Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques

Syed Muhammad Tayyab,Paolo Pennacchi,Steven Chatterton

doi:10.3390/machines9080173

Syed Muhammad Tayyab, Paolo Pennacchi + Show 1 more

Open Access

https://doi.org/10.3390/machines9080173

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Journal: Machines	Publication Date: Aug 18, 2021
Citations: 13	License type: CC BY 4.0

Affiliation: Politecnico di Milano

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.

Highlights

In the present industrial era, early fault detections and correct fault severity level identifications of machines and their components are very important for their uninterrupted availability and to avoid any catastrophic failure
Different Artificial Intelligence (AI) models such as Artificial neural network (ANN) and K-nearest neighbors (KNN) use these features for training, and the same features are used for predictions
It has been observed that the performance of AI models for defect diagnosis is affected significantly when the models are deployed for fault detection and diagnosis under the operating conditions, which are different from those under which they were trained

Summary

Introduction

In the present industrial era, early fault detections and correct fault severity level identifications of machines and their components are very important for their uninterrupted availability and to avoid any catastrophic failure. Prediction of the defects in machines facilitates in performing timely maintenance of degraded or damaged component. A defective gear may cause serious problem in the machine’s operation and catastrophic failure in the case of damage. Early gearbox fault detections and the correct severity level identifications or diagnoses are very much important for the availability and smooth operation of machinery. Different techniques have been introduced by the researchers for accurate fault detection and diagnosis of gears, the technique of fault diagnosis by monitoring the vibrational signal is most widely used [2,3]. Vibration signal analyses in time domain, frequency domain and time-frequency domain have been extensively used by the researchers for gearbox faults diagnosis [4]

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Conclusion