Machine Learning-Based Predictive Maintenance for Mechanical Equipment in Smart Manufacturing

Defining the Residual Resource of Metallurgical Equipment Components Under Limited Data Conditions

This study presents the development and validation of a machine learning model for predicting the residual life of bronze bearing liners used in the universal spindle of a 1680 rolling mill. The model is based on the Random Forest ensemble algorithm and implemented in the RStudio environment using real industrial obtained from 36 bearing replacement events. The input feature set includes operating time, replacement frequency, failure probability density, and replacement count, enabling the identification of nonlinear relationships between technological parameters and component degradation. Model performance was evaluated using mean squared error (MSE) and mean absolute error (MAE), achieving an MSE of 0.125 and an MAE of 0.32 days on the test data set, with cross‐validation confirming model stability (MSE = 0.130 ± 0.012). Sensitivity analysis demonstrated robustness to input data variations typical for industrial environments. Comparative analysis with linear regression showed significantly lower predictive accuracy of conventional statistical approaches. The obtained results confirm the feasibility of applying ensemble machine learning methods for reliable residual life prediction under small‐sample industrial conditions. The proposed approach enables data‐driven maintenance planning, reduces the risk of unplanned downtime, and supports the implementation of intelligent condition monitoring systems aligned with Industry 4.0 principles.

Predictive Maintenance in Production Equipment-Machine Learning–Based Failure Prediction for Improved Reliability in a Latex centrifuge. Mechanical Engineering

Machine Learning-Based Predictive Maintenance of a Wire Drawing Machine Using Vibration and Motor Current Signals

A Composite Health Index (CHI) is developed to transform multi-motor sensor data into an interpretable machine-level degradation indicator and is used to train ensemble machine learning models including Random Forest, Extra Trees, and XGBoost.

Comparative Analysis of Time, Frequency, and Wavelet Features for Tool Condition Monitoring Using Machine Learning under Cross-Tool Validation

Tool condition monitoring (TCM) is critical for ensuring high machining quality and preventing catastrophic tool failures in smart manufacturing. While data-driven approaches increasingly rely on complex machine learning architectures and hybrid feature extraction (combining time, frequency, and time-frequency domains), the specific computational and accurate contributions of each domain remain poorly understood—a critical bottleneck for real-time edge-computing deployment. This study presents a systematic benchmarking analysis comparing time-domain, frequency-domain (FFT), wavelet-based, and hybrid feature sets for tool wear classification using the standard PHM 2010 Data Challenge dataset. To ensure industrial relevance, model generalization is rigorously assessed through cross-tool validation using a robust leave-one-out approach. Operating under strict computational efficiency constraints, a Decision Tree and Random Forest framework is employed. The results demonstrate that simpler time-domain features perform exceptionally robustly, with the Random Forest model achieving 92.3% classification accuracy. Crucially, hybrid features offer only marginal improvements at the expense of high computational overhead, while standalone frequency and wavelet domains prove less effective under cross-tool constraints. Furthermore, our analysis reveals two critical pitfalls for practical TCM design: feature selection techniques fail to improve accuracy, and standard segment normalization severely degrades performance by inadvertently removing vital signal magnitude information linked to tool wear. These insights provide concrete, computationally efficient guidelines for deploying lean, high-accuracy machine learning models on real-time industrial edge devices. 

Development of ANN and ANFIS Models for Prediction of Tool Wear in High-Speed Milling

In precision machining, tool wear is one of the primary factors affecting machining quality and production efficiency. This study developed intelligent prediction models for tool wear in the high-speed milling (HSM) of AISI 1045 medium-carbon steel by integrating robust process design with backpropagation neural networks (BPNN) and adaptive neuro-fuzzy inference systems (ANFIS). Robust process design was employed to optimize the machining parameters, while the hyperparameters of both BPNN and ANFIS models were systematically optimized to improve prediction performance. Tool wear was measured after a fixed cutting length and used to establish the prediction models. The optimized machining parameters reduced tool wear by 53% compared with the worst experimental condition. The optimized BPNN model achieved a prediction accuracy of 96.68%, whereas the ANFIS model with Gaussian membership functions achieved 100%, demonstrating superior predictive performance. The proposed approach effectively combines robust process design and intelligent prediction models to accurately predict tool wear using a limited experimental dataset, providing an efficient methodology for tool wear prediction and machining parameter optimization in intelligent manufacturing applications.

Comparative Evaluation of Machine Learning Algorithms for Fault Diagnosis in Automotive Press Lines

The results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications.