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	<title>temporal features &#8211; BIOENGINEER.ORG</title>
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		<title>AI That Learns Where to Look: Reinforcement Learning Sharpens Machine Fault Diagnosis</title>
		<link>https://bioengineer.org/ai-that-learns-where-to-look-reinforcement-learning-sharpens-machine-fault-diagnosis/</link>
		
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		<pubDate>Thu, 08 Oct 2026 12:47:47 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[1D-CNN]]></category>
		<category><![CDATA[Applied Intelligence]]></category>
		<category><![CDATA[condition monitoring]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Fault Diagnosis]]></category>
		<category><![CDATA[hard attention]]></category>
		<category><![CDATA[hydraulic systems]]></category>
		<category><![CDATA[machinery health]]></category>
		<category><![CDATA[recurrent attention]]></category>
		<category><![CDATA[Reinforcement Learning]]></category>
		<category><![CDATA[rolling bearings]]></category>
		<category><![CDATA[temporal features]]></category>
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					<description><![CDATA[Researchers at Shanghai Jiao Tong University have developed a fault diagnosis framework that uses recurrent attentional reinforcement learning to adaptively search for the most informative temporal fragments in machinery sensor data, achieving 76.4 percent accuracy on rolling bearings and 100 percent on a hydraulic system.]]></description>
		
		
		
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