Zhongyong Liu

TL;DR: This paper proposes a novel approach for fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) with two-dimension (2D) image data, and investigates its effectiveness of identifying faults at different PEMFCs in terms of discrimination capacity and robustness.

TL;DR: In this paper , a three-dimensional, multi-component and multi-physics PEMFC model is developed to investigate the effect of performance degradation on its external magnetic field.

TL;DR: In this paper , an efficient PEMFC fault diagnosis technique was proposed, where the external magnetic field (EMF) was measured during its operation, from which performance degradation mechanisms and corresponding faults can be identified.

TL;DR: A novel method is proposed to assess the cutting tool condition, which consists of a convolutional neural network with wider first-layer kernels (W-CONV), and long short-term memory (LSTM) and demonstrates that with the proposed method, tool wear condition can be identified accurately and efficiently.

TL;DR: In this article , a health indicator extraction method based on auto-encoder is proposed, with which PEMFC future voltage can be predicted by long short-term memory network (LSTM).