial robot arm

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autoencoder-enabled eye-tracking data analytics for objective assessment of user preference in humanoid robot appearance design

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Li, Fan Chen, Chun-Hsien Liu, Yisi Chang, Danni Cui, Jian Sourina, Olga Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore Fraunhofer Singapore Singapore Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

Humanoid robots have been extensively utilized in service industries to provide information and product delivery through direct interactions with users. As the design of humanoid robot appearance significantly impacts human-robot interactions, it is crucial to assess user preference towards it. Traditional evaluation tools, such as surveys, field observations, and interviews, are often time-consuming and subjective. Therefore, this study aims to develop a novel eye-tracking-based assessment tool to investigate user preference towards humanoid robot appearance design. We analyze the critical factors influencing user preference from two perspectives: the attributes of robot appearance and users' selective attention distribution. Accordingly, we propose an integrated machine learning method, combining an autoencoder neural network with a support vector machine to handle the collected visual data. This method, named ASVM, extracts several novel indicators from the eye-tracking data via an unsupervised autoencoder neural network and manual entropy analysis. The proposed ASVM achieves an accuracy of 91%, outperforming other classical machine learning methods, including decision tree, naive Bayes, and support vector machine. ASVM can objectively assess user preference towards humanoid robot appearance design with high time resolution. Furthermore, it can enhance humanoid robot design by revealing the visual attention distribution in assessing robot appearance.

关键词： autoencoder Eye -tracking Robot appearance design User preference

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autoencoder Application for Anomaly Detection in Power Consumption of Lighting Systems

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IEEE ACCESS 2023年 11卷 124150-124162页

作者： Smialkowski, Tomasz Czyzewski, Andrzej Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Detecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning method using the autoencoder model with LSTM and 1D Convolutional networks for various configurations and training periods. The evaluation of the algorithms was carried out based on real data from an extensive lighting control system. A practical approach was proposed using real-time, unsupervised algorithms employing limited computing resources that can be implemented in industrial devices designed to control intelligent city lighting. An anomaly detection algorithm based on classic LSTM networks, single-layer and multi-layer, was used for comparison purposes. Error matrix calculus was used to assess the quality of the models. It was shown that based on the autoencoder method, it is possible to construct an algorithm that correctly detects anomalies in power measurements of lighting systems, and it is possible to build a model so that the algorithm works correctly regardless of the season of the year.

关键词： Anomaly detection autoencoder machine learning road lighting systems smart city smartmeters

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autoencoder-based drug synergy framework for malignant diseases

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2024年 113卷 108273页

作者： Rani, Pooja Dutta, Kamlesh Kumar, Vijay Natl Inst Technol Comp Sci & Engn Dept Hamirpur 177005 HP India Dr BR Ambedkar Natl Inst Technol Jalandhar Informat Technol Dept Jalandhar 144027 Punjab India

Drug combination emerges as a viable option for the treatment of malignant diseases. Drug combination outperforms monotherapy by improving therapeutic efficacy, reducing toxicity, and overcoming drug resistance. To find viable drug combinations it is difficult to traverse empirically because of enormous combinational space. Machine learning and deep learning approaches are used to uncover novel synergistic drug combinations in enormous combinational space. Here, AESyn, a novel autoencoder-based drug synergy framework for malignant diseases using a bag of words encoding is proposed. The bag of word encoding technique is used to extract drug-targeted genes. The framework utilized screening data from NCI-ALMANAC, and O'Neil datasets. autoencoders take drug embeddings with drug-targeted genes as input for processing. The autoencoder in the proposed framework is used to extract drug features. The proposed framework is evaluated on classification and regression metrics. The performance of the proposed framework is compared with existing methods of drug synergy. According to the findings, the proposed framework achieved high performance with an accuracy of 95%, AUROC of 94.2%, and MAPE of 7.2. The autoencoder-based framework for malignant diseases using an encoding technique provides a stable, order-independent drug synergy prediction.

关键词： Embeddings Drug synergy autoencoder Deep learning Encoding Neural network Malignant diseases

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