Author(s)

Weidong Li ¹, Renhao Xu ¹, Binbin Qiu ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China

Corresponding Author

Binbin Qiu

ABSTRACT

Bearing vibration signals are fundamental to the condition monitoring and fault diagnosis of rotating machinery, serving as key indicators of mechanical health. However, due to their strong nonlinearity and non-stationary dynamic behavior, conventional signal processing and feature extraction techniques often struggle to capture meaningful fault-related information with sufficient clarity and robustness. To overcome these limitations, this paper proposes an enhanced time-frequency analysis framework, rigorously validated using experimental datasets acquired from a machinery fault simulation test rig. The proposed method incorporates multiple data augmentation techniques—specifically, noise injection, gain adjustment, and time reversal—to enrich training diversity and improve feature representation. These augmentations effectively enhance time-frequency resolution, suppress background noise and cross-term interference, and sharpen the definition of transient fault signatures. Consequently, the approach yields significantly more discriminative and robust fault indicators compared to baseline methods. Comprehensive comparative experiments further demonstrate the superiority of the proposed framework and provide valuable practical insights for the deployment of deep learning models in real-world intelligent fault diagnosis systems.

KEYWORDS

Industry-education integration, Bearing fault diagnosis, Vibration signal recognition, Time-frequency analysis, Data augmentation

CITE THIS PAPER

Weidong Li, Renhao Xu, Binbin Qiu. Industry-Education Integration Case for an Enhanced Time-Frequency Analysis Method: Bearing Fault Diagnosis. Automation and Machine Learning (2026). Vol. 7, No. 1, 62-70. DOI: http://dx.doi.org/10.23977/autml.2026.070108.

REFERENCES

[1] Puntambekar R, Vyas P, Thakkar A, Patel D. A survey of machine learning and deep learning methods for vibration-based bearing fault diagnosis: The need, challenges, and potential future research directions. Neurocomputing. 2025; 661: 131628.
[2] Cerrada M, Sánchez RV, Li C, Pacheco F, Cabrera D, De Oliveira JV, Vásquez RE. A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing. 2018; 99: 169–196.
[3] Rehman AU, Jiao W, Jiang Y, Wei J, Sohaib M, Sun J, et al. Deep learning in industrial machinery: A critical review of bearing fault classification methods. Applied Soft Computing. 2025; 171: 112785.
[4] Hakim M, Omran AAB, Ahmed AN, Al-Waily M, Abdellatif A. A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations. Ain Shams Engineering Journal. 2023; 14(4): 101945.
[5] Guan Y, Feng Z. Adaptive linear chirplet transform for analyzing signals with crossing frequency trajectories. IEEE Transactions on Industrial Electronics. 2021; 69(8): 8396-8410.
[6] Yuan PP, Zhang J, Feng JQ, Wang HH, Ren WX, Wang C. An improved time-frequency analysis method for structural instantaneous frequency identification based on generalized S-transform and synchroextracting transform. Engineering Structures. 2022; 252: 113657.

Sponsors, Associates, and Links

---

• Power Systems Computation
  
  
• Internet of Things (IoT) and Engineering Applications
  
  
• Computing, Performance and Communication Systems
  
  
• Journal of Artificial Intelligence Practice
  
  
• Advances in Computer, Signals and Systems
  
  
• Journal of Network Computing and Applications
  
  
• Journal of Web Systems and Applications
  
  
• Journal of Electrotechnology, Electrical Engineering and Management
  
  
• Journal of Wireless Sensors and Sensor Networks
  
  
• Journal of Image Processing Theory and Applications
  
  
• Mobile Computing and Networking
  
  
• Vehicle Power and Propulsion
  
  
• Frontiers in Computer Vision and Pattern Recognition
  
  
• Knowledge Discovery and Data Mining Letters
  
  
• Big Data Analysis and Cloud Computing
  
  
• Electrical Insulation and Dielectrics
  
  
• Crypto and Information Security
  
  
• Journal of Neural Information Processing
  
  
• Collaborative and Social Computing
  
  
• International Journal of Network and Communication Technology
  
  
• File and Storage Technologies
  
  
• Frontiers in Genetic and Evolutionary Computation
  
  
• Optical Network Design and Modeling
  
  
• Journal of Virtual Reality and Artificial Intelligence
  
  
• Natural Language Processing and Speech Recognition
  
  
• Journal of High-Voltage
  
  
• Programming Languages and Operating Systems
  
  
• Visual Communications and Image Processing
  
  
• Journal of Systems Analysis and Integration
  
  
• Knowledge Representation and Automated Reasoning
  
  
• Review of Information Display Techniques
  
  
• Data and Knowledge Engineering
  
  
• Journal of Database Systems
  
  
• Journal of Cluster and Grid Computing
  
  
• Cloud and Service-Oriented Computing
  
  
• Journal of Networking, Architecture and Storage
  
  
• Journal of Software Engineering and Metrics
  
  
• Visualization Techniques
  
  
• Journal of Parallel and Distributed Processing
  
  
• Journal of Modeling, Analysis and Simulation
  
  
• Journal of Privacy, Trust and Security
  
  
• Journal of Cognitive Informatics and Cognitive Computing
  
  
• Lecture Notes on Wireless Networks and Communications
  
  
• International Journal of Computer and Communications Security
  
  
• Journal of Multimedia Techniques
  
  
• Computational Linguistics Letters
  
  
• Journal of Computer Architecture and Design
  
  
• Journal of Ubiquitous and Future Networks