基于动态软传感器的制造业,与使用模糊逻辑和深度学习架构的特征表示和分类集成
Shakir Khan1,2, Tamanna Siddiqui3, Azrour Mourade4
1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
The International journal, advanced manufacturing technology
|June 26, 2023
概括
本研究介绍了一种用于工业自动化的新型深度学习技术,通过高级特征表示和分类来提高软传感器的准确性. 该方法显著提高了制造过程中的预测性能和测量精度.
科学领域:
- 自动化和控制工程自动化和控制工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 软传感器对于估计难以测量的工业过程变量至关重要.
- 准确的特征表示是开发有效软传感器的关键.
- 深度学习 (DL) 为软传感中的复杂数据结构提供了高级功能.
研究的目的:
- 提出一种用于工业自动化中的基于软传感器的动态特征表示和数据分类的新技术.
- 通过预处理解决常见的数据问题,如缺失值和硬件故障.
- 提高在制造环境中的软传感器的准确性和效率.
主要方法:
- 数据预处理以处理缺失值并识别硬件/通信错误.
- 使用基于模糊逻辑的堆叠数据驱动自动编码器 (FL_SDDAE) 的特征表示.
- 使用最小平方误差反传播神经网络 (LSEBPNN) 来对表示特征进行分类,以最大限度地减少平均平方误差.
主要成果:
- 实现了34%的计算时间缩短.
- 提高了64%的服务质量 (QoS).
- 根平均平方误差 (RMSE) 减少了41%,平均绝对误差 (MAE) 减少了35%.
- 证明了94%的预测性能和85%的测量准确度.
结论:
- 拟议的FL_SDDAE和LSEBPNN方法为工业自动化软传感器技术提供了重大进展.
- 该技术有效地处理数据复杂性,并改善关键性能指标.
- 这种方法提供了一个强大的解决方案,以加强制造过程的监控和控制.
相关概念视频
01:26Classification of Systems-I
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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01:22Multi-input and Multi-variable systems
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In the absence...
In the absence...
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01:31Classification of Systems-II
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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01:30Classification of Signals
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291