Condition monitoring of critical mechanical elements through Graphical Representation of State Configurations and Chromogram of Bands of Frequency

Abstract

Fault detection is a crucial aspect to avoid catastrophic failures on mechanical systems, as well as to save money for companies. Currently, a number of non-destructing tests, signal processing analysis and artificial intelligence techniques are used for processing larger and larger amounts of maintenance data in all industry fields, either independently or combined. This manuscript presents a novel methodology for the condition monitoring of machinery, based on vibration analysis. The methodology is supported on two novel signal processing techniques: Graphical Representation of State Configurations (GRSC) and Chromogram of Bands of Frequency (CBF). These two new techniques apply basic concepts of the machine deterioration theory to the frequency spectrum. In order to prove the successful of the work presented, the methodology is tested against two real examples: vibration signals from the Case Western Reserve University (CWRU) Bearing Data Centre, and vibration signals from a high-speed train in normal operation. The results show that these new techniques can process large amounts of data without using artificial intelligence, identify adequately the operating condition of the tested systems and give precise information about that operating system by means of simple graphs and colours.