AE法による回転機異常診断システム

抄録

This paper describes the development of a machine condition diagnosis system using acoustic emission (AE) techniques. The system is applicable to rotating machinery such as steam turbines, generators and rolling mills.
The basic algorithm of the system discriminates between various abnormal conditions of machines using the relationship between AE waveform characteristics and frequency characteristics of an envelope detection signal of the AE. In order to analyze the waveform characteristics, the following waveform parameters are calculated: mean value, AE event, duration time, rise time, and AE energy. The frequency characteristics of the envelope detection signal of the AE are obtained by fast Fourier transform (FFT) calculations. Using the results of the waveform analysis, the AE signal is identified as either a continuous type or a burst type. Then using the results of the frequency analysis, the AE signal is identified as either a wide band type or a narrow band type, and the latter is further classified as either a rotation tuned type or an untuned type. These categorize the AE signal by six types of abnormal conditions.
Suitable application software for a diagnosed object are provided from rubbing diagnosis, rotor crack diagnosis and journal bearing diagnosis software which were developed using the algorithm described above. Thus, abnormal conditions and source locations can be monitored in detail. The system uses an interlocutive form with the operators, who can use graphic, data, or trend displays for the diagnosed results.