Resource-Efficient Field-Programmable Gate Array Implementation of an Integer Fuzzy Inference System for Conveyor Gear Condition Monitoring
Received: 11 December 2025 | Revised: 18 January 2026, 29 January 2026, and 1 February 2026 | Accepted: 2 February 2026 | Online: 16 February 2026
Corresponding author: Jazi Eko Istiyanto
Abstract
Industrial conveyor systems rely on reliable gearbox drives, where undetected tooth scoring and fractures can lead to downtime and safety risks. Vibration monitoring is well-established, but edge deployment on low-power Field-Programmable Gate Arrays (FPGAs) is constrained by limited logic resources and the absence of floating-point hardware. This study presents a VLSI implementation of an integer fuzzy inference engine for real-time conveyor gear condition monitoring on a small Lattice iCE40 HX8K FPGA. Three time-domain features—peak-to-peak and Willison amplitudes from the y- and z-axes of an ADXL345 accelerometer—are computed over 1 s windows and fed to a 3-class fuzzy system (Normal, Scoring, Damaged) with trapezoidal inputs and triangular outputs, implemented entirely in fixed-point arithmetic. Membership values are quantized from a 64-bit floating-point reference to 16-, 8-, and 4-bit integers. On a labeled vibration dataset, all configurations achieve F1-scores above 90%, and the 8-bit engine matches the floating-point baseline within 0.46 % while using 5256 of 7680 logic cells (68%). The results indicate that integer fuzzy inference is a practical option for gearbox monitoring on low-end FPGAs under strict area and power budgets.
Keywords:
field-programmable gate array, fuzzy inference system, gear condition monitoring, vibration, time-domain featuresDownloads
References
N. J. Parmar, A. T. James, and M. Asjad, "Analysis of Maintenance Outsourcing Challenges for Belt Conveyors in the Industry 4.0 Era," Journal of Global Operations and Strategic Sourcing, vol. 16, no. 3, pp. 718–744, Aug. 2023.
M. Elahi, S. O. Afolaranmi, W. M. Mohammed, and J. L. Martinez Lastra, "Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems," Energies, vol. 15, no. 13, Jun. 2022, Art. no. 4705.
S. E. Kramti, J. B. Ali, E. Bechhoefer, K. Takrouni, A. Darghouthi, and M. Sayadi, "Toward an Online Strategy for Mechanical Failures Diagnostics Inside the Wind Turbine Generators Based on Spectral Analysis," Wind Engineering, vol. 45, no. 4, pp. 782–792, Aug. 2021.
Q. Fan, Q. Zhou, C. Wu, and M. Guo, "Gear Tooth Surface Damage Diagnosis Based on Analyzing the Vibration Signal of an Individual Gear Tooth," Advances in Mechanical Engineering, vol. 9, no. 6, Jun. 2017, Art. no. 168781401770435.
L. Wang, H. Li, T. Xi, and S. Wei, "Fault Feature Extraction Method for Rolling Bearings Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Variational Mode Decomposition," Sensors, vol. 23, no. 23, Nov. 2023, Art. no. 9441.
M. Altaf, T. Akram, M. A. Khan, M. Iqbal, M. M. I. Ch, and C.-H. Hsu, "A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals," Sensors, vol. 22, no. 5, Mar. 2022, Art. no. 2012.
S. Eichstädt, M. Gruber, A. P. Vedurmudi, B. Seeger, T. Bruns, and G. Kok, "Toward Smart Traceability for Digital Sensors and the Industrial Internet of Things," Sensors, vol. 21, no. 6, Mar. 2021, Art. no. 2019.
M. Uppal et al., "Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve," Computers, Materials & Continua, vol. 72, no. 1, pp. 1799–1814, 2022.
A. Ikpehai et al., "Low-Power Wide Area Network Technologies for Internet-of-Things: A Comparative Review," IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2225–2240, Apr. 2019.
B. Liang, S. Wang, Y. Huang, Y. Liu, and L. Ma, "F-LSTM: FPGA-Based Heterogeneous Computing Framework for Deploying LSTM-Based Algorithms," Electronics, vol. 12, no. 5, Feb. 2023, Art. no. 1139.
