Next Article in Journal
Proof-of-Principle of a Cherenkov-Tag Detector Prototype
Next Article in Special Issue
Automated Calibration System for Digital Multimeters Not Equipped with a Communication Interface
Previous Article in Journal
Reliable UHF Long-Range Textile-Integrated RFID Tag Based on a Compact Flexible Antenna Filament
Previous Article in Special Issue
Research on the Single-Value Indicators for Centrifugal Pump Based on Vibration Signals
Open AccessLetter

Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors

1
Beijing Institute of Technology, Zhuhai 519088, China
2
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, West Yorkshire HD1 3DH, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3436; https://doi.org/10.3390/s20123436
Received: 2 May 2020 / Revised: 8 June 2020 / Accepted: 12 June 2020 / Published: 18 June 2020
As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation of the temperature distribution, which provides the possibility to distinguish the fault type of reciprocating compressors by differentiating the distribution using infrared thermal imaging. In this paper, three types of common fault are laboratory experimented in an uncontrolled temperature environment. The temperature distribution signals of a reciprocating compressor are captured by a non-contact infrared camera remotely in the form of heat maps during the experimental process. Based on the temperature distribution under baseline condition, temperature fields of six main components were selected via Hue-Saturation-Value (HSV) image as diagnostic features. During the experiment, the average grayscale values of each component were calculated to form 6-dimension vectors to represent the variation of the temperature distribution. A computational efficient multiclass support vector machine (SVM) model is then used for classifying the differences of the distributions, and the classification results demonstrate that the average temperatures of six main components aided by SVM is a promising technique to diagnose the faults of reciprocating compressors under various operating conditions with a classification accuracy of more than 99%. View Full-Text
Keywords: reciprocating compressors; fault diagnosis; thermal imaging; support vector machines (SVM) reciprocating compressors; fault diagnosis; thermal imaging; support vector machines (SVM)
Show Figures

Figure 1

MDPI and ACS Style

Deng, R.; Lin, Y.; Tang, W.; Gu, F.; Ball, A. Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors. Sensors 2020, 20, 3436.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop