School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
Sensors (Basel). 2023 Jun 22;23(13):5819. doi: 10.3390/s23135819.
Accurate equipment operation trend prediction plays an important role in ensuring the safe operation of equipment and reducing maintenance costs. Therefore, monitoring the equipment vibration and predicting the time series of the vibration trend is one of the effective means to prevent equipment failures. In order to reduce the error of equipment operation trend prediction, this paper proposes a method for equipment operation trend prediction based on a combination of signal decomposition and an Informer prediction model. Aiming at the problem of high noise in vibration signals, which makes it difficult to obtain intrinsic characteristics when directly using raw data for prediction, the original signal is decomposed once using the variational mode decomposition (VMD) algorithm optimized by the improved sparrow search algorithm (ISSA) to obtain the intrinsic mode function (IMF) for different frequencies and calculate the fuzzy entropy. The improved adaptive white noise complete set empirical mode decomposition (ICEEMDAN) is used to decompose the components with the largest fuzzy entropy to obtain a series of intrinsic mode components, fully combining the advantages of the Informer model in processing long time series, and predict equipment operation trend data. Input all subsequences into the Informer model and reconstruct the results to obtain the predicted results. The experimental results indicate that the proposed method can effectively improve the accuracy of equipment operation trend prediction compared to other models.
准确的设备运行趋势预测对于确保设备的安全运行和降低维护成本起着重要作用。因此，监测设备振动并预测振动趋势的时间序列是防止设备故障的有效手段之一。为了降低设备运行趋势预测的误差，本文提出了一种基于信号分解和 Informer 预测模型相结合的设备运行趋势预测方法。针对振动信号中存在的高噪声问题，直接使用原始数据进行预测时难以获取内在特征，本文采用改进麻雀搜索算法（ISSA）优化的变分模态分解（VMD）算法对原始信号进行一次分解，得到不同频率的固有模态函数（IMF），并计算模糊熵。采用改进的自适应白噪声完备集合经验模态分解（ICEEMDAN）对具有最大模糊熵的分量进行分解，得到一系列固有模态分量，充分结合了 Informer 模型在处理长序列方面的优势，对设备运行趋势数据进行预测。将所有子序列输入到 Informer 模型中并重构结果，得到预测结果。实验结果表明，与其他模型相比，所提出的方法可以有效地提高设备运行趋势预测的准确性。
