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Machine Learning-Driven Predictive Maintenance in Smart Manufacturing

Author(s):Rajesh Kumar Sharma, Priya Nair, Michael T. Andersen, Anjali Mehta, Suresh Babu Reddy

Affiliation: Department of Mechanical Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India, Department of Electronics & Communication, Malla Reddy College of Engineering, Hyderabad, Telangana, India

Page No: 42-47

Volume issue & Publishing Year: Volume 3, Issue 3, March 2026

published on: 2026/03/08

Journal: International Journal of Advanced Multidisciplinary Application.(IJAMA)

ISSN NO: 3048-9350

DOI: https://doi.org/10.5281/zenodo.18931154

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Abstract:
Unplanned equipment failures in smart manufacturing environments generate substantial direct costs through lost production, emergency maintenance labour, and spare parts procurement, as well as indirect costs through contractual penalties, customer attrition, and reputational damage. Predictive maintenance (PdM) — the paradigm of continuously monitoring equipment health indicators and generating maintenance interventions only when and where required — offers the prospect of 70–85% reduction in unexpected downtime relative to time-based preventive strategies, while simultaneously extending component service life and optimising technician scheduling. The proliferation of Industrial Internet of Things (IIoT) sensor infrastructure, edge computing platforms, and industrial communication standards has created the technical preconditions for pervasive, low-latency PdM implementation across manufacturing plants of diverse scales and sectors.
This paper presents a comprehensive PdM framework integrating a novel hybrid Long Short-Term Memory — Gradient Boosting Machine (LSTM-GBM) architecture with an IoT-Edge-Cloud three-tier system design. The LSTM sub-component captures long-range temporal dependencies in multivariate sensor time series — including vibration, acoustic emission, current draw, temperature, and oil particle count — while the GBM sub-component provides rapid, high-precision fault classification on LSTM-generated feature embeddings. A dataset of 2.4 million sensor readings collected from CNC machining centres, industrial compressors, and conveyor systems across three manufacturing facilities over eighteen months was used for model training and validation. The proposed LSTM-GBM model achieved a fault detection accuracy of 94.7%, F1-score of 0.923, and AUC-ROC of 0.978 on the held-out test set, outperforming six benchmark methods including CNN-LSTM, standalone LSTM, standalone GBM, Random Forest, and SVM. Deployment across the three test facilities over a six-month operational trial demonstrated a 67.3% reduction in unplanned downtime, 31.4% decrease in maintenance costs, and a conservative projected annual saving of USD 2.84 million per facility, establishing a compelling operational and financial case for scaled IIoT-PdM implementation.

Keywords: predictive maintenance, LSTM-GBM, Industrial IoT, machine learning, fault detection, smart manufacturing, time series, edge computing, AUC-ROC, condition monitoring, vibration analysis, deep learning

Reference:

  • [1] Accenture. (2015). Driving Unconventional Growth through the Industrial Internet of Things. Accenture Strategy Report.
  • [2] Deloitte. (2017). Predictive Maintenance and the Smart Factory. Deloitte Insights Manufacturing Report.
  • [3] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  • [4] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30.
  • [5] Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834.
  • [6] Li, X., Zhang, W., & Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering & System Safety, 182, 208–218.
  • [7] Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.
  • [8] Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3), 2213–2227.
  • [9] Mobley, R. K. (2002). An Introduction to Predictive Maintenance (2nd ed.). Butterworth-Heinemann.
  • [10] Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
  • [11] Sipos, R., Fradkin, D., Moerchen, F., & Wang, Z. (2014). Log-based predictive maintenance. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1254–1263.
  • [12] Tsang, A. H. C. (1995). Condition-based maintenance: Tools and decision making. Journal of Quality in Maintenance Engineering, 1(3), 3–17.