Surface Roughness of a machined component is crucial in identifying its functional capability when the manufactured specimen has metal to metal contact during operating condition since most wear and tear of the parts occurs due to friction between the surfaces of the moving parts. It is quite difficult to manually check the surface roughness of each component being manufactured on a manufacturing line. This paper aims to present a methodology to predict surface roughness using Image Processing, Computer Vision, and Machine Learning. Two machine learning algorithms Bagging Tree and Stochastic Gradient Boosting are compared and evaluated based on statistical parameters .It is observed that Stochastic Gradient Boosting predicts surface roughness in an efficient way both for training and Ten-fold cross-validation. The methodology used can be employed for online inspection and qualitative assessment of machined components.

Bearings are one of the crucial components of any machine having rotary parts. They are employed to support and ensure smooth operations of the shafts in the rotary machinery. Therefore, any fault in the bearings can lead to a decline in the level of production and equipment. For this reason, it is important to monitor the bearing health. This paper presents a signal analysis technique for machine health monitoring using the Hilbert-Huang Transform (HHT). HHT is a time domain approach which extracts instantaneous frequency data from a signal by decomposing the signal into Intrinsic Mode Functions (IMF) using the Empirical Mode Decomposition (EMD). The Least Absolute Shrinkage and Selection Operator (LASSO) is used as feature ranking method which is used to improve the prediction accuracy by reducing input data to machine learning model by aiding to select only a subset of the feature vector rather than using all of the features. In the present work, training and tenfold cross-validation accuracy or two classifiers have been compared. The comparative analysis presented in this paper reveals that the utilization of LASSO as a feature ranking method shows a substantial decrease in the data to be handled and improving the diagnosis accuracy.

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To reveal the machinery health condition, time-frequency analysis is an effective tool when signals are non-stationary. To identify bearing faults, numerous techniques have been proposed by various researchers. However, little research focused on image processing-based texture feature extraction for the identification of faults. The time-frequency image contains many sensitive fault information regarding bearing conditions, which can be extracted in the form of features. Therefore, in this paperwork, a methodology is proposed based on Fast Walsh Hadamard Transform (FWHT) time-frequency spectrogram, gray level co-occurrence matrix (GLCM), and machine learning techniques. A feature vector is constructed which consists of one dimension and two-dimension features extracted from Fast Walsh Hadamard Transform coefficients. To identify the fault conditions, LASSO-based feature ranking is applied to determine the suitable features. Finally, classifiers like Support vector machine (SVM), Random forest, and K-nearest neighbors (KNN) are evaluated for identifying bearing faults. Training, Testing, five-fold cross-validation performed on fusion feature vector. Results indicate that ranked fusion features are effective to diagnose bearing faults with good accuracy.