Machinery components undergo wear and tear over time due to regular usage, necessitating the establishment of a robust prognosis framework to enhance machinery health and avert catastrophic failures. This study focuses on the collection and analysis of vibration data obtained from roller bearings experiencing various fault conditions. By employing a combination of techniques sourced from existing literature, distinct configurations within vibration datasets were examined to pinpoint the primary defects in roller bearings. The significant features identified through this analysis were utilized to formulate optimized stochastic model equations. These models, developed separately for inner and outer race fault features in comparison to healthy bearing features under random conditions, offer valuable insights into machinery prognosis. The application of these models aids in effective maintenance management, optimization of machinery performance, and the minimization of catastrophic failures and downtime, thereby contributing to overall machinery reliability.

Machinery components degrade over time due to continuous use. A reliable prognosis framework can improve machinery health by monitoring the behavior of its parts and providing warnings before critical failures occur. Bearings, which are essential components of rotating machinery, help maintenance personnel assess the machine’s condition during continuous wear. In this study, vibration data from roller bearings under various conditions and faults were collected. The Vibration analysis technique was employed to detect and classify different faults in bearings based on the characteristics of the vibration signals generated by the machinery. Faults can be detected, diagnosed, and classified by analyzing bearing vibration signatures using techniques such as frequency analysis, time-domain analysis, spectral analysis, and kurtogram classifiers. This enables appropriate maintenance actions to be taken in time, preventing further damage or failures.

Machinery parts gradually wear out over time due to regular usage. To improve machinery health and prevent critical issues, a reliable prognosis framework can be implemented by monitoring the behaviour of machinery parts and issuing warnings before they reach a critical state. To achieve this, vibration data from roller bearings experiencing various fault conditions have been collected. Different techniques from the literature were combined to analyze the distinct configurations in the vibration data sets and identify the main defects in roller bearings. The significant features extracted from this analysis were then used to create optimized stochastic model equations, separately regressing inner and outer race fault features to healthy bearing features under random conditions. These models can help engineers design more dependable systems, optimize their performance, and minimize the risk of failures and downtime.

Rolling element bearings are critical components in machinery that rotates, supporting radial and axial loads while facilitating smooth motion. Bearing malfunctions rank among the most frequent mechanical failures in rotating machinery, resulting in significant downtime and repair costs if not identified promptly. The Root Mean Square (RMS) value is a widely used statistical feature in Condition Monitoring (CM), providing a reliable and quantitative technique for detecting early-stage bearing faults. This study introduces a method for predicting machine health by utilizing RMS values derived from baseline vibration data of Healthy-Bearing (HB), Inner-Race-Faulty-Bearing (IRFB), and Outer-Race-Faulty-Bearing (ORFB) to explore the deterioration of roller bearings under various conditions using a statistical approach. The AutoRegressive Moving Average (ARMA) (p,q) model was employed to fit the extracted vibration features from bearing data to create a stochastic relationship between the healthy data and the inner race faulty data, as well as between the healthy data and the outer race faulty data. The model's accuracy of the predictive model is evaluated using an error matrix. This study adds to advancing data-driven prognostics in industrial machinery by merging statistical modeling with vibration-based condition monitoring.