Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS) value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis.

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Gearbox Fault Identification and Classification with Convolutional Neural Networks

One of the earliest documented applications of acoustic emission technology (AET) to rotating machinery monitoring was in the late 1960s. Since then, there has been an explosion in research- and application-based studies covering bearings, pumps, gearboxes, engines, and rotating structures. In this paper we present a comprehensive and critical review to date on the application of AET to condition monitoring and diagnostics of rotating machinery.

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Development of Acoustic Emission Technology for Condition Monitoring andDiagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and RotatingStructures.

This study concerns with fault diagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various types of bearing defects associated with shaft speeds as low as 10rpm under several loading conditions. The data was acquired from the low speed bearing test rig using acoustic emission (AE) and accelerometer sensors under a constant load with different speeds. The aim of this study is to address the problem of detecting an incipient bearing fault and to find reliable methods for low speed machine fault diagnosis. In this paper, two methods of multi-class classification techniques for fault diagnosis through RVM and SVM are presented and the effectiveness of using AE and vibration signals due to low impact rate for low speed diagnosis. In the present study, component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. The classification for fault diagnosis was also conducted using original data feature and without feature extraction. The result shows that multi-class RVM produces promising results and has the potential for use in fault diagnosis of low speed machine.

Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

https://core.ac.uk/download/pdf/141066.pdf

A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears

Acoustic emission (AE) is gaining ground as a non-destructive technique for health diagnosis on rotating machinery. There are vast opportunities for development of the AE technique on various forms of rotating machinery, including gearboxes. This paper reviews some recent developments in application of AE to gear defect diagnosis. Furthermore, an experimental investigation that examines the effectiveness of AE for gear defect identification is presented. It is concluded that application of the AE technique to seeded gear defect detection is fraught with difficulties. In addition, the viability of the AE technique for gear defect detection from non-rotating components of a machine is called into question.

Application of acoustic emission to seeded gear fault detection

It is widely recognised that acoustic emission (AE) is gaining ground as a non-destructive technique (NDT) for health diagnosis on rotating machinery. The source of AE is attributed to the release of stored elastic energy that manifests itself in the form of elastic waves that propagate in all directions on the surface of a material. These detectable AE waves can provide useful information about the health condition of a machine. This paper reports on part of an ongoing experimental investigation on the application of AE for gear defect diagnosis. Furthermore, the possibility of monitoring gear defects from the bearing casing is examined. It is concluded that AE offers a complimentary tool for health monitoring of gears.

Tim Toutountzakis

Papers

Application of acoustic emission to seeded gear fault detection

Observations of acoustic emission activity during gear defect diagnosis