Query: Remaining useful life prediction based on health index similarity

Candidate papers:

abstract: Abstract Condition based maintenance and the prediction of the remaining useful life (RUL) of cutting tools are of crucial importance to reduce unexpected downtime and ensure quality. Our paper proposes an original RUL prediction model based on health index (HI) similarity, where both distance similarity and spatial direction similarity are considered. Data mining is carried out to the large and messy original monitoring data to construct the HIs of the cutting tools, which are then used to predict the RUL. The novelty of our method is that it can make full use of limited historical datasets to achieve more accurate prediction results. The model is applied to the data obtained from a turbine factory's slotting cutter machining process and is compared to one of the most popular prognostic method least squares support vector regression. Our proposed approach is also applied to two further case studies a GaAs based semiconductor laser and simulated data. The comparative results show the effectiveness and practicability of our proposed method, even when the data fluctuate a lot and show distinctive trends.
author_names: Yingchao Liu

Xiaofeng Hu

Wenjuan Zhang
title: Remaining useful life prediction based on health index similarity
venue: Reliab. Eng. Syst. Saf.
year: 2019

abstract: T/R module is an important part of large phased array radar antenna array, the remaining useful life (RUL) prediction is of great significance for equipment support personnel to master current health status and improve maintenance support ability. At first, the key indexes of multistate degradation are extracted by association rule, by calculating the similarity of the key indexes and combining the RUL of similar samples, the RUL information corresponding to the key indexes is obtained. The RUL prediction results of T/R module are obtained according to the weight of different indexes. The practicability and effectiveness of the proposed method are verified by example analysis and comparison, which provide theoretical guidance for the RUL prediction of electronic equipment. The proposed method is of great significance for maintenance support personnel of the military equipment to plan the maintenance resources reasonably and improve the combat effectiveness of the equipment.
author_names: Xiaodong Hou

Jiangping Yang

Bin Deng

Chunhe Chang

Yu Lin Zhang

Jiajing Xu
title: A Remaining Useful Life Prediction Method for T/R Module Based on Index Similarity
venue: 2018 Prognostics and System Health Management Conference (PHM Chongqing)
year: 2018

abstract: The concept of Prognostic and Health Management (PHM) on machinery attracts a lot of attention during these years, as a key technique of PHM, the prediction of Remaining Useful Life (RUL) of equipment is well studied. As a method with reasonable assumption, Health Index (HI) is successfully applied in RUL prediction. However, in the scenario of large mechanical equipment, there are some disadvantages in tradition HI method. So the notion of Core State is raised and some improvements are introduced, and furthermore, a unified framework for the RUL prediction of large equipment, which mainly based on similarity metric of HI, is then presented. Finally, the proposed idea was carried out through a real data experiment. The result indicated the signification effect of Core State in RUL prediction and verified the validity of the framework as well.
author_names: Bo Wang

Li Zhang

Xuliang He
title: Study on the prediction of Remaining Useful Life of large equipment based on the similarity metric of Health Index
venue: 2010 International Conference on Mechanic Automation and Control Engineering
year: 2010

abstract: Predicting the remaining useful life (RUL) of systems is of importance in prognostics and health management to improve the maintenance capability in modern industries. Several approaches for RUL prediction using multiple sensory data assume the system's degradation in a specific form, which may not be applicable to the diversity of practical engineering systems. In this paper, a two phase data driven framework is proposed. The first phase is an offline process of constructing the health index (HI) library from run to failure data using fuzzy similarity based data fusion, which enables to create the HIs without any assumptions. In the second phase, the HI of online is mapped onto the library created offline to find the best matching position using time series similarity search. This simultaneously enables the current condition of the system being informed as well as estimating its time left. The degradation simulation dataset of turbofan engine from NASA Prognostics Center is used to evaluate the potential application of the proposed framework.
author_names: Van Tung Tran

Jihoon Hong
title: A Multi Sensor Prognostics Framework Based on Data Fusion and Time Series Similarity Search
venue: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
year: 2020

abstract: Abstract Remaining useful life (RUL) estimation of a degrading system is the major prognostic activity in many industry applications. This paper presents an improved version of the similarity based curve matching method for the remaining useful life estimation of a mechanical system, which is a companion paper of our previous work on RUL estimations using a bidirectional recurrent neural network (RNN) based autoencoder scheme. We propose a zero centering rule to tackle the varying initial health across instances (systems) when using the similarity based health index curve matching technique to identify the training instances that share a similar degradation pattern with the test instance whose RUL needs to be determined. However, this rule will also induce a significant prediction error, especially when the off line training instances are abundant, or the true RULs of the on line test instances are large. Thus, an ensemble approach that integrates the RUL estimations obtained from the similarity based curve matching techniques, with and without the zero centering rules, is introduced to increase the robustness and accuracy of proposed method for RUL estimations. We evaluate the prognostic performance of the ensemble algorithm and standalone algorithms on four publicly available turbofan engine degradation datasets. The results demonstrate that the proposed ensemble approach gives more robust and reliable RUL estimations compared to any independent algorithm used on all the studied datasets.
author_names: W Yu

