Scipediacontent at 10:47, 18 March 2026

2026-03-18T10:47:51Z

Scipediacontent: Scipediacontent moved page Review 307417406188 to Liu et al 2026b

2026-02-11T13:10:33Z

Scipediacontent moved page Review 307417406188 to Liu et al 2026b

Scipediacontent: Scipediacontent moved page Draft content 422287416 to Review 307417406188

2026-02-11T13:05:04Z

Scipediacontent moved page Draft content 422287416 to Review 307417406188

Scipediacontent: Created page with " == Abstract ==
Uncertainty and conflicting information are pervasive in artificial intelligence (AI)-driven engineering systems, especially in multisensor fault diagnosis..."

2026-02-11T13:04:36Z

Created page with " == Abstract == <p>Uncertainty and conflicting information are pervasive in artificial intelligence (AI)-driven engineering systems, especially in multisensor fault diagnosis..."

New page

== Abstract ==

<p>Uncertainty and conflicting information are pervasive in artificial intelligence (AI)-driven engineering systems, especially in multisensor fault diagnosis. Dempster-Shafer theory (DST) has garnered significant interest across various fields as it provides a powerful framework for modeling uncertainty. However, despite its advantages, the application of Dempster’s rule can lead to paradoxical outcomes when it encounters highly conflicting evidence. To address this limitation, this paper first presents a new evidential Jensen-alpha divergence (EJ AD) to quantify the discrepancy between the evidence items based on DST. Furthermore, an advanced version, the reinforced evidential Jensen-alpha divergence (REJ AD) is developed, which takes into account the quantity of potential propositions. We demonstrate thatREJ ADcan be transformed into various divergences such as the χ2divergence, Jensen-Shannon divergence, Hellinger distance, and arithmetic-geometric divergence under certain conditions. Also, we show the key properties ofREJ AD, including non-negativity, non-degeneracy and symmetry. Additionally, we design a new multisensor fault diagnosis method utilizingREJ ADand belief entropy. The superior performance of the proposed method is tested in three distinct fault diagnosis cases, and analysis shows robust performance across a range of its key parameter α, offering a computationally feasible, scalable and interpretable solution for AI-based decision-making in real-world engineering applications.OPEN ACCESS Received: 15/10/2025 Accepted: 17/11/2025</p>

== Document ==
<pdf>Media:Draft_content_422287416-9633-document.pdf</pdf>

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−	<pdf>Media:~~Draft_content_422287416-9633-document~~.pdf</pdf>	+	<pdf>Media:Liu_et_al_2026b_3855_61. TSP_RIMNI_74645.pdf</pdf>

← Older revision	Revision as of 13:10, 11 February 2026
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← Older revision	Revision as of 13:05, 11 February 2026
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Liu et al 2026b - Revision history

Scipediacontent at 10:47, 18 March 2026

Scipediacontent: Scipediacontent moved page Review 307417406188 to Liu et al 2026b

Scipediacontent: Scipediacontent moved page Draft content 422287416 to Review 307417406188

Scipediacontent: Created page with " == Abstract == Uncertainty and conflicting information are pervasive in artificial intelligence (AI)-driven engineering systems, especially in multisensor fault diagnosis..."

Scipediacontent: Created page with " == Abstract ==
Uncertainty and conflicting information are pervasive in artificial intelligence (AI)-driven engineering systems, especially in multisensor fault diagnosis..."