Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators
TLDR
A thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning is presented and provides a basis for predicting the remaining useful life of bearings and gears.Abstract:
Prognostics and health management is an emerging discipline to scientifically manage the health condition of engineering systems and their critical components. It mainly consists of three main aspects: construction of health indicators, remaining useful life prediction, and health management. Construction of health indicators aims to evaluate the system’s current health condition and its critical components. Given the observations of a health indicator, prediction of the remaining useful life is used to infer the time when an engineering systems or a critical component will no longer perform its intended function. Health management involves planning the optimal maintenance schedule according to the system’s current and future health condition, its critical components and the replacement costs. Construction of health indicators is the key to predicting the remaining useful life. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. Because it is difficult to measure and quantify the health conditions of bearings and gears in many cases, numerous vibration-based methods have been proposed to construct bearing and gear health indicators. This paper presents a thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning. This review paper will be helpful for designing further advanced bearing and gear health indicators and provides a basis for predicting the remaining useful life of bearings and gears. Most of the bearing and gear health indicators reviewed in this paper are highly relevant to simulated and experimental run-to-failure data rather than artificially seeded bearing and gear fault data. Finally, some problems in the literature are highlighted and areas for future study are identified.read more
Citations
More filters
Journal ArticleDOI
Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
Purushottam Gangsar,Rajiv Tiwari +1 more
TL;DR: Overall, this paper includes review of system signals, conventional and advance signal processing techniques; however, it mainly covers, the selection of effective statistical features, AI methods, and associated training and testing strategies for fault diagnostics of IMs.
Journal ArticleDOI
A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction
Jialin Li,Xueyi Li,David He +2 more
TL;DR: A directed acyclic graph (DAG) network that combines long short term memory (LSTM) and a convolutional neural network (CNN) to predict the RUL to improve the prognostic accuracy of the network.
Journal ArticleDOI
An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image
TL;DR: The proposed method has a higher fault diagnosis accuracy than existing deep learning diagnosis methods and the use of hierarchical regularization to obtain better training results.
Intelligent condition-based prediction of machinery reliability
Abstract: Abstract The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models that attempt to forecast machinery health based on condition data. This paper presents a novel approach for incorporating population characteristics information and suspended condition trending data of historical units into prognosis. The population characteristics information extracted from statistical failure distribution enables longer-range prognosis. The accurate modelling of suspended data is also found to be of great importance, since in practice machines are rarely allowed to run to failure and hence data are commonly suspended. The proposed model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function (PDF) estimator. The trained network is capable of estimating the future survival probabilities of an operating asset when a series of condition indices are inputted. The output survival probabilities collectively form an estimated survival curve. Pump vibration data were used for model validation. The proposed model was compared with two similar models that neglect suspended data, as well as with a conventional time series prediction model. The results support our hypothesis that the proposed model can predict more accurately and further ahead than similar methods that do not include population characteristics and/or suspended data in prognosis.
Journal ArticleDOI
Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice
Enrico Zio,Enrico Zio +1 more
TL;DR: This paper aims at pointing out main challenges and directions of advancements, for full deployment of condition-based and predictive maintenance in practice, for Prognostics and Health Management and its benefits in practice.
References
More filters
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
A review on machinery diagnostics and prognostics implementing condition-based maintenance
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
Book
Stochastic Resonance
TL;DR: In this article, a theoretical approach based on linear response theory (LRT) is described, and two new forms of stochastic resonance, predicted on the basis of LRT and subsequently observed in analogue electronic experiments, are described.
Book
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
TL;DR: This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing and how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more.
Journal ArticleDOI
Remaining useful life estimation - A review on the statistical data driven approaches
TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.