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

Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acoustic emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the remaining useful life (RUL) of machine components is required. This model is used to analyze the output signals of a machine whilst in operation and accordingly helps to set an early alarm tool that reduces the untimely replacement of components and the wasteful machine downtime. Recent improvements indicate the drive on the way towards incorporation of prognosis and diagnosis machine learning techniques in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; support vector machine regression, multilayer artificial neural network model and gaussian process regression, to correlate AE features with corresponding natural wear of slow speed bearings throughout series of laboratory experiments. Analysis of signal parameters such as signal intensity estimator and root mean square was undertaken to discriminate individual types of early damage. It was concluded that neural networks model with back propagation learning algorithm has an advantage over the other models in estimating the RUL for slow speed bearings if the proper network structure is chosen and sufficient data is provided.Peer reviewe

Machine Prognosis with Full Utilization of Truncated Lifetime Data

Intelligent machine fault prognostics estimates how soon and likely a failure will occur with little human expert judgement. It minimizes production downtime, spares inventory and maintenance labour costs. Prognostic models, especially probabilistic methods, require numerous historical failure instances. In practice however, industrial and military communities would rarely allow their engineering assets to run to failure. It is only known that the machine component survived up to the time of repair or replacement but there is no information as to when the component would have failed if left undisturbed. Data of this sort are called truncated data. This paper proposes a novel model, the Intelligent Product Limit Estimator (iPLE), which utilizes truncated data to perform adaptive long-range prediction of a machine component's remaining lifetime. It takes advantage of statistical models' ability to provide useful representation of survival probabilities, and of neural networks ability to recognise nonlinear relationships between a machine component's future survival condition and a given series of prognostic data features. Progressive bearing degradation data were simulated and used to train and validate the proposed model. The results support our hypothesis that the iPLE can perform better than similar prognostics models that neglect truncated data

Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

Deployment of large-scale wind turbines requires sophisticated operation and maintenance strategies to ensure the devices are safe, profitable and cost-effective. Prognostics aims to predict the remaining useful life (RUL) of physical systems based on condition measurements. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes to combine two supervised machine learning techniques, namely, regression model and multilayer artificial neural network model, to predict the RUL of an operational wind turbine gearbox using vibration measurements. Root Mean Square (RMS), Kurtosis (KU) and Energy Index (EI) were analysed to define the bearing failure stages. The proposed methodology was evaluated through a case study involving vibration measurements of a high-speed shaft bearing used in a wind turbine gearbox

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

A Deep Learning Approach to Prognostics of Rolling Element Bearings

The use of deep learning approaches for prognostics and remaining useful life predictions have become obviously prevalent. Artificial recurrent neural networks like the long short-term memory are popularly employed for forecasting, prognostics and health management practices, and in other fields of life. As an unsupervised learning approach, the efficiency of the long short-term memory for time-series predictive purposes is quite remarkable in contrast to standard feedforward neural networks. Virtually all mechanical systems consist mostly of rotating components which are by nature, prone to degradation/failure from known and uncertain causes. As a result, condition monitoring of these rolling element bearings is necessary in order to carry out prognostics and make necessary life predictions which guide safe and cost-effective decision making. Several studies have been conducted on effective approaches and methods for accurate prognostics of rolling element bearings; however, this paper presents a case study on rolling element bearing prognostics and degradation performance using an LSTM model