9,587 research outputs found

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

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    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 ϵ\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

    Overview of Remaining Useful Life prediction techniques in Through-life Engineering Services

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    Through-life Engineering Services (TES) are essential in the manufacture and servicing of complex engineering products. TES improves support services by providing prognosis of run-to-failure and time-to-failure on-demand data for better decision making. The concept of Remaining Useful Life (RUL) is utilised to predict life-span of components (of a service system) with the purpose of minimising catastrophic failure events in both manufacturing and service sectors. The purpose of this paper is to identify failure mechanisms and emphasise the failure events prediction approaches that can effectively reduce uncertainties. It will demonstrate the classification of techniques used in RUL prediction for optimisation of products’ future use based on current products in-service with regards to predictability, availability and reliability. It presents a mapping of degradation mechanisms against techniques for knowledge acquisition with the objective of presenting to designers and manufacturers ways to improve the life-span of components

    An Integrated Fuzzy Inference Based Monitoring, Diagnostic, and Prognostic System

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    To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable grab bag of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ~45 hours and 95% CIs of ~162 hours

    Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

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    © 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

    Review of Health Prognostics and Condition Monitoring of Electronic Components

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    To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

    Probabilistic Monte-Carlo method for modelling and prediction of electronics component life

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    Power electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In recent years, several research work about reliability, failure mode and aging analysis have been extensively carried out. There is a need for an efficient algorithm able to predict the life of power electronics component. In this paper, a probabilistic Monte-Carlo framework is developed and applied to predict remaining useful life of a component. Probability distributions are used to model the component’s degradation process. The modelling parameters are learned using Maximum Likelihood Estimation. The prognostic is carried out by the mean of simulation in this paper. Monte-Carlo simulation is used to propagate multiple possible degradation paths based on the current health state of the component. The remaining useful life and confident bounds are calculated by estimating mean, median and percentile descriptive statistics of the simulated degradation paths. Results from different probabilistic models are compared and their prognostic performances are evaluated

    Prognostics health management: perspectives in engineering systems reliability prognostics

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    The Prognostic Health Management (PHM) has been asserting itself as the most promising methodology to enhance the effective reliability and availability of a product or system during its life-cycle conditions by detecting current and approaching failures, thus, providing mitigation of the system risks with reduced logistics and support costs. However, PHM is at an early stage of development, it also expresses some concerns about possible shortcomings of its methods, tools, metrics and standardization. These factors have been severely restricting the applicability of PHM and its adoption by the industry. This paper presents a comprehensive literature review about the PHM main general weaknesses. Exploring the research opportunities present in some recent publications, are discussed and outlined the general guide-lines for finding the answer to these issues.(undefined

    Prognostics and Health Management of Industrial Equipment

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    ISBN13: 9781466620957Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL)
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