# Diagnosing and Balancing Approaches of Bowed Rotating Systems: A Review
## 2 Materials and Methods
### Smart Inspection  
Using smart fault classification methods such as machine learning in rotor systems has increased sharply throughout the recent decade. Regression and classification are the two main methods that can be used to carry out this process. Furthermore, three techniques--supervised, unsupervised, and reinforcement--can be used to perform classification. Some features are necessary for all of these methods, regardless of whether users can manually enter these parameters or if the network can automatically extract the features. Take the principal component analysis (PCA) as an example [43].  
[PERSON] et al. [44] studied the effects of the initial bow on the longitudinal responses of a rotary system. The system response was evaluated in three scenarios: with different amounts of residual bow magnitude, disc eccentricity, and damping ratio. To investigate the effect of the fault more accurately, one of the shafts was placed motionless in the test rig for two years. Wavelet transformation and nonlinear manifold learning processes were used in extracting the fault features. ISOMAP, a nonlinear dimensionality reduction procedure, along with PCA, were employed to reduce the dimension of the collected signal, and also to isolate signals of unbalancing and shaft bow. The eccentricity effect was found to inhibit rotor vibration. When the eccentricity and damping ratio were small, the residual shaft bow could have a significant impact on rotor vibration. On the other hand, the impact was minimal. It was emphasized that the operating frequency is effective too. Prior to the resonance, the rotor longitudinal responses increased as the damping ratio decreased. The longitudinal responses of the rotors significantly decreased after the resonance.  
[PERSON] and [PERSON] [45] extracted six statistical features of time-domain signals that were investigated to classify unbalanced, misaligned, and bowed systems in the cases of single and/or multiple defects. An artificial neural network (ANN) and support vector machine (SVM) were used to process the extracted features. The results showed that the SVM took less time and effort to calculate.  
[PERSON] et al. [46] classified four different faults, i.e., misalignment, bow, blade erosion, and cracked case, in a steam turbine of a thermal power plant. A multilayer perceptron neural network was applied in three various scenarios, including feature extraction and dimension reduction utilizing PCA.  
[PERSON] et al. [47] used a residual convolutional neural network for the classification of malfunctioned rotary systems. Six health scenarios, i.e., normal, bowed, broken bar, faulty bearing, high impedance, and unbalanced rotor systems, were considered. The nonstationary parts of the vibration signals were segmented, then for each section, the act of feature extraction was performed by the network automatically.  
Considering the referenced articles, it can be understood that a wide spectrum of them investigated the impacts of a bowed shaft on the dynamic behavior of the complicated systems (with different supports or geared ones) rather than the simple Jeffcott rotor. Furthermore, the effects of a bowed shaft on the symptoms of other faults, such as disc unbalance, disc skew, and rubbing, have been discussed in some articles.