Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

Abstract

This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

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“…Five-fold cross-validation is used to train and test the model. To forecast failure, the LSTM + CRP model is compared to the baseline LSTM [ 49 ], SVM [ 50 ], and logistic regression [ 51 ] models.…”

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“…RF [190] Classification-Bad or good product quality Heterogeneous (Various sensors from a production floor in a factory) CNN [189] Classification [192] Classification-Abnormal or normal vibration data (from electric motor in a crane) Homogeneous (Accelerometer) RF + SVM [193] Classification-Failure prediction Heterogeneous (Multiple sensors from SECOM dataset) RNN (LSTM) [194] Regression-Predicting data from sensors Heterogeneous (Different sensors [Pressure, Temperature, Vibration etc. ])…”

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