Comparison of Wireless Technologies for Rotating Machinery Diagnostics

Author(s):  
Cezary Worek ◽  
Łukasz Krzak ◽  
Rafał Mrówka ◽  
Tomasz Barszcz
1995 ◽  
Vol 1 (3-4) ◽  
pp. 237-266 ◽  
Author(s):  
Agnes Muszynska

This paper outlines rotating machinery malfunction diagnostics using vibration data in correlation with operational process data. The advantages of vibration monitoring systems as a part of preventive/predictive maintenance programs are emphasized. After presenting basic principles of machinery diagnostics, several specific malfunction symptoms supported by simple mathematical models are given. These malfunctions include unbalance, excessive radial load, rotor-to-stator rubbing, fluid-induced vibrations, loose stationary and rotating parts, coupled torsional/lateral vibration excitation, and rotor cracking. The experimental results and actual field data illustrate the rotor vibration responses for individual malfunctions. Application of synchronous and nonsynchronous perturbation testing used for identification of basic dynamic characteristics of rotors is presented. Future advancements in vibration monitoring and diagnostics of rotating machinery health are discussed. In the Appendix, basic instrumentation for machine monitoring is outlined.


2016 ◽  
Vol 5 ◽  
pp. 1107-1118 ◽  
Author(s):  
Haedong Jeong ◽  
Seungtae Park ◽  
Sunhee Woo ◽  
Seungchul Lee

2001 ◽  
Author(s):  
Jen-Yi Jong ◽  
Wade Dorland ◽  
Tony Fiorucci ◽  
Thomas Zoladz ◽  
Tom Nesman

Author(s):  
Blazˇ Suhacˇ ◽  
Jozˇe Vizˇintin ◽  
Pavle Bosˇkoski ◽  
Dani Juricˇic´

Rotating machines are one of the most wide spread items of equimpnet in the industrial plants; hence the reliable operation is of great practical importance. Analyses show that when a run-to-failure philosophy is adopted in rotating machinery maintenance, their downtime is usually three to four times longer comparing to a periodic or proactive maintenance approach. A successful proactive maintenance program requires an integration of several diagnostic procedures into an intelligent data processing system. Such a system allows detection of a broad range of faults in an early stage. The main aim of this paper is to present current results of our development of an intelligent rotating machinery diagnostics program for detecting a broad range of faults from signals which can be measured non-destructively and on-line. The main motivation is to develop computationally efficient algorithm that can be implemented on a standard (low-cost) platform. In that respect we have developed a test rotating machine equipped with accelerometers, temperature sensors and sensors for lubricating oil characterization. In this paper we focus on gear-box faults and a feature extraction procedure based on non-parametric statistical concepts as suggested and demonstrated on experimental data.


2002 ◽  
Author(s):  
Bob Grissom ◽  
Charles T. Hatch ◽  
Donald E. Bently

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