A novel approach for predictive maintenance combining GAF encoding strategies and deep networks

Ferraro, Antonino; Galli, Antonio; Moscato, Vincenzo; Sperlí, Giancarlo

doi:10.1109/dependsys51298.2020.00027

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…This might be due to the specific attributes of each PdM case. In general, synthetic data cannot represent a real event or failure, and generating synthetic data requires knowledge of the equipment [93][94][95][96][97].…”

Section: Answers To Rq5: Distribution Of Studies By Input Parameters ...mentioning

confidence: 99%

A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect

et al. 2022

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With the rapid progress of network technologies and sensors, monitoring the sensor data such as pressure, temperature, current, vibration and other electrical, mechanical and chemical variables has become much more significant. With the arrival of Big Data and artificial intelligence (AI), sophisticated solutions can be developed to prevent failures and predict the equipment’s remaining useful life (RUL). These techniques allow for taking maintenance actions with haste and precision. Accordingly, this study provides a systematic literature review (SLR) of the predictive maintenance (PdM) techniques in transportation systems. The main focus of this study is the literature covering PdM in the motor vehicles’ industry in the last 5 years. A total of 52 studies were included in the SLR and examined in detail within the scope of our research questions. We provided a summary on statistical, stochastic and AI approaches for PdM applications and their goals, methods, findings, challenges and opportunities. In addition, this study encourages future research by indicating the areas that have not yet been studied in the PdM literature.

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Section: Answers To Rq5: Distribution Of Studies By Input Parameters ...mentioning

confidence: 99%

A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect

et al. 2022

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A deep attention based approach for predictive maintenance applications in IoT scenarios

Luca

Ferraro²,

Galli³

et al. 2023

JMTM

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PurposeThe recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.Design/methodology/approachIn this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.FindingsThe achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.Research limitations/implicationsA comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.Practical implicationsThe proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.Originality/valueThe proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

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MTAP-DK: Multivariate Time-Series Anomaly Prediction with Domain Knowledge

Xue

Peng

Zhang

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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A novel approach for predictive maintenance combining GAF encoding strategies and deep networks

Cited by 6 publications

References 17 publications

A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect

A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect

A deep attention based approach for predictive maintenance applications in IoT scenarios

MTAP-DK: Multivariate Time-Series Anomaly Prediction with Domain Knowledge

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