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Go to Editorial ManagerDiagnosing faults in rotary machines is critical, as early fault detection is a precise and essential task in minimizing operational risks and economic losses. Bearings are vital components in rotary machines and are subject to gradual degradation due to continuous operation. Failure to detect early damage can lead to problem escalation, resulting in severe damage and increased costs. In this study, two types of signals from rotary machines are analyzed: acoustic emission (AE) signals and vibration signals. These signals are utilized as input features for a deep learning neural network based on images, where the features are extracted using the Kurtogram, a powerful fourth-order spectral analysis tool that generalizes spectral kurtosis (SK) for a given signal. The results demonstrate that the accuracy of diagnosing the machine’s operational condition whether healthy or faulty ranges from 99.2% to 100%, while the accuracy of fault classification reaches 96.6%. These findings highlight the high efficiency of this methodology in fault detection and classification, establishing it as one of the most important techniques for diagnosing faults in rotary machines.
Vibration analysis is indispensable for different mechanical applications for early fault diagnosis, and many methods are used to analyze signals. Order tracking is one of these methods that is necessary for vibration analysis, especially for rotating machines. One critical and widely used method of order tracking is Vold-Kalman order tracking (VK-OT), which is used to diagnose faults in non-stationary machines. However, it has complicated and intensive calculations, requires special analysis tools, needs large memory, and takes a very long time to extract the results. The proposed method aims to analyze signals by using Vold-Kalman filter order tracking with shorter time and less calculation memory with high accuracy by using partitioning method that separates the signal into many blocks with overlaps. The proposed method achieved less processing time and need much smaller memory than the original Vold-Kalman filter-based methods.
Bearing 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.
The rotor unbalances and misalignment in rotary machines are two major sources of vibration. rotor unbalance and misalignment is omnipresent in all rotating machinery widely used in many industrial applications, posing a serious threat to machine life and operation. The present work is an attempt to investigate the vibration characteristics (Amplitude, FFT, and time waveform) of a rotating mechanical system, which has an unbalanced rotor and misalignment. Vibration signals are acquired using an accelerometer mounted on the bearing housing nearer to the rotor. The FFT analysis of the acquired data revealed the response of an unbalanced rotor under operating conditions. Numerical analysis of the system using ANSYS portrayed the modal frequencies and mode shapes. Transient Structural analysis illustrates the response of the system to different mass unbalances. The results revealed that the magnitude of vibration characteristics significantly increases with excitation frequency and exciting force.