Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

Lifetime Based Health Indicator for Bearings using Convolitional Neural Networks

Master's thesis Renewable Energy ENE500 - University of Agder 2019Out of all the components in rotating electrical machinery, bearings have the highest failure rate. Bearingdegradation is a seemingly random process which is hard to both model and predict. Countless of con-dition based methods and algorithms have been proposed in order to accurately diagnose incipient faultsand estimate the remaining useful lifetime of bearings. These methods are often complex and hard to im-plement. In this thesis, a data-driven method of estimating a linear lifetime based health indicator (HI)using convolutional neural networks (CNNs) is proposed. The idea behind the method is to train a CNNmodel to recognize the shapes and distributions of vibration data in order to predict a HI with minimalpre-processing. Two models are presented: A CNN that takes time-series vibration data as input and aCNN that takes vibration frequency spectrum data as input. Finally, HIs are predicted on unique datasetsand their respective remaining useful lifetimes (RULs) are estimated as part of the model validation process.The results show that the models are able to recognize relevant fault features to a certain degree. However, accurate predictions have proven difficult in many cases

Data modelling and Remaining Useful Life estimation of rolls in a steel making cold rolling process

The economic cost of roll refurbishment in the steel-making industry is considerable. In a cold rolling mill, wear and damage of rolls disrupt the industrial environment, so it is critical to predict the remaining useful life early and change the roll without causing disruption to the manufacturing process. However, since cold rolling is a complex process affected by multiple variables which are operated in adverse conditions, it is very challenging to mathematically analyse the roll wear and failure. For this reason, in the present paper, a data-driven solution is proposed to predict the correct time for changing individual rolls. To develop an accurate predictive model, several datasets containing high-resolution production data and roll refurbishment data collected from a UK based steel plant have been acquired and processed in a way that the roll wear is modelled as a Remaining Useful Life (RUL) problem, where the number of coils that a roll is able to process is viewed as the remaining cycles. Then hybrid deep learning models are used to predict the Remaining Useful Life of rolls used in steel making. This novel data-driven approach achieves high prediction accuracy and has been validated on a real-world dataset. The proposed approach not only helps avoiding early failure but also can serve as a critical step towards the design of an optimal, automated maintenance schedule for the roll management

Bibliometric Analysis of Bearing Fault Detection using Artificial Intelligence

The new industrial revolution called Industry 4.0 is proliferating at its peak. The time is no longer away when the human race is going to witness a huge paradigm shift. Intelligent machines empowered by Artificial Intelligence (AI)will take over the presence of human workers in the industrial manufacturing sector with the target of achieving 100% automation. With the emergence of cut-throat price competition in the product market, it has become equally important to manufacture goods at minimal costs and with the highest quality. Predicting the decrease in machinery efficiency at an earlier stage to accomplish this objective helps to reduce the failure of the system earlier and at a lower cost. As most of these machineries constitute of bearings, early fault detection in bearings has always been a major goal for the manufacturing industry. Recently researchers have explored the power of AI for fault diagnostics in bearings and it has shown promising results. Therefore, in this paper, the authors present an extensive bibliometric study of the research carried out for fault detection in bearings using Artificial Intelligence. The study focuses on 4314 extracted literature from Scopus database in the form of scientific documents such as journals, articles, book chapters over a period of 2010-2020. This paper will give an in-depth view of the research trends in the domain of bearing fault detection