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	<title>Tahmine dayalı bakım &#8211; BIOENGINEER.ORG</title>
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		<title>Multi-Scale Indicators Enhance Proton Exchange Membrane Fuel Cell Health</title>
		<link>https://bioengineer.org/multi-scale-indicators-enhance-proton-exchange-membrane-fuel-cell-health/</link>
		
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		<pubDate>Sat, 17 Jan 2026 19:51:33 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Çok ölçekli göstergeler]]></category>
		<category><![CDATA[Enerji sürdürülebilirliği]]></category>
		<category><![CDATA[İşte 5 uygun etiket: **PEMFC sağlık durumu tahmini]]></category>
		<category><![CDATA[Tahmine dayalı bakım]]></category>
		<category><![CDATA[Yakıt hücresi teknolojisi** **Açıklama:** 1. **PEMFC sağlık durumu tahmini:** Makalenin temel ara]]></category>
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					<description><![CDATA[In an era where sustainable energy solutions are increasingly crucial for mitigating climate change, advancements in fuel cell technology are taking center stage. One of the promising innovations in this field is the proton exchange membrane fuel cell (PEMFC), known for its high efficiency and environmentally friendly operation. Researchers are continuously exploring ways to enhance [&#8230;]]]></description>
		
		
		
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		<title>Acoustic Machine Learning for Ball Bearing Fault Detection</title>
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		<pubDate>Mon, 29 Dec 2025 06:19:44 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Akustik makine öğrenmesi]]></category>
		<category><![CDATA[Endüstriyel akustik izleme]]></category>
		<category><![CDATA[Makine öğrenmesi ile arıza öngörüsü]]></category>
		<category><![CDATA[Rulman arıza tespiti]]></category>
		<category><![CDATA[Tahmine dayalı bakım]]></category>
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					<description><![CDATA[In the evolving landscape of industrial maintenance and machinery diagnostics, the utilization of advanced acoustic monitoring systems offers substantial potential for groundbreaking advancements. Recent research conducted by Chandrakala et al. unveils a machine learning-based approach tailored to detect faults in ball bearings through acoustic signals, proving that such an innovative methodology could revolutionize predictive maintenance [&#8230;]]]></description>
		
		
		
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