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

• Published in: Applied sciences 2020-01, Vol.10 (2), p.506
• Main Authors: Resendiz-Ochoa, Emmanuel, Saucedo-Dorantes, Juan J., Benitez-Rangel, Juan P., Osornio-Rios, Roque A., Morales-Hernandez, Luis A.
• Format: Article
• Language: English
• Subjects: Acoustics; Artificial intelligence; Classification; Data processing; Deep learning; Discriminant analysis; Downtime; Fault diagnosis; gearbox; Gears; Industrial equipment; Information processing; Infrared analysis; Infrared imaging; infrared thermography; Machinery; Neural networks; sensor; Sensors; Signal processing; Supervised learning; Support vector machines; Thermal analysis; Thermal imaging; Thermography; Vibration; Vibration analysis; Vibration monitoring; Wavelet transforms; Wear
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10020506

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.