Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

Abstract

In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.

In situ investigation of the influence of varying load conditions on tooth deformation and wear of polymer gears

“…Different studies investigated the correlation of changes in mechanical and acoustic vibration signals and certain types of gear failure such as pitting [18,19], formation of cracks [18,20] and wear of the tooth flank [18,21]. The use of other sensors like thermographic cameras [22] and oil wear debris sensors [16] is less common. However, condition monitoring test rigs do not consider the underlying process of gear failure.…”

Thermal Imaging Study to Determine the Operational Condition of a Conveyor Belt Drive System Structure

“…The use of thermal imaging is a very important and useful research method because, as a method for object diagnosis, it allows fast, safe and also accurate measurements in even restrictive space [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71]. In a deep mine environment, cameras can be used to work in smoky, dusty and dark environments.…”

Tracking changes in grinding wheel condition using reduced features of acoustic emission signals

“…PCA (Principal Component Analysis) and LDA are commonly used methods for dimensionality reduction in machine learning. [31][32][33][34] Both of them are linear transformation techniques and the difference is that LDA is a supervised whereas PCA is unsupervised. PCA is a technique that finds the directions of maximal variance.…”