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 |  موضوع: بحث بعنوان Fault Detection and Severity Level Identification of Spiral Bevel Gears الخميس 15 سبتمبر 2022, 4:06 am | |
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بحث بعنوان
Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques
و المحتوى كما يلي :
Syed Muhammad Tayyab, Steven Chatterton * and Paolo Pennacchi
Department of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milan, Italy;
syedmuhammad.tayyab@polimi.it (S.M.T.); paolo.pennacchi@polimi.it (P.P.)
* Correspondence: steven.chatterton@polimi.it; Tel.: +39-02-2399-8442
Abstract: Spiral bevel gears are known for their smooth operation and high load carrying capability;
therefore, they are an important part of many transmission systems that are designed for high
speed and high load applications. Due to high contact ratio and complex vibration signal, their
fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears
have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial
Intelligence (AI) techniques have been used for fault detection and fault severity level identification
of spiral bevel gears under different operating conditions. Although AI techniques have gained much
success in this field, it is mostly assumed that the operating conditions under which the trained AI
model is deployed for fault diagnosis are same compared to those under which the AI model was
trained. If they differ, the performance of AI model may degrade significantly. In order to overcome
this limitation, in this research study, an effort has been made to find few robust features that
show minimal change due to changing operating conditions; however, they are fault discriminating.
Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both
models are trained and tested by using the selected robust features for fault detection and severity
assessment of spiral bevel gears under different operating conditions. A performance comparison
between both classifiers is also carried out.
Keywords: fault detection; fault severity level identification; artificial intelligence (AI); artificial
neural network (ANN); K-nearest neighbors (KNN); features extraction
Conclusions
In this study, fault detection and severity level identification of spiral bevel gears are
carried out under different operating conditions by using two AI models, ANN and KNN,
as classifiers. Time domain statistical features were extracted from the vibration data of
spiral bevel gears, one with normal health condition and two with faulty conditions at
different severity levels, in order to train the classifiers. The performance of both classifiers
in terms of fault detection and severity level identification accuracy gradually degraded as
the operating conditions under which the models were deployed for predictions deviated
farther away from the operating conditions under which the models were trained. The
performance degradation was due to the higher sensitivity of most of the features underconsideration towards the operating conditions. Variation in most of the features due to
operating conditions was much more prominent than compared to their change because
of the fault or fault severity level. Therefore, most of the features were misleading the
classifiers. The features were found more sensitive to change in speed than compared to
change in load. Three features (rms, Energy-I and Energy-II) were identified as robust features which showed least sensitivity to operating conditions but were fault discriminative
and demonstrated an increasing trend with respect to fault severity level. ANN and KNN
performed predictions with 100% accuracy under all operating conditions while using
only robust features. Thus, the performance of ANN and KNN classifiers was significantly
improved for fault detection and severity level identification of spiral bevel gears under
different operating conditions by eliminating misleading features, which were sensitive to
operating conditions, and selecting the robust features that are less sensitive to operating
conditions but were also fault discriminative. The overall performance of ANN and KNN
classifiers was found almost comparable to one another.
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