An Adaptive Modeling Framework for Bearing Failure Prediction

Abstract

Rolling element bearings are a common component in rotating equipment, a class of machines that is essential in a wide range of industries. Detecting and predicting bearing failures is then vital for reducing maintenance and production costs due to unplanned downtime. In previous literature, significant efforts have been devoted to building data-driven health models from historical bearing data. However, a common limitation is that these methods are typically tailored to specific failure instances and have limited ability to model bearing failures between repairs in the same system. In this paper, we propose a multi-state health model to predict bearing failures before they occur. The model employs a regression-based method to detect health state transition points and applies an exponential random coefficient model with a Bayesian updating process to estimate time-to-failure distributions. A model training framework is also introduced to make our proposed model applicable to more bearing instances in the same system setting. The proposed method has been tested on a publicly available bearing prognostics dataset. Case study results show that the proposed method provides accurate failure predictions across several system failures, and that the training approach can significantly reduce the time necessary to generate an effective, generalized model.

Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions

“…The academic research community has identified many PHM-related tasks [19,20], which will be referred to as PHM capabilities. Several PHM capabilities are defined in [21,22], including detecting ongoing equipment degradation, diagnosing the root cause of degradation, and predicting when maintenance will be necessary. General anomaly detection and repair quality assessment are other examples of PHM capabilities that may be enabled.…”

Smart Quantum Tunneling Composite Sensors to Monitor FKM and FFKM Seals

“…Therefore, a point exists where optimum cost savings can be achieved by scheduling equipment maintenance at the ideal point. Broadly, predictive maintenance models have been developed in an attempt to find this point [ 34 , 35 , 36 ] with the ultimate goal of enhancing yield, reducing unplanned downtime, and general improvement of manufacturing efficiency.…”

“…One common approach to implementing predictive maintenance is to ‘sensorize’ equipment in an attempt to connect specific sensor output to critical performance metric information. For example, the use of vibration sensors to measure bearing performance inside a motor, where an optimal sensor location is found, and time-series vibration signals are analyzed to determine the point-of-failure of the bearings in industrial applications [ 34 , 36 ]. One other such example for maintenance of seals is a slit-valve door (BSV) seal described in a U.S. Patent [ 37 ], which is based on the use of sensors positioned on various locations of a slit valve door seal (Bonded Slit Valve, also known as BSV seal) that used external strain sensor data attached to the sealing product (along with other factors) to understand product lifetime, thus creating a seal lifetime monitoring system.…”

Research on defect detection method of bearing dust cover based on machine vision and multi-feature fusion algorithm

“…As an important part of the machinery manufacturing industry, bearings are applied to connect the rotating mechanisms and reduce friction damage between agents [1,2]. It is well known that bearing defects are one of the most common sources of failure in induction motors [3,4].…”