Bearings are one of the critical components in rotating machines and the majority of the failure arises from the defective bearings. Bearing failure leads to failure of a machine and unpredicted productivity loss for production facilities. Hence, bearing fault detection and diagnosis is an integral part of the preventive maintenance procedures. In this paper vibration signals for three conditions of a deep groove ball bearing Normal (N), defect on inner race (IR) and defect on outer race (OR) were acquired from a customized bearing test rig, under one load and two speed conditions. Discrete Wavelet Transform (DWT) has been used for vibration signal analysis. The statistical features extracted from the dominant wavelet coefficients are used as inputs to ANN classifier to evaluate its performance. The vibration signals have also been denoised using a new thresholding scheme. A comparison of ANN performance is made based on raw vibration data and denoised data. The ANN performance has been found to be comparatively higher when denoised signals were used as inputs to the classifier. Also various mother wavelet functions (Db8, Db4, Db44 and Sym10) were used to analyze the denoised vibration signals and their performance has been evaluated using the ANN classifiers

Airfoil noise due to pressure fluctuations impacts the efficiency of aircraft and has created significant concern in the aerospace industry. Hence, there is a need to predict airfoil noise. This paper uses the airfoil dataset published by NASA (NACA 0012 airfoils) to predict the scaled sound pressure using five different input features. Diverse Random Forest and Gradient Boost Models are tested with five-fold cross-validation. Their performance is assessed based on mean-squared error, coefficient of determination, training time, and standard deviation. The results show that the Extremely Randomized Trees algorithm exhibits the most superior performance with the highest Coefficient of Determination.

The surfaces produced by the machining process are sensitive to the type of machining process and the conditions under which it is performed. Thus, surface texture identification is crucial in quality assurance, as it acts as a feedback to the machining process. Machined surface identification using image processing and machine learning (ML)-based techniques is gaining much importance due to industrial automation. This investigation addresses the development of ML models using gray-level co-occurrence matrices (GLCM) features to classify the machined (turned, ground and shaped) surfaces. The influence of distance-based dimensionality reduction techniques (DRT) viz., Fisher's criterion, Separation index and Bhattacharya distance on the performance of the ML-based image classifiers is explored. The GLCM features extracted from the machined surface images are used as inputs to ML classifiers. A threshold criterion function (TCF) is used to select the sensitive features in the DRT. Among all the classifiers, the (Random Forest) RAF model could produce a better classification accuracy as high as 95.3%. Also, analysis results show that the proposed dimensionality reduction methodology with TCF effectively identifies the most sensitive features. A maximum dimensionality reduction of 62% is achieved. The proposed methodology showed a 7.2% improvement in classification accuracy over the techniques reported in the previous study. Thus, developed ML models successfully classify the machined surface images with a minimum time and computational burden on the computer.

This article uses the cluster dependent weighted fuzzy C-means based radial basis function neural network for comparing the different dimensionality reduction techniques for the fault diagnosis in the rolling element bearing. The vibration signals from normal bearing, bearing with defect on the inner race, and bearing with defect on the outer race were acquired under one radial load and two shaft speeds. These signals were subjected to the wavelet transform based denoising from which several time and frequency domain features were extracted. Dimensionality reduction techniques, namely, principal component analysis, Fisher’s criterion, and separation index, have been used to select the sensitive features. The selected features were used to train and test the radial basis function neural network, where the centers of the radial basis function units have been optimized by the cluster dependent weighted fuzzy C-means and the widths of the radial basis function units have been fixed by trial and error. Finally, a comparison of the dimensionality reduction techniques based on the radial basis function neural network performance is presented.