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. 2024 Aug 14;24(16):5263.
doi: 10.3390/s24165263.

Impact of Measurement Uncertainty on Fault Diagnosis Systems: A Case Study on Electrical Faults in Induction Motors

Simone Mari  1 , Giovanni Bucci  1 , Fabrizio Ciancetta  1 , Edoardo Fiorucci  1 , Andrea Fioravanti  1
Affiliations

Impact of Measurement Uncertainty on Fault Diagnosis Systems: A Case Study on Electrical Faults in Induction Motors

Simone Mari et al. Sensors (Basel). .

Abstract

Classification systems based on machine learning (ML) models, critical in predictive maintenance and fault diagnosis, are subject to an error rate that can pose significant risks, such as unnecessary downtime due to false alarms. Propagating the uncertainty of input data through the model can define confidence bands to determine whether an input is classifiable, preferring to indicate a result of unclassifiability rather than misclassification. This study presents an electrical fault diagnosis system on asynchronous motors using an artificial neural network (ANN) model trained with vibration measurements. It is shown how vibration analysis can be effectively employed to detect and locate motor malfunctions, helping reduce downtime, improve process control and lower maintenance costs. In addition, measurement uncertainty information is introduced to increase the reliability of the diagnosis system, ensuring more accurate and preventive decisions.

Keywords: anomaly detection; artificial neural networks (ANNs); fault diagnosis; induction motors; measurement uncertainty.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Maintenance models and intervention times.
Figure 2
Figure 2
Common faults in induction motors.
Figure 3
Figure 3
The different damage conditions analyzed: (a) short circuit between winding turns, (b) short-circuit ring damage, (c) rotor bar damage.
Figure 4
Figure 4
Vibration measurements acquired along the x, y, and z axes with their respective harmonic content.
Figure 5
Figure 5
Implemented artificial neural network architecture.
Figure 6
Figure 6
Error matrices obtained for training the ANN with measurements acquired, along the x-axis (left) and y-axis (right).
Figure 7
Figure 7
Model accuracy as a function of number of neurons for different configurations of hidden layers.
Figure 8
Figure 8
Impact of uncertainty on classification accuracy. On the left, the uncertainty bands overlap, resulting in an otherwise incorrect classification that is not possible. On the right, the uncertainty bands do not overlap, thus confirming the correctness of the classification.
See this image and copyright information in PMC

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