M. Rosół and W. Kula, "An Energy-Efficient Field-Programmable Gate Array Rapid Implementation of a Structural Health Monitoring System," Energies, vol. 17, no. 11, May 2024, Art. no. 2626.
S. S. Rafiammal, D. N. Jamal, and S. K. Mohideen, "Reconfigurable Hardware Design for Automatic Epilepsy Seizure Detection using EEG Signals," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5803–5807, Jun. 2020.
B. Bengherbia, R. Kara, A. Toubal, M. O. Zmirli, S. Chadli, and P. Wira, "FPGA Implementation of a Wireless Sensor Node with a Built-in ADALINE Neural Network Coprocessor for Vibration Analysis and Fault Diagnosis in Machine Condition Monitoring," Measurement, vol. 163, Oct. 2020, Art. no. 107960.
M. H. El Yousfi Alaoui, A. Jilbab, and S. El Hani, "A New Approach to DWT Design for Real Time De-noising of Vibration Signatures Related to the Induction Machine Defects," in 2016 International Conference on Electrical and Information Technologies, Tangiers, Morocco, May 2016, pp. 197–202.
S. Raja and M. Rathinakumar, "Transient Analysis of the Fuzzy Logic-Based Speed Control of a Three-phase BLDC Motor," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 9855–9860, Feb. 2023.
C. Pham-Quoc, T. Pham-Dinh, and B. Kieu-Do-Nguyen, "Efficient Random Forest Acceleration for Edge Computing Platforms with FPGA Technology," Journal of Advances in Information Technology, vol. 15, no. 2, pp. 195–201, 2024.
Y. Luo and Y. Chen, "FPGA-Based Acceleration on Additive Manufacturing Defects Inspection," Sensors, vol. 21, no. 6, Mar. 2021, Art. no. 2123.
V.-K. Wong et al., "Active Ultrasonic Structural Health Monitoring Enabled by Piezoelectric Direct-Write Transducers and Edge Computing Process," Sensors, vol. 22, no. 15, Jul. 2022, Art. no. 5724.
P. Song et al., "Resource-Saving Customizable Pipeline Network Architecture for Multi-Signal Processing in Edge Devices," Sensors, vol. 22, no. 15, Jul. 2022, Art. no. 5720.
H. Ansaf, B. K. Ansaf, and S. S. Al Samahi, "A Neuro-Fuzzy Technique for the Modeling of β-Glucosidase Activity from Agaricus bisporus," BioChem, vol. 1, no. 3, pp. 159–173, Oct. 2021.
N. F. Idris and M. A. Ismail, "Breast Cancer Disease Classification Using Fuzzy-ID3 Algorithm with FUZZYDBD Method: Automatic Fuzzy Database Definition," PeerJ Computer Science, vol. 7, May 2021, Art. no. e427.
L. T. Giang et al., "Adaptive Spatial Complex Fuzzy Inference Systems with Complex Fuzzy Measures," IEEE Access, vol. 11, pp. 39333–39350, 2023.
N. Goyal, M. Dave, and A. K. Verma, "Fuzzy Based Clustering and Aggregation Technique for Under Water Wireless Sensor Networks," in 2014 International Conference on Electronics and Communication Systems, Coimbatore, India, Feb. 2014, pp. 1–5.
A. M. Pandith et al., "Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs," Computers, Materials & Continua, vol. 83, no. 1, pp. 259–279, 2025.
S. Hadiyoso, A. Z. Ramdani, I. D. Irawati, and I. Wijayanto, "Implementation of First Order Statistical Processor on FPGA for Feature Extraction," International Journal of Reconfigurable and Embedded Systems, vol. 13, no. 2, Jul. 2024, Art. no. 234.
A. Rizal, S. Hadiyoso, and A. Z. Ramdani, "FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN," Electronics, vol. 11, no. 19, Sept. 2022, Art. no. 3026.
V. B. Bhandari, Design of Machine Elements, 5th ed. New Delhi, India: McGraw-Hill Education (India), 2021.
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Copyright (c) 2026 Ahmad Sabiq, Jazi Eko Istiyanto, Andi Dharmawan, Rachmat Sriwijaya
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