scholarly journals Remaining Useful Life Calculation of a Component using Hybrid Fatigue Crack Model

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Ebechhoefer ◽  
Lei Xiao ◽  
Xinghui Zhang
Keyword(s):  
Remaining Useful Life ◽  
Crack Model ◽  
Real World Data ◽  
Data Set ◽  
Mechanics Model ◽  
Time Period ◽  
Important Benefit ◽  
Operational Readiness ◽  
Useful Life ◽  
Improved Model

High value asset vehicles, or vehicles where safety/operational readiness is important, benefit from an accurate remaining useful life (RUL) estimate. For these assets, RUL allows operators to realize revenue because of improved availability. This paper uses a hybrid, high cycle fracture mechanics model to improve the RUL estimate. The hybrid method’s performance is validated using prognosability, trendability and monotonicity against two existing models using a real-world data set. The improved model allows a longer time period to marshal the resources needed for repair or give operations personnel an extended window to bring other assets to cover missions that would otherwise be unavailable.

scholarly journals Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

2021 ◽  
Vol 11 (11) ◽  
pp. 4773
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Health State ◽  
Rolling Bearings ◽  
Data Set ◽  
Time Frequency ◽  
Useful Life ◽  
Degradation Indicator

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines

2017 ◽  
Author(s):  
Zhixiong Li ◽  
Dazhong Wu ◽  
Chao Hu ◽  
Janis Terpenny ◽  
Sheng Shen
Keyword(s):  
Predictive Model ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Weighted Sum ◽  
Aircraft Engines ◽  
Data Set ◽  
Ensemble Learning Algorithm ◽  
Useful Life ◽  
Mechanical Components

The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specifically, this method assigns an optimized, degradation-dependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using a data set collected from an engine simulator. Analysis results show that the predictive model trained by the ensemble learning algorithm outperform the existing methods.

scholarly journals A Novel Prediction Process of the Remaining Useful Life of Electric Vehicle Battery Using Real-World Data

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2174
Author(s):  
Xu Wang ◽  
Jian Li ◽  
Ben-Chang Shia ◽  
Yi-Wei Kao ◽  
Chieh-Wen Ho ◽  
...  
Keyword(s):  
Inventory Management ◽  
Real World ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Real World Data ◽  
World Data ◽  
The Real ◽  
Battery Capacity ◽  
Useful Life ◽  
Online Process

In modern society, environmental sustainability is always a top priority, and thus electric vehicles (EVs) equipped with lithium-ion batteries are becoming more and more popular. As a key component of EVs, the remaining useful life of battery directly affects the demand of the EV supply chain. Accurate prediction of the remaining useful life (RUL) benefits not only EV users but also the battery inventory management. There are many existing methods to predict RUL based on state of health (SOH), but few of them are suitable for real-world data. There are several difficulties: (1) battery capacity is not easy to obtain in the real world; (2) most of these methods use the individual data for each battery, and the computing processes are difficult to perform in the cloud; (3) there is a lack of approaches for real-time SOH estimating and RUL predicting. This paper adopts several statistical methods to perform the prediction and compars the results of different models on experimental data (NASA dataset). Then, real-world data were implemented for an online process of RUL prediction. The main finding of this research is that the required CPU time was short enough to meet the daily usage after the real-world data was implemented for an online process of RUL prediction. The feasibility and precision of the prediction model can help to support the frequency control in power systems.

scholarly journals Remaining Useful Life Estimation of Bearings

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Hugo M. Ferreira ◽  
Alexandre C. De Sousa
Keyword(s):  
Best Practices ◽  
Model Evaluation ◽  
Prediction Models ◽  
Meta Analysis ◽  
Remaining Useful Life ◽  
Experimental Methodology ◽  
Data Set ◽  
Life Estimation ◽  
Problem Class ◽  
Useful Life

In the domain of predictive maintenance, when trying to repli- cate and compare research in remaining useful life estimation (RUL), several inconsistencies and errors were identified in the experimental methodology used by various researchers. This makes the replication and the comparison of results diffi- cult, thus severely hindering both progress in this research do- main and its practical application to industry. We survey the literature to evaluate the experimental procedures that were used, and identify the most common errors and omission in both experimental procedures and reporting. A total of 70 papers on RUL were audited. From this meta- analysis we estimate that approximately 11% of the papers present work that will allow for replication and comparison. Surprisingly, only about 24.3% (17 of the 70 articles) com- pared their results with previous work. Of the remaining work, 41.4% generated and compared several models of their own and, somewhat unsettling, 31.4% of the researchers made no comparison whatsoever. The remaining 2.9% did not use the same data set for comparisons. The results of this study were also aggregated into 3 categories: problem class selec- tion, model fitting best practices and evaluation best practices. We conclude that model evaluation is the most problematic one. The main contribution of the article is a proposal of an ex- perimental protocol and several recommendations that specif- ically target model evaluation. Adherence to this protocol should substantially facilitate the research and application of RUL prediction models. The goals are to promote the collab- oration between scholars and practitioners alike and advance the research in this domain.

scholarly journals A novel deep capsule neural network for remaining useful life estimation

2019 ◽  
Vol 234 (1) ◽  
pp. 151-167 ◽  
Author(s):  
Andrés Ruiz-Tagle Palazuelos ◽  
Enrique López Droguett ◽  
Rodrigo Pascual
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Computation Time ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Fault Diagnostics ◽  
Data Set ◽  
Life Estimation ◽  
Useful Life ◽  
Fault Prognostics

With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.

