Abstract

In industrial production, the characteristics of compressor vibration signal change with the production environment and other external factors. Therefore, to ensure the effectiveness of the model, the vibration signal prediction model needs to be updated constantly. Due to the complex structure of Long Short Term Memory (LSTM) network, the LSTM model is difficult to adapt to the scene of online update. Therefore, the update model based on LSTM is difficult to respond quickly to data changes, which affects the accuracy of the model. To solve this problem, the online learning algorithm is introduced into prediction model, Error-LSTM (E-LSTM) model is proposed in this paper. The main idea of E-LSTM model is to improve the accuracy and efficiency of the model according to test error of the model. First, the hidden layer neurons of LSTM network are divided into blocks, and only part of the modules are activated at each time step. The number of modules activated is determined by test error. Thus, the training speed of the model is accelerated and the efficiency of the model is improved. Second, the E-LSTM model can adaptively adjust the training method according to the data distribution characteristics, so as to improve the accuracy of updated model. In experimental part, two types of datasets are used to verify the performance of the proposed model. LSTM model is used for comparative experiments, and the results showed that the updating model based on E-LSTM is better than that based on LSTM in terms of model accuracy and efficiency.

中文翻译：

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基于改进的长时记忆的自适应更新模型用于振动信号的在线预测

摘要

在工业生产中，压缩机振动信号的特性随生产环境和其他外部因素而变化。因此，为了确保模型的有效性，需要不断更新振动信号预测模型。由于长期短期记忆（LSTM）网络的复杂性，LSTM模型很难适应在线更新的场景。因此，基于LSTM的更新模型难以快速响应数据更改，从而影响了模型的准确性。针对这一问题，将在线学习算法引入预测模型，提出了Error-LSTM（E-LSTM）模型。E-LSTM模型的主要思想是根据模型的测试误差来提高模型的准确性和效率。第一，LSTM网络的隐藏层神经元被划分为块，并且每个时间步仅激活部分模块。激活的模块数量取决于测试错误。因此，加快了模型的训练速度，并提高了模型的效率。其次，E-LSTM模型可以根据数据分布特征自适应地调整训练方法，从而提高了更新模型的准确性。在实验部分，使用两种类型的数据集来验证所提出模型的性能。用LSTM模型进行比较实验，结果表明，基于E-LSTM的更新模型在模型准确性和效率上均优于基于LSTM的模型。激活的模块数量取决于测试错误。因此，加快了模型的训练速度，并提高了模型的效率。其次，E-LSTM模型可以根据数据分布特征自适应地调整训练方法，从而提高了更新模型的准确性。在实验部分，使用两种类型的数据集来验证所提出模型的性能。用LSTM模型进行比较实验，结果表明，基于E-LSTM的更新模型在模型准确性和效率上均优于基于LSTM的模型。激活的模块数量取决于测试错误。因此，加快了模型的训练速度，并提高了模型的效率。其次，E-LSTM模型可以根据数据分布特征自适应地调整训练方法，从而提高了更新模型的准确性。在实验部分，使用两种类型的数据集来验证所提出模型的性能。用LSTM模型进行比较实验，结果表明，基于E-LSTM的更新模型在模型准确性和效率上均优于基于LSTM的模型。从而提高模型更新的准确性。在实验部分，使用两种类型的数据集来验证所提出模型的性能。用LSTM模型进行比较实验，结果表明，基于E-LSTM的更新模型在模型准确性和效率上均优于基于LSTM的模型。从而提高模型更新的准确性。在实验部分，使用两种类型的数据集来验证所提出模型的性能。用LSTM模型进行比较实验，结果表明，基于E-LSTM的更新模型在模型准确性和效率上均优于基于LSTM的模型。