Intelligent Fault Diagnosis in Rotating Machinery

Rotating machinery underpins a vast array of industrial operations, from power generation turbines to manufacturing assembly lines and aerospace propulsion systems. Ensuring the integrity of bearings, shafts and gear assemblies is paramount to avoiding unplanned downtime, catastrophic failures and excessive maintenance costs. Intelligent fault diagnosis has emerged as a transformative approach, leveraging modern sensing technologies, signal processing and artificial intelligence to detect, classify and prognose incipient faults in real time. By shifting from manual feature extraction and rule­based decision making to data‐driven models, remote monitoring platforms can deliver higher accuracy, robustness to noisy environments and adaptability to varying operational regimes.

Central to this evolution are convolutional neural networks and advanced machine-learning frameworks that automatically learn discriminative representations from vibration, acoustic or electrical signals. Techniques such as time–frequency image analysis, deep generative modelling and domain adaptation address key challenges posed by non-stationary signals, scarce fault samples and changing operating conditions. Multi-sensor data fusion further enhances diagnostic confidence by jointly analysing complementary streams of information, while data augmentation methods based on synthetic signal generation help mitigate class imbalance and privacy constraints on real data.

Intelligent fault diagnosis not only improves the reliability and safety of critical rotating systems but also contributes to sustainability by optimising maintenance schedules, reducing material waste and lowering energy consumption. As industry 4.0 and digital-twin paradigms gain traction, the integration of cloud computing, edge analytics and physics-informed models promises even greater predictive insight and autonomous decision support. Continued interdisciplinary research will be required to refine algorithms, validate them under field conditions and ensure resilience against cyber-physical threats.

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Intelligent Fault Diagnosis in Rotating Machinery publication trend

The graph below shows the total number of articles in intelligent fault diagnosis in rotating machinery across all publications each year (not limited to Nature Index journals).

Technical terms

Rotating machinery: Mechanical systems with components such as shafts, bearings and gears in continuous motion, commonly found in industrial equipment.

Fault diagnosis: The process of detecting, isolating and characterising defects within machinery to inform maintenance and prevent failures.

Convolutional neural network (CNN): A class of deep-learning model that applies convolutional filters to input data, automatically extracting hierarchical features.

Domain adaptation: Techniques enabling a model trained under one set of operating conditions or sensor arrangements to perform accurately in different settings.

Time–frequency representation: A signal processing method that maps a time series into a joint time and frequency domain, revealing non-stationary patterns.

References

1. Data augmentation in predictive maintenance applicable to hydrogen combustion engines: a review. Artificial Intelligence Review (2024).
2. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals. Sensors (2017).
3. Deep Learning Enabled Fault Diagnosis Using Time‐Frequency Image Analysis of Rolling Element Bearings. Shock and Vibration (2017).
4. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox. Sensors (2017).