Neural Turing Machines for the Remaining Useful Life estimation problem

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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“…Recently, Falcon et al . (2020) proposed a dual-stream architecture, which consists of a MHA and a neural Turing machine module.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, Falcon et al (2020) proposed a dual-stream architecture, which consists of a MHA and a neural Turing machine module. Briefly, the time-series are first cut into shorter windows.…”

Section: Almentioning

confidence: 99%

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

Luca

Ferraro²,

Galli³

et al. 2023

JMTM

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“…Falcon et al [103] showed that neural turing machines (NTMs) can estimate RUL more accurately than MLPs, CNNs, and LSTMs. The NTM architecture's additional memory access allows it to extract features directly from raw sensor measurements.…”

Section: Ai For Defect Prognosis and Estimation Of Rulmentioning

confidence: 99%

Latest innovations in the field of condition-based maintenance of rotatory machinery: a review

Kumar,

Gandhi,

Tang

et al. 2023

Meas. Sci. Technol.

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Health monitoring in rotatory machinery is a process of developing a mechanism to determine its state of deterioration. It involves analysing the presence of damage, locating the fault, determining the severity of the problem, and calculating the amount of time that the machine can still be used effectively by making use of signal processing methods. The journey started to repair when the machine fails and progressed to the modern era, which involves the use of advanced sensors to capture data and conduct on-line signal processing methods to extract relevant features. By seamlessly integrating advanced smart sensing, data collection, and intelligent algorithms, modern technologies have transformed the landscape of condition-based maintenance for rotary machinery, bridging the gap between fundamental understanding and practical engineering applications. In this review paper, first, the roadmap of the condition-based maintenance (CBM) journey for rotary machinery is briefly introduced. Then, CBM task techniques are reviewed in the context of manual identification of defects, applying artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life. Finally, the challenges, and issues of signal processing methods to detect faults in rotary machinery, and remedies to overcome such challenges are deeply discussed and future research directions are identified to ensure safe operation for rotary machinery.

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