Fault Diagnosis of Bearings in Rotating Machinery Based on Vibration Power Signal Autocorrelation

• Main Authors: Sadoughi, A., Tashakkor, S., Ebrahimi, M., Rezaei, E.
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural networks; Autocorrelation; Bearing; Condition monitoring; Data mining; Diagnosis; fault; Fault detection; Fault diagnosis; Frequency; Intelligent; Machinery; Shafts; Signal analysis; Vibration
• DOI: 10.1109/SICE.2006.314734

Since fault in a great number of bearings commences from a single point defect, research on this category of faults has shared a great deal in predictive diagnosis literature. Single point defects will cause certain characteristic fault frequencies to appear in machine vibration spectrum. In traditional methods, data extracted from frequency spectrum has been used to identify damaged bearing part. Because of impulsive nature of fault strikes, and complex modulations present in vibration signal, a simple spectrum analysis may result in erroneous conclusions. When a shaft rotates at constant speed, strikes due to a single point defect repeat at constant intervals. Each strike shows a high energy distribution around it. This paper considers the time intervals between successive impulses in auto-correlated vibration power signals. The most frequent interval between successive impulses determines the period of defective part. This period is related to fault frequency and therefore shows the defective part. A comparison of results extracted from the traditional and the proposed methods shows the efficiency improvement of the second method in respect of the first one