Fault Diagnosis and Prognostics in Machine Tool Systems

Machine tool systems underpin modern manufacturing, demanding high precision and uninterrupted operation. Fault diagnosis focuses on the early detection and classification of anomalies in components such as spindles, bearings and ball screws, while prognostics seeks to predict the remaining useful life of these elements. Together, these approaches support condition-based and predictive maintenance strategies that reduce unplanned downtime, optimise maintenance schedules and extend equipment life. Advances in sensor technologies have enabled continuous monitoring of vibration, acoustic emissions, temperature and motor currents. Signal-processing techniques in the time, frequency and time-frequency domains—alongside data-driven methods such as machine learning and deep neural networks—have greatly improved the sensitivity and specificity of fault detection. Model-based methods, including physics-informed degradation models, complement purely statistical approaches by providing insight into wear and fatigue mechanisms. Integrating these tools into digital twins and Industry 4.0 frameworks allows real-time assessment and automated decision-making, offering manufacturers a global competitive edge through lower costs, higher throughput and enhanced safety.

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Fault Diagnosis and Prognostics in Machine Tool Systems publication trend

The graph below shows the total number of articles in fault diagnosis and prognostics in machine tool systems across all publications each year (not limited to Nature Index journals).

Technical terms

Fault diagnosis: The process of detecting, isolating and identifying faults in machine components before they cause failure.

Prognostics: The prediction of the time at which a system or component will no longer perform its intended function.

Ball screw: A precision mechanical actuator that translates rotational motion to linear motion via recirculating ball bearings.

Time-frequency analysis: Techniques such as wavelet or short-time Fourier transforms that reveal how signal spectral content evolves over time.

Convolutional neural network (CNN): A deep learning architecture that automatically learns spatial hierarchies of features, often from image-like data.

Kernel extreme learning machine (KELM): A rapid training algorithm for single-layer feedforward networks using kernel functions for nonlinear mapping.

Remaining useful life (RUL): The estimated duration a component will function before it requires repair or replacement.

References

1. A Novel Fault Diagnosis Method Based on the KELM Optimized by Whale Optimization Algorithm. Machines (2022).
2. Ball screw fault diagnosis based on continuous wavelet transform and two-dimensional convolution neural network. Measurement and Control (2022).
3. Wear Calculation‐Based Degradation Analysis and Modeling for Remaining Useful Life Prediction of Ball Screw. Mathematical Problems in Engineering (2018).