A Microsensor Array for Diesel Engine Lubricant Monitoring Using Deep Learning with Stochastic Global Optimization
A Microsensor Array for Diesel Engine Lubricant Monitoring Using Deep Learning with Stochastic Global Optimization
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DOI：
10.1016/j.sna.2022.113671
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Aaron Urban;Jiang Zhe
中科院分区：
文献类型：
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作者：
Aaron Urban;Jiang Zhe
测量柴油机润滑油中的临界性质浓度是防止机器故障和过度损坏的重要任务。虽然有几个现有的传感器用于单独检测这些属性，但它们存在交叉敏感性问题和针对不同操作温度的繁琐校准。我们开发了一个叉指传感器阵列的基础上，人工神经网络（ANN）自动调整与随机全局优化（SGO）方法测量水，碱，烟尘，和柴油燃料污染物浓度的润滑油。利用神经网络对温度效应进行补偿。通过一个独特的模拟退火过程，从而导致在一个增加的预测精度相比，人工神经网络与传统的选择架构的神经网络结构自动选择。在训练过程中使用了辍学和数据增强技术，以防止过度拟合。实验结果表明，人工神经网络的能力，准确地确定从重叠的传感器响应的油的性质，以及消除需要校准各种工作温度。传感器阵列能够提供关于柴油动力机器的健康状态的全面信息。
Measurement of concentrations of the critical properties in a diesel powered machine’s lubricant oil is an important task in preventing machine failure and excessive damage. While there are several existing sensors for detecting these properties individually, they suffer from cross sensitivity issues and tedious calibrations for varying operating temperatures. We developed an interdigital sensor array based on an artificial neural network (ANN) automatically tuned with a stochastic global optimization (SGO) method for measuring water, base, soot, and diesel fuel contaminant concentrations in a lubricant oil. The temperature effect was compensated with the neural network. The neural network architecture was automatically selected through a unique simulated annealing process which resulted in an increased prediction accuracy when compared to the ANN with traditionally selected architecture. Dropout and data augmentation techniques were used during training to prevent overfitting. Experiment results demonstrated the ANN’s ability in accurately determining the oil properties from the overlapped sensor responses as well as removing the need to calibrate for a variety of operating temperatures. The sensor array is able to provide comprehensive information about a diesel powered machine’s health status.