<div>
<p>Sound and vibration
analysis is a prominent tool used for scientific investigations in various
fields such as structural model identification or dynamic behavior studies. In
manufacturing fields, the vibration signals collected through commercial
sensors are utilized to monitor machine health, for sustainable and cost-effective
manufacturing.</p>
<p> Recently, the development of commercial
sensors and computing environments have encouraged researchers to combine
gathered data and Machine Learning (ML) techniques, which have been proven to
be efficient for categorical classification problems. These discriminative
algorithms have been successfully implemented in monitoring problems in
factories, by simulating faulty situations. However, it is difficult to
identify all the sources of anomalies in a real environment. </p>
<p>In this
paper, a Neural Network (NN) application on a KUKA KR6 robot arm is introduced,
as a solution for the limitations described above. Specifically, the autoencoder
architecture was implemented for anomaly detection, which does not require the
predefinition of faulty signals in the training process. In addition,
stethoscopes were utilized as alternative sensing tools as they are easy to
handle, and they provide a cost-effective monitoring solution. To simulate the normal
and abnormal conditions, different load levels were assigned at the end of the
robot arm according to the load capacity. Sound signals were recorded from
joints of the robot arm, then meaningful features were extracted from
spectrograms of the sound signals. The features were utilized to train and test
autoencoders. During the autoencoder process, reconstruction errors (REs) between
the autoencoder’s input and output were computed. Since autoencoders were
trained only with features corresponding to normal conditions, RE values corresponding
to abnormal features tend to be higher than those of normal features. In each
autoencoder, distributions of the RE values were compared to set a threshold, which
distinguishes abnormal states from the normal states. As a result, it is
suggested that the threshold of RE values can be utilized to determine the
condition of the robot arm.</p>
</div>
<br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8864375 |
| Date | 14 August 2019 |
| Creators | Hanjun Kim (6947996) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Machine_Anomaly_Detection_using_Sound_Spectrogram_Images_and_Neural_Networks/8864375 |
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