• Ezzeldin Ayman Ibrahim Ismail Division of Control and Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Mohd. Ridzuan Ahmad Division of Control and Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia



Machine learning, maintenance, anomaly detection, rotary machine, microcontroller


The integration of machine learning solutions is becoming more prominent in the industry. In industrial maintenance, new approaches categorized under predictive maintenance primarily use machine learning to identify patterns that could lead to machine failures. However, in most cases, implementing a machine learning approach is very expensive regarding resources and experienced personnel. Therefore, this approach is usually more costly in some machines than replacing these faulty machines instead. This paper proposes a low-cost machine-learning approach to detect anomalies in a rotary machine by monitoring its casing temperature using EdgeImpulse to Train the model and a Raspberry Pico as the microcontroller. The project is divided into two phases. Data is collected to be used to train and test the model. The model is then deployed to the microcontroller and is connected to a sensor attached to the motor. The model developed showed promising results with an accuracy of 91% and a ƒ1 score of 0.91.


R. A. Câmara, H. S. Mamede, and V. D. d. Santos. 2019. Predictive Industrial Maintenance with a Viable Systems Model and Maintenance 4.0. 2019 8th International Conference On Software Process Improvement (CIMPS), 23-25 Oct. 2019. 1-8. Doi: 10.1109/CIMPS49236.2019.9082435.

J. Zenisek, F. Holzinger, and M. Affenzeller. 2019. Machine learning Based Concept Drift Detection for Predictive Maintenance. Computers & Industrial Engineering. 137: 106031. Doi:

J. Rabatel, S. Bringay, and P. Poncelet. 2011. Anomaly Detection in Monitoring Sensor Data for Preventive Maintenance. Expert Systems with Applications. 38(6): 7003-7015. Doi: 10.1016/j.eswa.2010.12.014.

S. K. Bose, B. Kar, M. Roy, P. K. Gopalakrishnan, and A. Basu. 2019. AdepoS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC. 597-602. Doi: 10.1145/3287624.3287716.

I. T. Christou, N. Kefalakis, A. Zalonis, and J. Soldatos. 2020. Predictive and Explainable Machine Learning for Industrial Internet of Things Applications. 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS), 25-27 May 2020. 213-218. Doi: 10.1109/DCOSS49796.2020.00043.

Q. Cao et al. 2022. KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0. Robotics and Computer-Integrated Manufacturing. 74: 102281. Doi: 10.1016/j.rcim.2021.102281.

P. Andrade et al. 2021. An Unsupervised TinyML Approach Applied for Pavement Anomalies Detection under the Internet of Intelligent Vehicles. 2021 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2021 - Proceedings. 642-647. Doi: 10.1109/MetroInd4.0IoT51437.2021.9488546.

F. Sakr, F. Bellotti, R. Berta, and A. De Gloria. 2020. Machine Learning on Mainstream Microcontrollers. Sensors (Switzerland). 20(9): 2638. Doi: 10.3390/s20092638.

H. Kayan, Y. Majib, W. Alsafery, M. Barhamgi, and C. Perera. 2021. AnoML-IoT: An End to End re-configurable Multi-protocol Anomaly Detection Pipeline for Internet of Things. Internet of Things (Netherlands). 16: 100437. Doi: 10.1016/j.iot.2021.100437.

L. Buonanno, D. Di Vita, M. Carminati, and C. Fiorini. 2020. A Directional Gamma-Ray Spectrometer with Microcontroller-Embedded Machine Learning. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(4): 433-443. Doi: 10.1109/JETCAS.2020.3029570.

C. Banbury et al. 2021. Micronets: Neural Network Architectures for Deploying Tinyml Applications on Commodity Microcontrollers. Proceedings of Machine Learning and Systems. 3.

B. Sudharsan et al. 2021. TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 14 June-31 July 2021. 883-884. Doi: 10.1109/WF-IoT51360.2021.9595024.

C. Banbury et al. 2021. MLPerf Tiny Benchmark.

A. Acernese, C. Del Vecchio, M. Tipaldi, N. Battilani, and L. Glielmo. 2021. Condition-based Maintenance: An Industrial Application on Rotary Machines. Journal of Quality in Maintenance Engineering. 27(4): 565-585. Doi: 10.1108/JQME-10-2019-0101.

S. Givnan, C. Chalmers, P. Fergus, S. Ortega-Martorell, and T. Whalley. 2022. Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors. Sensors (Basel). 22(9). Doi: 10.3390/s22093166.

X. Zhao, Z. Liu, T. Wang, J. Bin, and M. Jia. 2020. Unsupervised Fault Diagnosis of Machine via Multiple-Order Graphical Deep Extreme Learning Machine. 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), 20-23 Aug. 2020. 1-6. Doi: 10.1109/APARM49247.2020.9209447.

C. Lou and X. Li. 2018. Unsupervised Fault Detection based on Laplacian Score and TEDA. Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. 267-270. Doi: 10.1109/DDCLS.2018.8515956.

B. S. J. Costa, C. G. Bezerra, L. A. Guedes, and P. P. Angelov. 2015. Online Fault Detection based on Typicality and Eccentricity Data Analytics. 2015 International Joint Conference on Neural Networks (IJCNN), 12-17 July 2015. 1-6. Doi: 10.1109/IJCNN.2015.7280712.

T. K. Nguyen, I. Azarkh, B. Nicolle, G. Jacquemod, and E. Dekneuvel. 2018. Applying NIALM Technology to Predictive Maintenance for Industrial Machines. 2018 IEEE International Conference on Industrial Technology (ICIT), 20-22 Feb. 2018. 341-345. Doi: 10.1109/ICIT.2018.8352201.

H. Lu, Y. Li, S. Mu, D. Wang, H. Kim, and S. Serikawa. 2018. Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning. IEEE Internet of Things Journal. 5(4): 2315-2322. Doi: 10.1109/JIOT.2017.2737479.



How to Cite

Ibrahim Ismail, E. A., & Ahmad, M. R. (2023). ANOMALY DETECTION IN THE TEMPERATURE OF AN AC MOTOR USING EMBEDDED MACHINE LEARNING. Jurnal Teknologi, 85(6), 67-73.



Science and Engineering