Predictive Intelligence in Industrial Systems Using Deep Learning for Fault Diagnosis
Download PDFAbstract
This chapter delves into the application of deep learning for predictive intelligence in industrial systems, with a specific focus on fault diagnosis. As industrial machinery becomes more complex, the need for robust, automated, and accurate fault detection and diagnosis (FDD) systems is paramount to ensure safety, reduce downtime, and optimize maintenance schedules. Traditional FDD methods often rely on manual feature extraction and expert knowledge, which can be time-consuming and less effective in handling the vast amounts of data generated by modern sensors. This chapter introduces a comprehensive deep learning framework that leverages a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model to automatically learn hierarchical features from raw sensor data and diagnose various fault conditions in rotating machinery. We explore the entire workflow, from data acquisition and preprocessing to model training, evaluation, and interpretation. Using a simulated dataset inspired by the Case Western Reserve University (CWRU) bearing dataset, we demonstrate the proposed model's superior performance in identifying different types of bearing faults. The chapter provides an in-depth discussion of the results, including performance metrics, feature visualization, and comparisons with other machine learning approaches. Finally, we conclude with the challenges and future directions in the field of intelligent fault diagnosis.
References
- Cláudio Santos et al. "Towards Industry 4.0: an overview of European strategic roadmaps". In: Procedia manufacturing 13 (2017), pp. 972–979.
- Shaohua Qiu et al. "Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review". In: Sensors 23.3 (2023), p. 1305.
- Rolf Isermann. "Model-based fault-detection and diagnosis–status and applications". In: Annual Reviews in control 29.1 (2005), pp. 71–85.
- Robert Bond Randall. Vibration-based condition monitoring: industrial, automotive and aerospace applications. John Wiley & Sons, 2021.
- Andrew KS Jardine, Daming Lin, and Dragan Banjevic. "A review on machinery diagnostics and prognostics implementing condition-based maintenance". In: Mechanical systems and signal processing 20.7 (2006), pp. 1483–1510.
- Mohammadreza Akbari and Thu Nguyen Anh Do. "A systematic review of machine learning in logistics and supply chain management: current trends and future directions". In: Benchmarking: An International Journal 28.10 (2021), pp. 2977–3005.
- Jiangdong Zhao et al. "A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges". In: AIP Advances 15.2 (2025).
- Xiaojie Guo, Liang Chen, and Changqing Shen. "Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis". In: Measurement 93 (2016), pp. 490–502.
- Yaguo Lei et al. "Applications of machine learning to machine fault diagnosis: A review and roadmap". In: Mechanical systems and signal processing 138 (2020), p. 106587.
- Wade A Smith and Robert B Randall. "Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study". In: Mechanical systems and signal processing 64 (2015), pp. 100–131.
Deep Learning: Foundations, Advances, and Intelligent Applications
