The Dynamic Evolution Modeling of the Bearings State Change (original) (raw)
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Model-based prognosis for rolling element bearings
2013
Rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime. Rolling contact wear is responsible for the damages initiating on and beneath the contacting surfaces of rolling element bearings. Wear prognosis aims to predict the defect and its evolution progress before it occurs. It addresses the use of automated methods to predict the degradation of physical system performance and the remaining lifetime. Actually, the fundamental need for predictive intelligence tools is to monitor the degradation rather than just detecting the defects, otherwise it will be hard to optimise the asset utilisation in cost effective manner. The current prognosis models are based on predetermined probabilistic damage functions or constant damage factors. Therefore, the purpose of this paper is to develop a prognosis model which is able to predict the evolution of rolling contact wear, using systems dynamics approach. Instead of pre-determined damage functions,the dynamic development of wear process is proposed. The dynamic development of wear considers multiple wear mechanisms and their interactions with respect to surface topological and tribological changes. Moreover, it considers the stress concentration mechanisms and their propagation processes. Therefore, the paper utilizes a five-stage model to simplify the dynamic development of wear progress over the lifetime. The five stages are running-in, steady-state, defect initiation (dentations, pits, and inclusions), defect propagation (extended pits, propagated cracks), damage growth (spalls). The paper is relevant in enhancing the effectiveness of prognostics procedures of rolling element bearing wear.
2020
The monitoring of condition variables for maintenance purposes is a growing trend amongst researchers and practitioners where decisions are based on degradation levels. The two approaches in Condition-Based Maintenance (CBM) are diagnosing the level of degradation (diagnostics) or predicting when a certain level of degradation will be reached (prognostics). Using diagnostics determines when it is necessary to perform maintenance, but it rarely allows for estimation of future degradation. In the second case, prognostics does allow for degradation and failure prediction, however, its major drawback lies in when to perform the analysis, and exactly what information should be used for predictions. This encumbrance is due to previous studies that have shown that degradation variable could undergo a change that misleads these calculations. This paper addresses the issue of identifying explosive changes in condition variables, using Control Charts, to determine when to perform a new model ...
Statistical Modeling of Bearing Degradation Signals
IEEE Transactions on Reliability
—Bearings are the most common mechanical components used in machinery to support rotating shafts. Due to harsh working conditions, bearing performance deteriorates over time. To prevent any unexpected machinery breakdowns caused by bearing failures, statistical modeling of bearing degradation signals should be immediately conducted. In this paper, given observations of a health indicator, a statistical model of bearing degradation signals is proposed to describe two distinct stages existing in bearing degradation. More specifically, statistical modeling of Stage I aims to detect the first change point caused by an early bearing defect, and then statistical modeling of Stage II aims to predict bearing remaining useful life. More importantly, an underlying assumption used in the early work of Gebraeel et al. is discovered and reported in this paper. The work of Gebraeel et al. is extended to a more general prognostic method. Simulation and experimental case studies are investigated to illustrate how the proposed model works. Comparisons with the statistical model proposed by Gebraeel et al. for bearing remaining useful life prediction are conducted to highlight the superiority of the proposed statistical model.
