Abdulkareem Ademola, Anyim Tochukwu, Popoola Olawale, Abubakar John, Ayoade Agbetuyi
Electrical and Information Engineering Department, Covenant University, P.M.B 1023, Ota, 112212, Ogun State, Nigeria.
Electrical Engineering Department, Centre for Energy and Electric Power, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Nigeria.
Heliyon. 2024 Dec 25;11(1):e41493. doi: 10.1016/j.heliyon.2024.e41493. eCollection 2025 Jan 15.
Unplanned downtime in industrial sectors presents significant challenges, impacting both production efficiency and profitability. To tackle this issue, companies are actively working towards optimizing their operations and reducing disruptions that hinder their ability to meet customer demands and financial goals. Predictive maintenance, utilizing advanced technologies like data analytics, machine learning, and IoT devices, offers real-time equipment data monitoring and analysis. This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. Implementing such a model can enable proactive maintenance, ultimately leading to decreased downtime in industrial operations. The study involved the acquisition of a dataset comprising healthy and faulty conditions of four 3-phase induction motors, along with relevant features for fault prediction. Multiple machine learning algorithms were trained using this dataset, exhibiting promising performance. The Random Forest (RF) model achieved the highest accuracy at 0.91, closely followed by the Artificial Neural Network (ANN) and k-nearest Neighbors (k-NN) models, both achieving an accuracy of 0.9. Meanwhile, the Decision Tree (DT) model showed the lowest accuracy at 0.89. Further model evaluation was carried out using a confusion matrix, which provided a detailed breakdown of the models' performance for each class, revealing the number of correctly and incorrectly classified induction motor conditions. The results from the confusion matrix indicate that the models effectively classified the various states and conditions of the induction motors. To enhance model performance in future work, potential avenues include refining the ANN and RF models, exploring transfer learning or ensemble methods, and incorporating diverse datasets to improve generalization.
工业部门的意外停机带来了重大挑战，影响着生产效率和盈利能力。为了解决这个问题，公司正在积极努力优化其运营，并减少那些阻碍他们满足客户需求和实现财务目标能力的干扰因素。利用数据分析、机器学习和物联网设备等先进技术的预测性维护，可提供实时设备数据监测和分析。本研究聚焦于开发一种通用的机器学习模型，用于预测工业环境中感应电动机的故障。实施这样的模型能够实现主动维护，最终减少工业运营中的停机时间。该研究涉及获取一个数据集，该数据集包含四台三相感应电动机的健康和故障状态，以及用于故障预测的相关特征。使用这个数据集对多种机器学习算法进行了训练，表现出了良好的性能。随机森林（RF）模型的准确率最高，为0.91，紧随其后的是人工神经网络（ANN）和k近邻（k-NN）模型，两者的准确率均为0.9。同时，决策树（DT）模型的准确率最低，为0.89。使用混淆矩阵进行了进一步的模型评估，该矩阵详细列出了每个类别的模型性能，揭示了感应电动机状态正确和错误分类的数量。混淆矩阵的结果表明，这些模型有效地对感应电动机的各种状态和条件进行了分类。为了在未来的工作中提高模型性能，潜在的途径包括优化ANN和RF模型、探索迁移学习或集成方法，以及纳入多样化的数据集以提高泛化能力。
