Predictive maintenance of actuators in linear systems: A receding horizon set‐theoretic approach

Tedesco, Francesco; Akram, Waseem; Forouzanfar, Mehdi; Casavola, Alessandro; Famularo, Domenico

doi:10.1002/rnc.6148

Cited by 1 publication

(1 citation statement)

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“…The continuous operation process of rolling bearings is its continuous degradation process. The evolution of this degradation process is considered measurable and predictable, and may lead to machine failure whenever certain thresholds are exceeded [2,3]. According to survey statistics, about 51% of industrial machinery and equipment failures are related to degradation damage to bearings [4].…”

Section: Introductionmentioning

confidence: 99%

Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM

et al. 2023

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For safe maintenance and to reduce the risk of mechanical faults, the remaining useful life (RUL) estimate of bearings is significant. The typical methods of bearings’ RUL prediction suffer from low prediction accuracy because of the difficulty in extracting features. With the aim of improving the accuracy of RUL prediction, an approach based on multi-branch improved convolutional network (MBCNN) with global attention mechanism combined with bi-directional long- and short-term memory (BiLSTM) network is proposed for bearings’ RUL prediction. Firstly, the original vibration signal is fast Fourier transformed to obtain the frequency domain signal and then normalized. Secondly, the original signal and the frequency domain signal are input into the designed MBCNN network as two branches to extract the spatial features, and then input into the BiLSTM network to further extract the timing features, and the RUL of bearings is mapped by the fully connected network to achieve the purpose of prediction. Finally, an example validation was performed on a publicly available bearing degradation dataset. Compared with some existing prediction methods, the mean absolute and root mean square errors of the predictions were reduced by “22.2%” to “50.0%” and “26.1%” to “52.8%”, respectively, which proved the effectiveness and feasibility of the proposed method.

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Section: Introductionmentioning

confidence: 99%