Update README.md

README.md

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 license: mit
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+<center>
+  <h1>CANDAS Dataset: A Cooling Fan Sound Dataset with Modeled Disturbances and Controlled Experimental Conditions<br>
+    <small>
+      <i>Henrique Lima, Cristofer Silva,
+      Ricardo Nery, Wilmer Córdoba Camacho, Ricardo Brazileiro, Rodrigo de Paula Monteiro, Andrea Maria Nogueira Cavalcanti Ribeiro, <br> Carmelo José Albanez Bastos-Filho,
+      Mariela Cerrada, Diego Pinheiro
+      </i>
+    </small>
+  </h1>
+</center>
+
+  <h5>
+    <i>Predictive maintenance cuts economic and safety risks in rotating machinery by leveraging vibration and acoustic data, which machine-learning
+    models convert into intelligent fault detectors. Acoustic signals are especially powerful for early fault detection and cooling fans, with simple rotational dynamics, 
+    are convenient proxies for complex rotors. Yet existing fan datasets lack disturbance models and controlled conditions. We present CANDAS, a controlled sound dataset 
+    featuring 28 h of recordings from two cooling fans under five modeled disturbance conditions.
+    </i>  
+  </h5>
+
+
+- Vibratory disturbance model proposed
+<center>
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/689a959b5ec21199bf4cf3fb/65ZM3ExSoZoSrW44n6UON.png" width="700">
+  <h4>The black dots represent first-layer magnets attached to the cooling fan blades, while the blue dots indicate additional magnets stacked on top. It is possible to design both
+balanced configurations (i.e., Config 1) and severely unbalanced ones(i.e., Config 5).</h4>
+</center>
 
-# CANDAS Dataset: A Cooling Fan Sound Dataset with Modeled Disturbances and Controlled Experimental Conditions