An Effective Predictive Maintenance Framework for Conveyor Motors Using Dual Time-Series Imaging and Convolutional Neural Network in an Industry 4.0 Environment

Kiangala, Kahiomba Sonia; Wang, Zenghui

doi:10.1109/access.2020.3006788

Cited by 80 publications

(69 citation statements)

References 28 publications

(52 reference statements)

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“…In order to overcome the feature engineering challenge, a recent research direction consists in representing complex information through images, and using Deep Neural Networks (DNN) for images classification to solve the AI task. This approach is used in different domains, including financial forecasting [31], activity recognition [32], and predictive maintenance [33], just to name a few. In this paper, we pursue this approach.…”

Section: Iot Techniques For Detecting Locomotion Anomaliesmentioning

confidence: 99%

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

2021

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The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of the elderly. In this work, we investigate the use of sensor data and deep learning to recognize those patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduce novel visual feature extraction methods for locomotion data. Our solution relies on locomotion trace segmentation, image-based extraction of salient features from locomotion segments, and vision-based deep learning. We carried out extensive experiments with a large dataset acquired in a smart-home test bed from 153 seniors, including people with cognitive diseases. Results show that our system can accurately recognize the cognitive status of the senior, reaching a macro-$$F_1$$ F 1 score of 0.873 for the three categories that we target: cognitive health, mild cognitive impairment, and dementia. Moreover, an experimental comparison shows that our system outperforms state-of-the-art methods.

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Section: Iot Techniques For Detecting Locomotion Anomaliesmentioning

confidence: 99%

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

2021

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“…Table 3 details a summary of the total 317 unique features within the articles included in the SLR, which have been grouped into five subcategories (fault detection, predictive maintenance, communication, virtualization, human machine interference (HMI)). Various Industry 4.0 IT solutions are used in fault detection [45][46][47], predictive maintenance [48][49][50], communication [51][52][53], virtualization [42,54,55], and human-machine interference (HMI) [56][57][58].…”

Section: Featuresmentioning

confidence: 99%

“…Various ML and AI solutions are key drivers of data-driven decision making are discussed. For example, big data analysis can be adopted for equipment reliability analysis and predictive maintenance, as discussed by several articles within the SLR process including Lee et al, Chen et [45,47,48,74,90,91]. ML algorithms can be used for predicting machine failures or abnormalities in advance, leading to better maintenance planning possibilities and cost reduction [45].…”

Section: Data-driven Decision Makingmentioning

confidence: 99%

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Lepasepp

Hurst

2021

Future Internet

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Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.

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“…As for fault feature construction, signal processing methods, such as wavelet transform, wavelet packet transform, empirical mode decomposition as well as variational mode decomposition, are adopted to construct the fault characteristic parameters. Then dimensionality reduction is realized through principal component analysis and auto-encoder of the constructed parameters [ 5 – 8 ]. The final key feature parameters are selected in this process.…”

Section: Introductionmentioning

confidence: 99%

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network

Yan

Liu

et al. 2021

PLoS ONE

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The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the “black box” problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.

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An Effective Predictive Maintenance Framework for Conveyor Motors Using Dual Time-Series Imaging and Convolutional Neural Network in an Industry 4.0 Environment

Cited by 80 publications

References 28 publications

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network

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