On-line fault detection & diagnosis of rotating machines using acoustic emission monitoring techniques

Abstract

On-line condition monitoring (CM) of heavy rotating machines plays an important role in industrial plants. It continuously provides the machine status which allows the detection of abnormalities and problems at incipient stages as well as the intervention of maintenance and production personnel at proper time to keep the plant running and to avoid serious accidents. Most of rotating machine failures is due to bearing faults. The ability to predict the bearing failure at early stage is of great importance. This paper discusses the concept of acoustic emission (AE) monitoring techniques, in which signal processing measurements are used to create a simple integrated structure for the integration of condition monitoring and real-time information management of systems. This allows AE signals with frequency range 100 KHz - 1 MHz to be processed and analyzed using advanced signal processing and data analysis techniques. The effectiveness for AE monitoring system for early detection of healthy bearing is conducted. A system that is being developed to provide a test-bed for this concept is described.