DeepESN neural networks for industrial predictive maintenance through anomaly detection from production energy Data
Open accessAbstract
Optimizing energy consumption is an important aspect of industrial competitiveness, as it directly impacts operational efficiency, cost reduction, and sustainability goals. In this context, anomaly detection (AD) becomes a valuable methodology, as it supports maintenance activities in the manufacturing sector, allowing for early intervention to prevent energy waste and maintain optimal performance. Here, an AD-based method is proposed and studied to support energy-saving predictive maintenance of production lines using time series acquired directly from the field. This paper proposes a deep echo state network (DeepESN)-based method for anomaly detection by analyzing energy consumption data sets from production lines. Compared with traditional prediction methods, such as recurrent neural networks with long short-term memory (LSTM), although both models show similar time series trends, the DeepESN-based method studied here appears to have some advantages, such as timelier error detection and higher prediction accuracy. In addition, the DeepESN-based method has been shown to be more accurate in predicting the occurrence of failure. The proposed solution has been extensively tested in a real-world pilot case consisting of an automated metal filter production line equipped with industrial smart meters to acquire energy data during production phases; the time series, composed of 88 variables associated with energy parameters, was then processed using the techniques introduced earlier. The results show that our method enables earlier error detection and achieves higher prediction accuracy when running on an edge device.
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Deep learning shines in predictive maintenance, using sensor data to detect subtle patterns and predict equipment failures before they happen.
“Deep learning shines in predictive maintenance, using sensor data to detect subtle patterns and predict equipment failures before they happen [52].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [52]
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Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26 · see citing works
“Deep learning shines in predictive maintenance, using sensor data to detect subtle patterns and predict equipment failures before they happen [52].”
- Authors
- Bonci, A., Fredianelli, L., Kermenov, R., Longarini, L., Longhi, S., Pompei, G., Prist, M., & Verdini, C.
- Affiliations
- Marche Polytechnic University (IT)
- National Research Council (IT)
- Syncode Scarl, 93900 Fermo, Italy
- Corresponding author
- Bonci, Andrea
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2024-09-26
- Volume
- 14
- Issue
- 19
- Pages
- 8686
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4402860901
- Cited by
- 16
- References
- 68
- Citations by year
- 2026: 4 · 2025: 12
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, cc-by)
- Funders
- the European project Horizon Europe “EDIH4Marche” (European Digital Innovation Hub for Marche), Call DIGITAL-2021-EDIH-01 (101084027)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- deep echo state network, anomaly detection, predictive maintenance, industrial energy consumption, failure prediction, energy conservation, edge computing, time series analysis, LSTM comparison
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