Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control

Liu, Yu; Pang, Zhibo; Karlsson, Magnus; Gong, Shaofang

doi:10.1016/j.buildenv.2020.107212

Cited by 74 publications

(41 citation statements)

References 52 publications

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“…In sliding window dataset and overlapped sliding window dataset, the observation window moves LAG steps and one step ahead, respectively. In both cases, the data points in the observation windows are used as target [31].…”

Section: Methodsmentioning

confidence: 99%

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Enable the landing of Internet of Things: a holistic approach

Liu

2021

Linköping Studies in Science and Technology. Dissertations

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for their valuable input and support to my study, research, and life. Thanks to Associate Professor Vangelis Angelakis who showed me another methodology to conduct research. Special thanks to Professor Michael Hörnquist who genuinely helped me a lot to stay and to develop.Moreover, I would like to show my appreciation to my colleagues and partners within the projects I was involved, Ola Weister, Kahin Akram Hasv san, Professor Anita Lloyd Spetz, Docent Donatella Puglisi, Tomas Uusitalo, Åke Johansson, and Patrik Arven, for their trust and support. I sincerely thank the company of my dear colleagues and friends from the Chinese students and scholars association and in my life. I do enjoy the friendship and fun we had.Vinnova, Swedish Environmental Protection Agency, and Norrköping Fund for Research and Development in Sweden are acknowledged for financial support of the Ph.D. study.Last but not least, I want to express my deepest gratitude to my family, my wife Xinchen Wang, my parents and my parents in law. Without your firm support behind me, I could not have gone so far.

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Section: Methodsmentioning

confidence: 99%

“…Grey cells: contextual anomaly detection results.) [31] the performance evaluation are prepared according to Figure 6.1, catering to the needs of prediction and pattern recognition models.…”

Section: Experimental Designmentioning

confidence: 99%

Enable the landing of Internet of Things: a holistic approach

Liu

2021

Linköping Studies in Science and Technology. Dissertations

Self Cite

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“…The detailed parameter configuration is shown in Table 2. In this experiment, three machine learning models extracted from this study [50], which are used to detect anomalies in an indoor environment so as to realize predictive maintenance with an active plant wall system, are utilized for the benchmarking. Specifically, a regression model is implemented using the Scikit-learn library, an autoencoder and a long short-term memory-encoder decoder (LSTM-ED) neural network model are implemented with Tensorflow.…”

Section: Experiments Implementation 1) Experiments 1 -Full Stack Round mentioning

confidence: 99%

“…Specifically, a regression model is implemented using the Scikit-learn library, an autoencoder and a long short-term memory-encoder decoder (LSTM-ED) neural network model are implemented with Tensorflow. For model implementation details refer to [50].…”

Section: Experiments Implementation 1) Experiments 1 -Full Stack Round mentioning

confidence: 99%

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Liu

Lan

Pang

et al. 2021

IEEE Open J. Ind. Electron. Soc.

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Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance.

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“…However, if anomalies appear during the process, representing abnormal deviations from the standard state, it is necessary to also use methods and techniques from the field of machine learning, such as support vector machine, artificial neural networks, or k-nearest neighbor. Their use is described by the authors in [ 21 , 22 , 23 , 24 , 25 ]. The results of the performed analyzes can be used for early warning of anomaly [ 26 ], classification [ 27 ], but also for the reduction of false alarms [ 28 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

Tanuška

Spendla

Kebísek

et al. 2021

Sensors

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One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

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Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control

Cited by 74 publications

References 52 publications

Enable the landing of Internet of Things: a holistic approach

Enable the landing of Internet of Things: a holistic approach

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

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