Convolutional feature extraction for process monitoring using ultrasonic sensors
Convolutional feature extraction for process monitoring using ultrasonic sensors
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DOI：
10.1016/j.compchemeng.2021.107508
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
A. Bowler;Michael P. Pound;N. Watson
中科院分区：
文献类型：
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作者：
A. Bowler;Michael P. Pound;N. Watson
超声波传感器是一种低成本的在线技术，可以与机器学习相结合用于工业过程监控。然而，使用传感器数据来训练用于过程监控的准确的机器学习模型依赖于特征选择方法。本文比较了卷积特征提取方法和传统的粗特征工程方法。卷积方法使用在辅助任务上预先训练的滤波权重来使用先前获得的传感器数据来分类超声波形数据集成员。滤波权重被用来从超声波形中提取特征。然后应用主成分分析产生五个主成分，以输入到长期短期记忆神经网络。在超声波传感器监测的发酵、混合和清洗数据集上对这两种方法进行了比较。总体而言，卷积特征方法产生的波形特征比粗略特征工程方法更有信息量，对于需要大量波形信息的数据集和65%的总体任务实现了更高的模型精度。多任务学习也改善了特征轨迹学习，但导致远离分类决策边界的数据点的模型精度降低。这可以通过进一步优化神经网络超参数来克服，尽管增加了模型开发时间。一旦训练好，卷积特征提取方法是一种使用卷积神经网络快速、方便地从超声波形中产生高质量特征的方法，并且只需要很少的训练数据。
Ultrasonic sensors are a low-cost and in-line technique and can be combined with machine learning for industrial process monitoring. However, training accurate machine learning models for process monitoring using sensor data is dependant on the feature selection methodology. This paper compares a convolutional feature extraction method to a traditional, coarse feature engineering approach. The convolutional method uses filter weights pre-trained on an auxiliary task to classify ultrasonic waveform dataset membership using previously obtained sensor data. The filter weights are used to extract features from the ultrasonic waveform. Principal component analysis is then applied to produce five principal components to be input into long short-term memory neural networks. The two approaches are compared on fermentation, mixing and cleaning datasets monitored using ultrasonic sensors. Overall, the convolutional feature method produced more informative waveform features than the coarse feature engineering approach, achieving higher model accuracy for datasets requiring substantial waveform information and for 65% of tasks overall. Multi-task learning also improved feature trajectory learning but led to reduced model accuracy for data points far from the classification decision boundaries. This can be overcome by further optimisation of neural network hyperparameters, though at increased model development time. Once trained, the convolutional feature extraction approach is a fast and convenient way of producing high quality features from ultrasonic waveforms using convolutional neural networks with little training data.