In this study, a data driven predictive maintenance system was developed for production lines in manufacturing. By utilizing the data generated from IoT sensors in real-time, the system aims to detect signals for potential failures before they occur by using machine learning methods. Consequently, it helps address the issues by notifying operators early such that preventive actions can be taken prior to a production stop. In current study, the effectiveness of the system was also assessed using real-world manufacturing system IoT data. The evaluation results indicated that the predictive maintenance system was successful in identifying the indicators of potential failures and it can help prevent some production stops from happening. The findings of comparative evaluations of machine learning algorithms indicated that models of Random Forest, a bagging ensemble algorithm, and XGBoost, a boosting method, appeared to outperform the individual algorithms in the assessment. The best performing machine learning models in this study have been integrated into the production system in the factory.

Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time

A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry

Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm

Electrical load-temperature CNN for residential load forecasting

Predictive maintenance using cox proportional hazard deep learning

An integrated deep learning-based approach for automobile maintenance prediction with GIS data

Automobile maintenance prediction using deep learning with GIS data

Reliability analysis for automobile engines: conditional inference trees

Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on automobile historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost.

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Scott Titmus

Papers

Predictive maintenance using cox proportional hazard deep learning

An integrated deep learning-based approach for automobile maintenance prediction with GIS data

Automobile maintenance prediction using deep learning with GIS data

Reliability analysis for automobile engines: conditional inference trees

Automobile Maintenance Modelling Using gcForest