scholarly journals Investigating computational geometry for failure prognostics

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Emmanuel Ramasso
Keyword(s):  
Computational Geometry ◽  
Health Management ◽  
Parameter Tuning ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Case Based Reasoning ◽  
Health State ◽  
Health Indicator ◽  
Optimal Functioning ◽  
Planar Figure

Prognostics and Health Management (PHM) is a multidisciplinary field aiming at maintaining physical systems in their optimal functioning conditions. The system under study is assumed to be monitored by sensors from which are obtained measurements reflecting the system’s health state. A health indicator (HI) is estimated to feed a data-driven PHM solution developed to predict the remaining useful life (RUL). In this paper, the values taken by an HI are assumed imprecise (IHI). An IHI is interpreted as a planar figure called polygon and a case-based reasoning (CBR) approach is adapted to estimate the RUL. This adaptation makes use of computational geometry tools in order to estimate the nearest cases to a given testing instance. The proposed algorithm called RULCLIPPER is assessed and compared on datasets generated by the NASA’s turbofan simulator (C-MAPSS) including the four turbofan testing datasets and the two testing datasets of the PHM’08 data challenge. These datasets represent 1360 testing instances and cover different realistic and difficult cases considering operating conditions and fault modes with unknown characteristics. The problem of feature selection, health indicator estimation, RUL fusion and ensembles are also tackled. The proposed algorithm is shown to be efficient with few parameter tuning on all datasets.

scholarly journals Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yuhuang Zheng
Keyword(s):  
Health Management ◽  
Detection Threshold ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Health State ◽  
Health Indicator ◽  
Degradation Model ◽  
Vibration Signals ◽  
Current Health ◽  
Useful Life

Prognostics health management (PHM) of rotating machinery has become an important process for increasing reliability and reducing machine malfunctions in industry. Bearings are one of the most important equipment parts and are also one of the most common failure points. To assess the degradation of a machine, this paper presents a bearing remaining useful life (RUL) prediction method. The method relies on a novel health indicator and a linear degradation model to predict bearing RUL. The health indicator is extracted by using Hilbert–Huang entropy to process horizontal vibration signals obtained from bearings. We present a linear degradation model to estimate RUL using this health indicator. In the training phase, the degradation detection threshold and the failure threshold of this model are estimated by the distribution of 600 bootstrapped samples. These bootstrapped samples are taken from the six training sets. In the test phase, the health indicator and the model are used to estimate the bearing’s current health state and predict its RUL. This method is suitable for the degradation of bearings. The experimental results show that this method can effectively monitor bearing degradation and predict its RUL.

scholarly journals Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 49 ◽  
Author(s):  
Faisal Khan ◽  
Omer Eker ◽  
Atif Khan ◽  
Wasim Orfali
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Health Management ◽  
Cost Effective ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Data Driven ◽  
Prediction Errors ◽  
Degradation Model ◽  
Jet Engine

In the aerospace industry, every minute of downtime because of equipment failure impacts operations significantly. Therefore, efficient maintenance, repair and overhaul processes to aid maximum equipment availability are essential. However, scheduled maintenance is costly and does not track the degradation of the equipment which could result in unexpected failure of the equipment. Prognostic Health Management (PHM) provides techniques to monitor the precise degradation of the equipment along with cost-effective reliability. This article presents an adaptive data-driven prognostics reasoning approach. An engineering case study of Turbofan Jet Engine has been used to demonstrate the prognostic reasoning approach. The emphasis of this article is on an adaptive data-driven degradation model and how to improve the remaining useful life (RUL) prediction performance in condition monitoring of a Turbofan Jet Engine. The RUL prediction results show low prediction errors regardless of operating conditions, which contrasts with a conventional data-driven model (a non-parameterised Neural Network model) where prediction errors increase as operating conditions deviate from the nominal condition. In this article, the Neural Network has been used to build the Nominal model and Particle Filter has been used to track the present degradation along with degradation parameter.

scholarly journals Development of Multivariate Failure Threshold with Quantifiable Operation Risks in Machine Prognostics

2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Xiaodong Jia ◽  
Wenzhe Li ◽  
Wei Wang ◽  
Xiang Li ◽  
Jay Lee
Keyword(s):  
Health Management ◽  
Cumulative Risk ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Risk Quantification ◽  
Quantification Method ◽  
Mk Test ◽  
Useful Life ◽  
Quantification Model ◽  
Operation Risk

Prognostics and Health Management (PHM) is attracting the attention from both academia and industry due to its great potential to enhance the resilience and responsiveness of the equipment to the potential operation risks. In literature, many methodologies are proposed to predict the Remaining Useful Life (RUL) of the equipment. However, there are two major challenges that limit the practicality of these methodologies. 1) How to generate a quantifiable Health Indicator (HI) to represent the operation risks? 2) How to define a reasonable failure threshold to predict RUL? To answer these two questions, this paper proposes a novel methodology for failure threshold determination with quantifiable operation risk in machine prognostics. In the proposed methodology, Fisher distance and Mann-Kendall (MK) test are firstly used to extract useful sensors based on which HI is estimated by applying Principle Component Analysis (PCA). Then, Rao-Blackwellized Particle Filter (RBPF) is employed to obtain the HI prediction and the uncertainties. Afterwards, a Bivariate-Weibull-distribution-based risk quantification model is designed to quantify the cumulative risk over time and over the increase of HI. The failure threshold, which is the ending point of the RUL, varies over different users and applications depending on the level of risk they want to tolerate. The validation of the methodology is based on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The results validate the effectiveness of the proposed risk quantification method and its potential application on machine prognostics.

