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Purpose In rotary machines, the bearing failure is one of the major causes of the breakdown of machinery. The bearing degradation monitoring is a great anxiety for the prevention of bearing failures. This paper aims to present a combination of the stationary wavelet decomposition and extra-trees regression (ETR) for the evaluation of bearing degradation. Design/methodology/approach The higher order cumulants features are extracted from the bearing vibration signals by using the stationary wavelet decomposition (stationary wavelet transform [SWT]). The extracted features are then subjected to the ETR for obtaining normal and failure state. A dominance level curve build using the dissimilarity data of test object and retained as health degradation indicator for the evaluation of bearing health. Findings Experiment conducts to verify and assess the effectiveness of ETR for the evaluation of performance of bearing degradation. To justify the preeminence of recommended approach, it is compared with the performance of random forest regression and multi-layer perceptron regression. Originality/value The experimental results indicated that the presently adopted method shows better performance for detecting the degradation more accurately at early stage. Furthermore, the diagnostics and prognostics have been getting much attention in the field of vibration, and it plays a significant role to avoid accidents.

Recently, the diagnostics and prognostics have been getting much attention in the field of vibration and play a significant role in avoiding accidents. In rotary machines, bearing failure is one of the major causes for a shutdown. The health conditions of rotary machines can be monitored through the vibration signal. Health condition indicators are needed to highlight with proper representation of fault feature for bearing prognostics. Hence, in the present paper, the vibration signature analysis of bearing has been attempted. For that purpose, in this paper, an approach of Hamiltonian theory for quantum harmonic oscillator is used for the evaluation of degradation feature from an original feature of accelerometer signals of the bearing. The main focus of this paper is to study the progress of the prognosis of degradation feature using k-Nearest Neighbours, support vector machine and decision tree. An experimental investigation on ball bearing has been conducted to see the effectiveness of the k-Nearest Neighbours, support vector machine, and decision trees by applying the acquired vibration signals. Experimental results are compared, which indicated an accurate prediction of bearing degradation and reserve information of bearing prognosis and severities.

Recently, the prognostic is much attention in the field of vibration-based bearing monitoring and it plays a significant role to avoid accidents. In rotary machines, the bearing failure is one of the major causes of machinery shutdown. The bearing degradation monitoring is a great concern for prevention of bearing failures. This paper presents an approach for the bearing degradation evaluation based on empirical mode decomposition and k-medoids clustering. The bearing fault features are extracted from vibration data using an intrinsic mode function of empirical mode decomposition process. The extracted features are then subjected to k-medoids clustering for obtaining normal and failure state. Assurance values curve, which is based on dissimilarity data of test object to the normal state is found and retained as degradation indicator for evaluation of bearing health. Experiment was conducted to verify and assess the effectiveness of proposed method for the evaluation of performance of bearing degradation. To justify the preeminence of recommended approach, the root mean square and kurtosis features of time domain, envelope analysis of diagnosis method, and degradation assessment classifiers, i.e. simplified fuzzy adaptive resonance theory map are commonly used in the bearing analysis compared with the proposed method. Early stage detection of degradation more accurately, the recommended method is better than the time-domain features and simplified fuzzy adaptive resonance theory map based on performance degradation assessment on bearing. Moreover, envelope analysis can be used to verify the early stage defect detected by the proposed method. In this study, it has been seen that the k-medoids clustering is an efficient tool to assess the performance of degradation of bearings.

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