Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

Mancuso, Alessandro; Compare, Michele; Salo, Ahti; Zio, Enrico

doi:10.1016/j.ress.2021.107536

Cited by 37 publications

(10 citation statements)

References 55 publications

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“…An assessment involving the health status, fault diagnosis, and RUL predictions was achieved [30]. More recent data-driven machine learning models and RUL predictions are proposed in the literature for many electrical systems [31][32][33]. However, PHM study on SMPS using the fusion of electrical signature data has been very limited.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

et al. 2021

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Switched-mode power supply (SMPS) has been of vital importance majorly in power management of industrial equipment with much-improved efficiency and reliability. Given the diverse range on loading and operating conditions of SMPS, several anomalies can occur in the device resulting to over-voltage, overloading, erratic atmospheric conditions, etc. Electrical over-stress (EOS) is one of the commonly used causes of failure among power electronic devices. Since there is a limitation for the SMPS in terms of input voltage and current (two methods of controlling an SMPS), the device has been subjected to an accelerated aging test using EOS. This study presents a two-fold approach to evaluate the overall state of health of SMPS using an integration of extended Kalman filter (EKF) and deep neural network. Firstly, the EKF algorithm would assist in fusing fault features to acquire an comprehensive degradation trend. Secondly, the degradation pattern of the SMPS has been monitored for four different electrical loadings, and a bi-directional long short-term memory (BiLSTM) deep neural network is trained for future predictions. The proposed model provides a unique approach and accuracy in SMPS fault indication with the aid of electrical parameters.

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Section: Motivation and Literature Reviewmentioning

confidence: 99%

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

et al. 2021

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“…Therefore, to further improve maintenance intelligence during an entire lifespan, new health theories and technologies are necessary. Prognostics and health management (PHM) (Zeng et al, 2005;Mancuso et al, 2021) focuses mainly on health status monitoring, prognostics, and management. PHM collects data on different dimensions and applies various intelligent algorithms to convert measured data into indicators that can characterize the health status of components to predict and manage faults before they occur.…”

Section: System Healthmentioning

confidence: 99%

Percolation-based health management of complex traffic systems

et al. 2021

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System health management, which aims to ensure the safe and efficient operation of systems by reducing uncertain risks and cascading failures during their lifetime, is proposed for complex transportation systems and other critical infrastructures, especially under the background of the New Infrastructure Projects launched in China. Previous studies proposed numerous approaches to evaluate or improve traffic reliability or efficiency. Nevertheless, most existing studies neglected the core failure mechanism (i.e., spatio-temporal propagation of traffic congestion). In this article, we review existing studies on traffic reliability management and propose a health management framework covering the entire traffic congestion lifetime, from emergence, evolution to dissipation, based on the study of core failure modes with percolation theory. Aiming to be “reliable, invulnerable, resilient, potential, and active”, our proposed traffic health management framework includes modeling, evaluation, diagnosis, and improvement. Our proposed framework may shed light on traffic management for megacities and urban agglomerations around the world. This new approach may offer innovative insights for systems science and engineering in future intelligent infrastructure management.

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“…bearings often requires halting production, leading to substantial cost expenditures and affecting factory production schedules. Therefore, the accurate prediction of the remaining useful life (RUL) of bearings under complex and harsh interference conditions is paramount for developing equipment maintenance strategies, reducing plant operation and maintenance costs, and enhancing factory production efficiency [3].…”

Section: Introductionmentioning

confidence: 99%

Ultra-long-term spatiotemporal feature fusion network for bearing remaining life prediction under strong interference

Yin,

Tan,

Wan

2024

Meas. Sci. Technol.

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Under high noise conditions and random impacts, which constitute strong interference, models often exhibit limited capability in capturing long-term dependencies, leading to lower accuracy in predicting the Remaining Useful Life (RUL) of bearings. To address this issue, a spatiotemporal fusion network capable of ultra-long-term feature analysis is proposed to enhance the accuracy of bearing RUL prediction under substantial interference. This network utilizes a dilated convolution-based lightweight vision transformer encoder to extract spatial features reflecting the short-term degradation state of the bearing. Then, these features are sequentially fed into an adaptive tiered memory unit, based on the multiple attention mechanism and the neuron layering mechanism, to analyze temporal features indicative of long-term degradation. Subsequently, short-term spatial and long-term temporal features are fused for RUL prediction. To validate the robustness and predictive accuracy of the proposed approach under strong interference, a gearbox-rolling bearing accelerated platform is constructed, simulating high noise and random impact conditions. Experiments confirm the high robustness and predictive accuracy of the proposed method under strong interference conditions.

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Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

Cited by 37 publications

References 55 publications

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

Percolation-based health management of complex traffic systems

Ultra-long-term spatiotemporal feature fusion network for bearing remaining life prediction under strong interference

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