scholarly journals Health Indicator for Predictive Maintenance Based on Fuzzy Cognitive Maps, Grey Wolf, and K-Nearest Neighbors Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
G. Mazzuto ◽  
S. Antomarioni ◽  
F. E. Ciarapica ◽  
M. Bevilacqua
Keyword(s):  
Cognitive Maps ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Fuzzy Cognitive Maps ◽  
Predictive Maintenance ◽  
Health State ◽  
Grey Wolf ◽  
Health Indicator ◽  
Neural Network Approach ◽  
Useful Life

An essential step in the implementation of predictive maintenance involves the health state analysis of productive equipment in order to provide company managers with performance and degradation indicators which help to predict component condition. In this paper, a supervised approach for health indicator calculation is provided combining the Grey Wolf Optimisation method, Swarm Intelligence algorithm, and Fuzzy Cognitive Maps. The k-neighbors algorithms is used to predict the Remaining Useful Life of an item, since, in addition to its simplicity, they produce good results in a large number of domains. The approach aims to solve the problem that frequently occurs in interpolation procedures: the approximation of functions belonging to a chosen class of functions of which we have no knowledge. The proposed algorithm allows maintenance managers to distinguish different degradation profiles in depth with a consequently more precise estimate of the Remaining Useful Life of an item and, in addition, an in-depth understanding of the degradation process. Specifically, in order to show its suitability for predictive maintenance, a dataset on NASA aircraft engines has been used and results have been compared to those obtained with a neural network approach. Results highlight how all of the degradation profiles, obtained using the proposed approach, are modelled in a more detailed manner, allowing one to significantly distinguish different situations. Moreover, the physical core speed and the corrected fan speed have been identified as the main critical factors to the engine degradation.

Deep Learning-based Health Indicator for Better Bearing RUL Prediction

2021 ◽  
Vol 263 (6) ◽  
pp. 493-498
Author(s):  
Taewan Kim ◽  
Seungchul Lee
Keyword(s):  
Domain Knowledge ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Health State ◽  
Health Indicator ◽  
Prognostic Performance ◽  
High Level ◽  
Processing Techniques ◽  
Signal Processing Techniques

The prognostic performance of data-driven approaches closely depends on the features extracted from the measurement. For a high level of prognostic performance, features must be carefully designed to represent the machine's health state well and are generally obtained by signal processing techniques. These features are themselves used as health indicators (HI) or used to construct HIs. However, many conventional HIs are heavily relying on the type of machine components and expert domain knowledge. To solve these drawbacks, we propose a fully data-driven method, that is, the adversarial autoencoder-based health indicator (AAE-HI) for remaining useful life (RUL) prediction. Accelerated degradation tests of bearings collected from PRONOSTIA were used to validate the proposed AAE-HI method. It is shown that our proposed AAE-HI can autonomously find monotonicity and trendability of features, which will capture the degradation progression from the measurement. Therefore, the performance of AAE-HI in RUL prediction is promising compared with other conventional HIs.

scholarly journals Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms

2020 ◽  
Author(s):  
Taoufik Najeh ◽  
Jan Lundberg
Keyword(s):  
Genetic Algorithms ◽  
Optimal Choice ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Health State ◽  
Rolling Bearings ◽  
Vibration Data ◽  
Proposed Model ◽  
Bearing Monitoring ◽  
Data Collection Approach

AbstractThis study is motivated by the need for a new advanced vibration-based bearing monitoring approach. The ARX-Laguerre model (autoregressive with exogenous) and genetic algorithms (GAs) use collected vibration data to estimate a bearing’s remaining useful life (RUL). The concept is based on the actual running conditions of the bearing combined with a new linear ARX-Laguerre representation. The proposed model exploits the vibration and force measurements to reconstruct the Laguerre filter outputs; the dimensionality reduction of the model is subject to an optimal choice of Laguerre poles which is performed using GAs. The paper explains the test rig, data collection, approach, and results. So far and compared to classic methods, the proposed model is effective in tracking the evolution of the bearing’s health state and accurately estimates the bearing’s RUL. As long as the collected data are relevant to the real health state of the bearing, it is possible to estimate the bearing’s lifetime under different operating conditions.

scholarly journals Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5888
Author(s):  
Joaquín Figueroa Barraza ◽  
Enrique López Droguett ◽  
Marcelo Ramos Martins
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Deep Learning ◽  
High Performance ◽  
Deep Neural Networks ◽  
Health Management ◽  
Remaining Useful Life ◽  
Health State ◽  
Prognostics And Health Management ◽  
Selection Framework

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.

