LSTM-based Working State Prediction of Industrial Internet Equipment

doi:10.3969/j.issn.1006-2475.2020.01.001

Abstract

Abstract: With the development of industrial Internet technology, working state prediction of industrial Internet equipment is of great significance for improving the reliability of equipment. In practical industrial scenarios, simple single-signal prediction and threshold methods are ineffective because the data is highly discrete and coincides over multiple time periods. This paper presents a working state prediction model of industrial Internet equipment based on LSTM (Long Short-Term Memory) neural network. Firstly, this paper uses the SMOTE algorithm for data skew processing and the PCA algorithm for data dimensionality reduction, then builds the working state prediction model of industrial Internet equipment based on LSTM neural network. Finally the model is evaluated by F1-Score. This paper is based on real air conditioning compressor data for experimental verification. The experimental results show the effectiveness of the proposed method.

Key words: LSTM neural network, time series prediction, industrial Internet equipment

CLC Number:

TP277
TP18

LI Zhao-tong, ZHANG Wei-shan, GUO Wu-wu. LSTM-based Working State Prediction of Industrial Internet Equipment[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.01.001.

References

［1］周剑飞,刘晨. 基于LSTM的设备故障在线检测方法［J/OL］. 计算机工程与应用:1-9［2019-03-05］. http://kns.cnki.net/kcms/detail/11.2127.TP.20181225.2104.005.html.
［2］ DRAGOMIR O E, GOURIVEAU R, DRAGOMIR F, et al. Review of prognostic problem in condition-based maintenance［C］// 2009 IEEE European Control Conference. 2009:1587-1592.
［3］ CHANDOLA V, BANERJEE A, KUMAR V. Anomaly detection: A survey［J］. ACM Computing Surveys (CSUR), 2009,41(3):Article No.15.
［4］ SIPOS R, FRADKIN D, MOERCHEN F, et al. Log-based predictive maintenance［C］// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014:1867-1876.
［5］ RAWAT S, RAWAT S. Multi-sensor data fusion by a hybrid methodology: A comparative study［J］. Computers in Industry, 2016,75(C):27-34.
［6］ ZHANG K, XU J, MIN M R, et al. Automated IT system failure prediction: A deep learning approach［C］// 2016 IEEE International Conference on Big Data. 2016:1291-1300.
［7］ ZHU X, HUANG Z, YANG Y, et al. Self-taught dimensionality reduction on the high-dimensional small-sized data［J］. Pattern Recognition, 2013,46(1):215-229.
［8］ QIU X, FU D, FU Z. An efficient dimensionality reduction approach for small-sample size and high-dimensional data modeling［J］. Journal of Computers, 2014,9(3):576-581.
［9］ SHARMA N, SAROHA K. A novel dimensionality reduction method for cancer dataset using PCA and feature ranking［C］// 2015 IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2015:2261-2264.
［10］ABULIGAH L M, KHADER A T, AI-BETAR M A, et al. Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering［J］. Expert Systems with Applications, 2017,84:24-36.
［11］陈志平,汪赞,张国安,等. 基于大数据的电梯故障诊断与预测研究［J］. 机电工程, 2019,36(1):90-94.
［12］范李平,张晓辉,苏伟. 基于大数据挖掘的变电设备故障预警研究及应用［J］. 电力大数据, 2019(1):1-7.
［13］王桂兰,赵洪山,米增强. XGBoost算法在风机主轴承故障预测中的应用［J］. 电力自动化设备, 2019,39(1):73-77.
［14］LEAHY K, HU R L, KONSTANTAKOPOULOS I C, et al. Diagnosing wind turbine faults using machine learning techniques applied to operational data［C］// 2016 IEEE International Conference on Prognostics and Health Management. 2016:1-8.
［15］HINTON G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks［J］. Science, 2006,313(5786):504-507.
［16］ASSENDORP J P. Deep Learning for Anomaly Detection in Multivariate Time Series Data［D］. Hochschule für Angewandte Wissenschaften Hamburg, 2017.
［17］〖JP2〗MALHOTRA P, TV V, RAMAKRISHNAN A, et al. Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder［C］//The 1st ACM SIGKDD Workshop on Machine Learning for Prognostics Health Management. 2016.
［18］INCE T, KIRANYAZ S, EREN L, et al. Real-time motor fault detection by 1-D convolutional neural networks［J］. IEEE Transactions on Industrial Electronics, 2016,63(11):7067-7075.
［19］JOLLIFFE I. Principal Component Analysis［M］. Springer Berlin Heidelberg, 2011.
［20］FAWCETT T, PROVOST F. Adaptive fraud detection［J］. Data Mining and Knowledge Discovery, 1997,1(3):291-316.
［21］〖JP+1〗LEWIS D D, CATLETT J. Heterogeneous uncertainty sampling for supervised learning［C］// Proceedings of the 11th International Conference on Machine Learning. 1994:148-156.
［22］JAPKOWICZ N, MYERS C, GLUCK M. A novelty detection approach to classification［C］// International Joint Conference on Artificial Intelligence. 1995, 1: 518-523.
［23］CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic minority over-sampling technique［J］. Journal of Artificial Intelligence Research, 2002,16: 321-357.
［24］HOCHREITER S, SCHMIDHUBER J. Long short-term memory［J］. Neural Computation, 1997,9(8):1735-1780.

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