×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerBearing fault diagnosis is essential for the maintenance, durability, and reliability of rotating machines. It can minimize economic losses by removing unplanned downtime in the industry due to the failure of rotary machines. In bearing fault detection, developing fault features extraction techniques that can successfully applicable for various fault severity and different operating conditions is still a critical issue. In the current work, the feature extraction technique is a combination between pre-processing algorithms and envelope analysis method. In the pre- processing stage, the autoregressive (AR) model is used to filter the original signal and remove the deterministic vibration sources, as well as maintain the signal representing the condition of the bearing without contaminating noises. Then, the most suitable frequency band is selected based on the spectral kurtosis (SK) analysis. This band contains the signature frequencies of the roller bearing. After that, envelope analysis is employed for detecting faults at different severity. Finally, the features represented the peaks at fundamental fault frequencies are automatically selected from the envelope spectrum. By analyzing all diagnoses results, it is found that the presented method effectively extracts the features at calculated resonance bearing frequencies and proves the significance of the enhancements in a pre-filtering stage in the overall detection performance. Also, it can benefit from these features in the fault classification fields at different speeds because it is independent of speed variation.
Monitoring the health of rotating machinery is essential to ensure system safety, achieve cost savings, and enhance overall reliability. The requirement for a reliable and clear method of identifying defects has prompted the development of several monitoring techniques. They utilize vibration, measurement of the motor's current signature, and acoustic emission data in the process of condition monitoring. The MFS (machinery fault simulator) equipment was used to determine bearing faults using vibration signal analysis. MFS conducts simulations and investigations of many bearing issues, including those occurring in the inner race, outer race, and balls. An accelerometer (type B & K 4366) was connected to a data acquisition device (IDAC-6C) to record vibration signals under different operating conditions. Furthermore, a tachometer equipped with an LCD display is employed to measure the rotational speed. Four types of defects in ball bearing (Koyo 1205C3 type) were studied, the slot in outer race with size 0.196 mm, the slot in inner race with size 0.191 mm, in ball with size 0.196 mm in additional to compound defect. In this paper, spectral correlation technique was employed to detect defects in ball bearings running at varying speed, along with spectral coherence and the corresponding Enhanced Envelope Spectrum (EES) in frequency-order domain and order-order domain. The results show that the adopted methods, that are used to analyze the real vibration signals for diagnosis the defected ball bearings, are suitable, accurate and less processing time for varying speed. The processing time of the FastACP method used to analyze the signals in order- order domain is less than that of the adopted method in the frequency-order domain for any defect type. Overall, using the FastACP method in the order-order domain significantly reduced processing time by approximately 27% compared to the adopted method in the frequency- order domain under varying speed conditions.