Abstract
Monitoring the condition of rotating machines is essential for the systems' safety, reducing maintenance costs, and increasing reliability. In this research, a fault detection system for bearings was developed using the vibration analysis technique with the statistical control chart approach. A test rig was first designed and constructed; then, various bearing faults, such as inner race and outer race faults, were simulated and examined in the test rig. After capturing the vibration signals at different bearing health conditions, the time-domain signal analysis technique was employed for extracting different indicative features. The obtained time domain features were then analyzed to find out the most fault-significant feature. Then, only one feature was selected to design the control chart for bearing health condition monitoring. The cumulative sum control chart (CUSUM was utilized since it can detect the small changes in bearing health states. The results showed the effectiveness of utilizing this method, and it was found that the percentage of the out-of-control points in the event of the combined cage and ball fault to the number of tested samples is greater than the other fault types.
Highlights
Rotating machinesare important for different industries, such as oil and gas industries, construction, and power plant industries.A catastrophic failure of rotating machinery would have a huge effect on the manu– facturing sector
Vibration analysis is necessary for analyzing structures and preventing failure. It contains information about the structure'smode shapes and natural frequencies, which are widely used for fault detection purposes[6,7,8].Time-domain and frequency-domain vibration techniques are used separately or in combination for bearing performance analysis. Features such as Root Mean Square, Crest Factor, Kurtosis, and others are used in the time-domain analysis, whereas the Fourier Transform technique is used in the frequencydomain analysis [9].control charts are a good example of a statistical process control system.The Shewhart X map is widely used for mean shift detection due to its ease of use.The exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) graphs are commonly used to detect relatively minor changes
Vibration signal analysis represents an effective technique that can be used for load variation and fault detection
Summary
Rotating machinesare important for different industries, such as oil and gas industries, construction, and power plant industries.A catastrophic failure of rotating machinery would have a huge effect on the manu– facturing sector. While vibration analysis is Vibration analysis is necessary for analyzing structures and preventing failure It contains information about the structure'smode shapes and natural frequencies, which are widely used for fault detection purposes[6,7,8].Time-domain and frequency-domain vibration techniques are used separately or in combination for bearing performance analysis. Features such as Root Mean Square, Crest Factor, Kurtosis, and others are used in the time-domain analysis, whereas the Fourier Transform technique is used in the frequencydomain analysis [9].control charts are a good example of a statistical process control system.The Shewhart X map is widely used for mean shift detection due to its ease of use.The exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) graphs are commonly used to detect relatively minor changes.
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