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Real-Time Fault Identification System for a Retrofitted Ultra-Precision CNC Machine from Equipment's Power Consumption Data: A Case Study of an Implementation


Ability to detect faults in manufacturing machines have become crucial in the era of Smart Manufacturing to enable cost savings from erratic downtimes, in an effort towards Green Manufacturing. The power consumption data provides myriad of information that would facilitate condition monitoring of manufacturing machines. In this work, we retrofit an ultra-precision CNC machine using an inexpensive power meter. The data collected from the power meter were streamed in real-time to Amazon Web Services (AWS) servers using industry standard Message Query Telemetry Transport (MQTT) protocol. The error identification study was carried out in two-folds, we first identify if the error has occurred followed by classifying the type of controller error. The study also develops anomaly detection models to identify normal operating condition of the machine from the anomalous error states. Anomaly detection was particularly favorable for manufacturing machines as it requires data only from the normal operating conditions of the machine. The developed models performed with macro F1-Score of 0.9971 ± 0.0012 and 0.9974 ± 0.0018 for binary and multiclass classification respectively. The anomaly detection models were able to identify the anomalous data instances with an average accuracy of 95%. A feature importance study was then carried out to identify the most valuable feature for error identification. Finally, the trained models were containerized and hosted at AWS. The overarching goal of this project was to develop a complete inexpensive ML pipeline that would enable industries to detect operation anomalies in manufacturing machines just from the energy consumption data of the machine.

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Data availability

The complete codebase used this work has been published at for further development.


\({D}_{M}\left(x\right)\) :

Mahalanobis distance of the data instance \(x\)

\({K}_{h}(x-{x}_{i})\) :

Kernel function of choice for Kernel Density Estimation (KDE)

\({f}_{h}\left(x\right)\) :

Density estimate at a point \(x\)

\(h\) :

Bandwidth parameter, controls the smoothness of the estimated density distribution


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The material is based on the work supported by the Wisconsin Alumni Research Foundation (WARF, MSN237362). Authors gracefully acknowledge the donation of the ROBONANO α-0iB to the University of Wisconsin Madison by FANUC Corporation, Japan.

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Correspondence to Sangkee Min.

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Selvaraj, V., Min, S. Real-Time Fault Identification System for a Retrofitted Ultra-Precision CNC Machine from Equipment's Power Consumption Data: A Case Study of an Implementation. Int. J. of Precis. Eng. and Manuf.-Green Tech. 10, 925–941 (2023).

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