Predicting machine failures using machine learning and deep learning algorithms

Devendra K. Yadav , Aditya Kaushik , Nidhi Yadav
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Abstract

Industry 4.0 emphasizes real-time data analysis for understanding and optimizing physical processes. This study leverages a Predictive Maintenance Dataset from the UCI repository to predict machine failures and categorize them. This study covers two objectives namely, to compare the performance of machine learning algorithms in classifying machine failures, and to assess the effectiveness of deep learning techniques for improved prediction accuracy. The study explores various machine learning algorithms and finds the XG Boost Classifier to be the most effective among them. Long Short-Term Memory (LSTM), a deep learning algorithm, demonstrates its superior accuracy in predicting machine failures compared to both traditional machine learning and Artificial Neural Networks (ANN). The novelty of this study is the application and comparison of machine learning and deep learning models to an unbalanced dataset. Findings of this study hold significant implications for industrial management and research. The study demonstrates the effectiveness of machine learning and deep learning algorithms in predictive maintenance, enabling proactive maintenance interventions and resource optimization.

利用机器学习和深度学习算法预测机器故障
工业 4.0 强调通过实时数据分析来了解和优化物理过程。本研究利用 UCI 数据库中的预测性维护数据集来预测机器故障并对其进行分类。本研究有两个目标,一是比较机器学习算法在机器故障分类方面的性能,二是评估深度学习技术在提高预测准确性方面的有效性。研究探索了各种机器学习算法,发现 XG Boost 分类器是其中最有效的算法。与传统机器学习和人工神经网络(ANN)相比,深度学习算法 "长短期记忆"(LSTM)在预测机器故障方面表现出更高的准确性。本研究的新颖之处在于将机器学习和深度学习模型应用于非平衡数据集并进行比较。研究结果对工业管理和研究具有重要意义。本研究证明了机器学习和深度学习算法在预测性维护中的有效性,从而实现了主动维护干预和资源优化。
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