Ii Yong Kim

Chris K Mechefske
title: An improved similarity based prognostic algorithm for RUL estimation using an RNN autoencoder scheme
venue: Reliab. Eng. Syst. Saf.
year: 2020

abstract: Abstract Remaining useful life (RUL) prediction has been a difficult problem in industry and academia. Deep learning techniques based on supervised learning have made great progress compared to traditional prediction algorithms. However, due to the random nature of system degradation behavior, deep supervised learning cannot accurately adapt the model obtained based on the training set to the systems in the testing set. The similarity based prediction method has achieved accurate approximate remaining life, which called similar RUL. Therefore, the combination of supervised learning and similarity based prediction has the potential to take advantages of the learning ability of the network model and similar RUL to improve the prediction accuracy. This paper investigates the effect of using similarity to improve the prediction accuracy of deep supervised learning. In addition, since health indicator (HI) and target labels are the essential factors of supervised learning, this paper proposes a method for constructing HI based on unsupervised learning. Then according to the constructed HI, a difference based method proposed to generate target labels. To verify the effectiveness of the proposed method, this paper conducted experimental verification on the C MAPSS engine dataset and XJTU SY bearing dataset and made related comparative experiments. Experimental results show that the proposed method obtains favorable results against the traditional methods.
author_names: Mengru Hou

Dechang Pi
title: Similarity based deep learning approach for remaining useful life prediction
year: 2020

abstract: In view of the problems of degradation index construction and predicted lag of similarity based methods, a degradation degree considered method for remaining useful life prediction based on similarity is proposed. The effectiveness of the proposed method is demonstrated by using turbofan engine data from NASA and a real case study.
author_names: Zeming Liang

Jianmin Gao

Hong-Quan Jiang

Xu Gao

Zhiyong Gao

Rongxi Wang
title: A Degradation Degree Considered Method for Remaining Useful Life Prediction Based on Similarity
venue: Computing in Science Engineering
year: 2019

abstract: Extracting suitable features from acquired data to accurately depict the current health state of a system is crucial in data driven condition monitoring and prediction. Usually, analogue sensor data is sampled at rates far exceeding the Nyquist rate containing substantial amounts of redundancies and noise, imposing high computational loads due to the subsequent and necessary feature processing chain (generation, dimensionality reduction, rating and selection) To overcome these problems, Compressed Sensing can be used to sample directly to a compressed space, provided the signal at hand and the employed compression/measurement system meet certain criteria. Theory states, that during this compression step enough information is conserved, such that a reconstruction of the original signal is possible with high probability. The proposed approach however does not rely on reconstructed data for condition monitoring purposes, but uses directly the compressed signal representation as feature vector. It is hence assumed that enough information is conveyed by the compression for condition monitoring purposes. To fuse the compressed coefficients into one health index that can be used as input for remaining useful life prediction algorithms and is limited to a reasonable range between 1 and 0, a logistic regression approach is used. Run to failure data of three translational electromagnetic actuators is used to demonstrate the health index generation procedure. A comparison to the time domain ground truth signals obtained from Nyquist sampled coil current measurements shows reasonable agreement. I.e. underlying wear out phenomena can be reproduced by the proposed approach enabling further investigation of the application of prognostic methods.
author_names: Christian Knoebel

Daniel Strommenger

Johannes Reuter

Clemens Guehmann
title: Health Index Generation Based on Compressed Sensing and Logistic Regression for Remaining Useful Life Prediction
venue: Annual Conference of the PHM Society
year: 2019

abstract: In this work, a hybrid prognostic framework which interfaces a model based prognostic method, namely particle filter, with a similarity based prognostic method is proposed. The proposed framework consists of automatic determination of predication start point, sensor fusion, and prognostics steps that lead to accurate remaining useful life (RUL) estimations. This approach first applies the canonical variate analysis (CVA) approach for determining the prediction start time and constructing the prognostic health indicators (HIs) The similarity based method is then employed together with the model based particle filter (PF) algorithm to improve the predictive performance in terms of reducing the uncertainty of RUL and improving the prediction accuracy. The proposed framework can automatically construct HIs that are suitable for RUL prediction and offer higher prediction accuracy and lower uncertainty boundaries than traditional model based PF methods. Our proposed approach is successfully applied on aircraft turbofan engines RUL prediction.
author_names: Xiaochuan Li

David Mba

Tianran Lin
title: A Similarity based and Model based Fusion Prognostics Framework for Remaining Useful Life Prediction
venue: 2019 Prognostics and System Health Management Conference (PHM Qingdao)
year: 2019

abstract: The problems in the remaining useful life (RUL) prediction of rolling bearings are difficulty in determining the failure threshold and large prediction error with a single prediction model. To solve them, based on the adaptive boosting integrated relevance vector machine model (AdaBoost_RVM) a method for constructing health indices and predicting RUL is proposed. The experimental results show that the failure thresholds of all the different bearings are 1 using the proposed method, and compared with the single RVM model, it yields a bearing RUL prediction that has a smaller error and is closer to the true value.
author_names: Wang Yujing

Wang Shida

Kang Shou-qiang

Xie Jinbao
title: Health index construction and remaining useful life prediction of rolling bearings
venue: 2019 14th IEEE International Conference on Electronic Measurement Instruments (ICEMI)
year: 2019