scholarly journals Remaining Useful Life Prediction of Bearings Using Ensemble Learning: The Impact of Diversity in Base Learners and Features

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Junchuan Shi ◽  
Tianyu Yu ◽  
Kai Goebel ◽  
Dazhong Wu
Keyword(s):  
Ensemble Learning ◽  
Health Management ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Methods ◽  
Vibration Data ◽  
Model Based ◽  
Useful Life ◽  
The Impact

Abstract Prognostics and health management (PHM) of bearings is crucial for reducing the risk of failure and the cost of maintenance for rotating machinery. Model-based prognostic methods develop closed-form mathematical models based on underlying physics. However, the physics of complex bearing failures under varying operating conditions is not well understood yet. To complement model-based prognostics, data-driven methods have been increasingly used to predict the remaining useful life (RUL) of bearings. As opposed to other machine learning methods, ensemble learning methods can achieve higher prediction accuracy by combining multiple learning algorithms of different types. The rationale behind ensemble learning is that higher performance can be achieved by combining base learners that overestimate and underestimate the RUL of bearings. However, building an effective ensemble remains a challenge. To address this issue, the impact of diversity in base learners and extracted features in different degradation stages on the performance of ensemble learning is investigated. The degradation process of bearings is classified into three stages, including normal wear, smooth wear, and severe wear, based on the root-mean-square (RMS) of vibration signals. To evaluate the impact of diversity on prediction performance, vibration data collected from rolling element bearings was used to train predictive models. Experimental results have shown that the performance of the proposed ensemble learning method is significantly improved by selecting diverse features and base learners in different degradation stages.

scholarly journals Data-driven remaining useful life prediction based on domain adaptation

2021 ◽  
Vol 7 ◽  
pp. e690
Author(s):  
Bin cheng Wen ◽  
Ming qing Xiao ◽  
Xue qi Wang ◽  
Xin Zhao ◽  
Jian feng Li ◽  
...  
Keyword(s):  
Test Data ◽  
Health Management ◽  
Domain Adaptation ◽  
Current Data ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
Data Driven ◽  
Target Domain ◽  
Useful Life

As an important part of prognostics and health management, remaining useful life (RUL) prediction can provide users and managers with system life information and improve the reliability of maintenance systems. Data-driven methods are powerful tools for RUL prediction because of their great modeling abilities. However, most current data-driven studies require large amounts of labeled training data and assume that the training data and test data follow similar distributions. In fact, the collected data are often variable due to different equipment operating conditions, fault modes, and noise distributions. As a result, the assumption that the training data and the test data obey the same distribution may not be valid. In response to the above problems, this paper proposes a data-driven framework with domain adaptability using a bidirectional gated recurrent unit (BGRU). The framework uses a domain-adversarial neural network (DANN) to implement transfer learning (TL) from the source domain to the target domain, which contains only sensor information. To verify the effectiveness of the proposed method, we analyze the IEEE PHM 2012 Challenge datasets and use them for verification. The experimental results show that the generalization ability of the model is effectively improved through the domain adaptation approach.

Complex evaluation of health state of population residing under combined exposure to noise and chemical risk factors caused by activities of major airport hub

2019 ◽  
pp. 12-16 ◽  
Author(s):  
Konstantin P. Luzhetsky ◽  
Ol’ga Yu. Ustinova ◽  
Svetlana S. Kleyn ◽  
Dmitrii N. Koshurnikov ◽  
Svetlana S. Vekovshinina ◽  
...  
Keyword(s):  
Risk Factors ◽  
Nervous System ◽  
Serum Level ◽  
Reference Group ◽  
Risk Index ◽  
Case Based Reasoning ◽  
Health State ◽  
Upper Respiratory Tract ◽  
Combined Exposure ◽  
Complex Evaluation

Complex evaluation covered health state of population residing under combined exposure to physical (noise) and chemical (manganese, formaldehyde, phenol, benzene) risk factors caused by airport activities. Findings are unacceptable chronic risks expressed through risk index for development of nervous system diseases (HI=9.45–51.75), respiratory disorders (HQ=2,62–6,95) and immune system ailments (HQ=1,75–4,23). In children, functional disorders of nervous system (parasympathetic type vegetative dystonia) and respiratory organs (chronic disorders of upper respiratory tract) are diagnosed 1,5–1,8 times higher than those in the reference group; over 5% of children aged 4–7 years demonstrate bilateral conductive deafness. Reliable cause-effect relationships were revealed between functional nervous system disorders (parasympathetic type vegetative dystonia, astheno-neurotic syndrome, vascular cephalgia, sleep disorders) and increased serum level of manganese and benzene (proportion of explained dispersion, R2=0,55–0,87, 26,44≤F≥389,54), between respiratory diseases (chronic rhinitis, chronic pharyngitis) and increased serum level of formaldehyde (R2=0,73–0,91; 350,8≤F≥778,3), with high statistic significance (p < 0,0001). For sanitary epidemiologic examination purposes, case-based reasoning for relationships of children health disorders, management decisions, the authors suggested and justified a list of parameters for negative combined impact of risk factors caused by airport hub.

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