In the present work, vibration, acoustic and thermal signals were correlated to the semi-autogenous grinding mill working parameters such as total power and inlet water flow rate, and then these parameters were monitored using vibration, acoustic and thermal analyses. Next, the influential controlling parameters were obtained to monitor the mill conditions via SPSS software and afterward by exploiting neural network method, the modified controlling parameters were acquired for the development of mill performance. It was found that the mill vibration and the bearing temperature increased with motors power. Also, the results revealed that the mill noise reduced with the increase in water flow rate. Finally, an analytical relationship between the controlling parameters and the noise as a measurable parameter was proposed.

中文翻译：

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不同工况下半自动磨机特性的试验分析

在目前的工作中，振动、声学和热信号与半自动磨机的工作参数（如总功率和进水流量）相关联，然后使用振动、声学和热分析监测这些参数。接下来，通过SPSS软件获得影响磨机状态的控制参数，然后利用神经网络方法获得修改后的控制参数，用于磨机性能的发展。发现磨机振动和轴承温度随着电机功率的增加而增加。此外，结果表明磨机噪音随着水流量的增加而降低。最后，提出了控制参数与作为可测量参数的噪声之间的解析关系。