# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 1 Introduction  
Rotating machines are critical assets and play an important role in industrial settings, from milling factories to refineries and power plants. These machines can be so important that a single failure can cause a cascade of negative impacts such as permanent damage to costly machines, factory shutdown, and worst, hurting some nearby workers. It is important to maintain rotating machines in good condition to ensure their performance and long lifetime. Rotating machines have been used in many areas and are heavily integrated into all areas of industry. These machines comprise of bearings and many other parts. As the machines rotate, wearing and misalignments are observed on the mechanical parts. Therefore, researchers have studied vibration and sound analysis to identify the faults. With the explosion of machine learning, it has found its path into fault analysis in rotating machines. There are many machine-learning application studies regarding rotating machinery health condition monitoring using various machine-learning techniques with data sets from different types of sensors. Researchers initially relied on signal processing techniques to identify the faults in the rotating machines using complex mathematical models, but with the increased popularity of Machine Learning (ML) techniques, researchers started to utilize algorithms such as K-Nearest Neighbor (KNN) or Support Vector Machines (SVM) and also Neural Networks. In a study presented in 2017, [PERSON] et al. proposed an automatic fault classifier by applying the Similiarity-Base Modeling to detect faults on rotating machines to obtain an accuracy of up to 96.4% and 98.7% on two different vibration data sets, which are the Case Western Reserve University Bearing Data (CWRUBD) and MaFuDa datasets [1]. In an earlier IEEE paper, [PERSON] et al. developed an enhanced diagnostic scheme with a neuro-fuzzy classifier that integrated several signal-processing methods. Using the proposedtechnique, the authors were able to detect a fault with an accuracy of up to 95.18% and 99.85% on their two available rotation data sets [2]. In another study, [PERSON] et al. state that they can detect faults up to 92.65% using discrete Wavelet Transform to analyze the vibration observed from accelerometers. Utilizing the multiresolution analysis and extracting the multiscale slope features to explore the sideband spectrum features of vibration [3]. In another study, [PERSON] et al. proposed to use generalized S transform and 2-D non-negative matrix factorization (2 DNMF) to introduce a time-frequency representation. The authors were able to reach up to 99.3% accuracy with the 2 DNFM model on the vibration data [4]. In another paper, [PERSON] et al. explored different models to extract discriminative heterogeneous features using complex envelop spectra, statistical time-frequency parameters and Wavelet Packet analysis. Upon feature extraction, ML models such as KNN and other ML models were used to classify the vibrations from the CWRUBD. The authors used the Area Under the Curve from the Receive Operating Characteristics (AUC-ROC) to gauge the performance [5]. In the study from another research group, [PERSON] et al. created a system with a prediction accuracy of 99% using the Multiscale Permutation Entropy (MPE) method, another technique to perform feature extraction. Upon feature extraction, the Support Vector Machine (SVM) was applied to automatically identify the bearings' faults [6]. In a recent study, [PERSON] et al. applied discriminant analysis using multi-view learning (DAML), and the authors proposed a DAML model, where multi-view refers to the analysis of vibration and frequency of the acoustic data. Using the KNN classifier, they achieved a diagnostic accuracy of up to 97% using data collected from eight accelerometers, a voice recorder, and a tachometer [7]. In a more recent study, [PERSON] et al.