Luleå University of Technology , 2018
Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Prognostics is defined as the estimation of remaining useful life. It is the most critical part of this process and is a key feature of maintenance strategies since the estimation of the remaining useful life (RUL) is essential to avoiding unscheduled maintenance. Prognostics is relatively immature compared to diagnostics, and a challenging task facing the research community is to overcome some of the major barriers to the application of PHM technologies to real-world industrial systems. This thesis presents research into methods for addressing these challenges for industrial applications. The thesis work focuses on prognostic approaches for three different engineering systems with different characteristics in terms of the prognostics of operation and maintenance aspects. The aim of this thesis is to facilitate better operation and maintenance decision making. The main benefits of prognostics are in anticipating future failures to increase uptime, implementing dynamic maintenance planning toward decreasing total costs and decreasing energy consumption. Therefore, there is a need for methods that can be used in these cases to classify the health states and predict the remaining useful life of assets. The studied engineered systems in this thesis are railway tracks, batteries and rolling element bearings. In a railway system, the track geometry has to be maintained to provide a safe and functional track. Therefore, track degradation of ballasted railway track systems has to be measured on a regular basis to determine when to maintain the track by tamping. Tamping aims to restore the geometry to its original state to ensure an efficient, comfortable and safe transportation system. To minimise the disruption introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. In this thesis, a particle-filter-based prognostic approach for railway track degradation for railway switches is proposed. The particle-filter-based prognostic will generate a probabilistic prediction result that can facilitate risk-based decision making. Li-ion batteries are another important components in engineering system and battery life prediction matters. Li-ion batteries are commonly used in a wide range of consumer electronic devices, electric vehicles of all types, military electronics, maritime applications, astronaut suits, and space systems. Many critical operations depend on such batteries as a reliable power source. It is therefore important for the user to get an accurate estimate of the battery end of discharge because an unforeseen discharge of a battery could have catastrophic consequences. To address this issue, a Bayesian hierarchical model (BHM)-based prognostics approach was applied to Li-ion batteries, where the goal was to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. The operational and reliability aspects, end of life (EoD) and end of life (EoL), are studied; it is shown that predictions of the EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g., predicting performance of a new battery, the predictions are based only on the prior distributions describing the similarity within a group of batteries and their dependency on the load cycle. A discharge cycle dependency is identified helping with estimation of battery reliability. Batteries have become a very important engineering system, rotating machines have played an important role, possibly the most important role, in the field of engineering. They have been used to drive the industrialisation of the world. For rotating machinery, rolling element bearings are a vital component and have several failure modes. Hence, there is significant need to monitor the health of bearings and detect degraded states and upcoming failures as early as possible to avoid serious accidents and equipment failure. For rolling element bearings, an investigation in using FEM models for estimating bearing forces from acceleration measurements was conducted. This study was performed at a paper mill where a bearing monitoring system was installed. The purpose of the study was to feed the bearing rating life L10 (a bearing life length calculation) with estimations of the dynamic bearing forces to continuously update the L10 calculation by generating a dynamic L10. In a second study for bearing lifetime prediction, a Bayesian hierarchical modelling (BHM) approach , which includes different data sources, such as enveloped acceleration data, in combination with degradation models and prior distributions of other parameters, was developed, in which the bearing rating life calculation can be included. The proposed prognostics methodology can be used in cases where there is less or noisy data. The above approach can even be used in cases whereby there is no prior knowledge of the system or little measurement data on the conditions. The presented BHM approach can also be used to predict the remaining useful life (RUL) of bearings both in situations in which the bearing is considered to be in a healthy state and in situations after a defect has been detected.
Bearing failure diagnosis and prognostics modeling in plants for industrial purpose
Journal of Engineering and Applied Science
When condition-based maintenance (CBM) is combined with proper decision support systems, it leads to enhanced utilization of resources and increased productivity which tends towards business efficiency. The forecasting of the future condition, the remaining operating life, or probability of stable system behavior, based on data from acquired condition monitoring is referred to as prognosis which is an important part of the CBM process. Despite auto-regression integrated moving average (ARIMA) time series modeling, being long established and dating back to the 1960s, it has surged through new advances over the years and is now recognized as a major forecasting technique. Its application is therefore investigated here in the context of the FEMTO–ST Institute (Franche-Comté Électronique Mécanique Thermique et Optique-Sciences et Technologies) bearing dataset. The work discussed in this article uses a time series approach which contributes to modeling and forecasting the remaining usefu...
A preventive maintenance strategy for an actuator using Markov models
IFAC-PapersOnLine, 2020
This paper deals with a proactive maintenance strategy used to increase the reliability of the equipment. A predicting schedule of the renewal interventions is proposed so as to ensure optimal equipment maintenance. Hence, the goal is to find the optimal time which is the most profitable to carry out the equipment renewal operations. For the optimization of the maintenance, the preventive strategy is based on the average maintenance cost. The deterioration process is modeled by a Markov model, which is able to provide information about the tendency of the equipment state. The considered case study is an actuator, used in the sugar industry. A parametric form of the random behavior of the main variables was added to the Markov model, in the particular case of this actuator.