Remaining Useful Life (RUL) Prediction of Rolling Element Bearing Using Random Forest and Gradient Boosting Technique

2018 ◽  
Author(s):  
Sangram Patil ◽  
Aum Patil ◽  
Vishwadeep Handikherkar ◽  
Sumit Desai ◽  
Vikas M. Phalle ◽  
...  
Keyword(s):  
Random Forest ◽  
Learning Curves ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Data Driven ◽  
Gradient Boosting ◽  
Data Set ◽  
Target Values ◽  
Rolling Element ◽  
Useful Life

Rolling element bearings are very important and highly utilized in many industries. Their catastrophic failure due to fluctuating working conditions leads to unscheduled breakdown and increases accidental economical losses. Thus these issues have triggered a need for reliable and automatic prognostics methodology which will prevent a potentially expensive maintenance program. Accordingly, Remaining Useful Life (RUL) prediction based on artificial intelligence is an attractive methodology for several researchers. In this study, data-driven condition monitoring approach is implemented for predicting RUL of bearing under a certain load and speed. The approach demonstrates the use of ensemble regression techniques like Random Forest and Gradient Boosting for prediction of RUL with time-domain features which are extracted from given vibration signals. The extracted features are ranked using Decision Tree (DT) based ranking technique and training and testing feature vectors are produced and fed as an input to ensemble technique. Hyper-parameters are tuned for these models by using exhaustive parameter search and performance of these models is further verified by plotting respective learning curves. For the present work FEMTO bearing data-set provided by IEEE PHM Data Challenge 2012 is used. Weibull Hazard Rate Function for each bearing from learning data set is used to find target values i.e. projected RUL of the bearings. Results of proposed models are compared with well-established data-driven approaches from literature and are found to be better than all the models applied on this data-set, thereby demonstrating the reliability of the proposed model.

Semi-Supervised Learning With Co-Training for Data-Driven Prognostics

2011 ◽  
Author(s):  
Chao Hu ◽  
Byeng D. Youn ◽  
Taejin Kim
Keyword(s):  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Individual Data ◽  
Data Set ◽  
Failure Data ◽  
Rich Information ◽  
Useful Life ◽  
Engineered Systems ◽  
Systems Failure

Traditional data-driven prognostics often requires a large amount of failure data for the offline training in order to achieve good accuracy for the online prediction. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while suspension data are readily available. In such cases, it becomes essentially critical to utilize suspension data, which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic algorithm, denoted by Coprog, which uses two individual data-driven algorithms with each predicting RULs of suspension units for the other. The confidence of an individual data-driven algorithm in predicting the RUL of a suspension unit is quantified by the extent to which the inclusion of that unit in the training data set reduces the sum square error (SSE) in RUL prediction on the failure units. After a suspension unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure unit that is added to the training data set. Results obtained from two case studies suggest that Coprog gives more accurate RUL predictions compared to any individual algorithm without the consideration of suspension data and that Coprog can effectively exploit suspension data to improve the accuracy in data-driven prognostics.

Comparison of Prognostic Health Management Algorithms for Assessment of Electronic Interconnect Reliability Under Vibration

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe
Keyword(s):  
Kalman Filter ◽  
Printed Circuit Board ◽  
Health Management ◽  
Remaining Useful Life ◽  
Circuit Board ◽  
Data Set ◽  
Printed Circuit ◽  
Interconnect Reliability ◽  
Useful Life ◽  
Management Algorithms

This paper compares three prognostic algorithms applied to the same data recorded during the failure of a solder joint in ball grid array component attached to a printed circuit board. The objective is to expand on the relative strengths and weaknesses of each proposed algorithm. Emphasis will be placed on highlighting differences in underlying assumptions required for each algorithm, details of remaining useful life calculations, and methods of uncertainty quantification. Metrics tailored specifically for prognostic health monitoring (PHM) are presented to characterize the performance of predictions. The relative merits of PHM algorithms based on a Kalman filter, extended Kalman filter, and a particle filter all demonstrated on the same data set will be discussed. The paper concludes by discussing which algorithm performs best given the information available about the system being monitored.

Sign in / Sign up

Export Citation Format

Share Document