scholarly journals A New Hybrid Prognostic Methodology

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Omer F. Eker ◽  
Fatih Camci ◽  
Ian K. Jennions
Keyword(s):  
Hybrid Methods ◽  
Industrial Applications ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Prognostic Models ◽  
Advantages And Disadvantages ◽  
Minimal Data ◽  
Useful Life ◽  
Filter Clogging ◽  
Short Term Prediction

Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications, this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason, a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer-term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available.

scholarly journals Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2453
Author(s):  
Amgad Muneer ◽  
Shakirah Mohd Taib ◽  
Sheraz Naseer ◽  
Rao Faizan Ali ◽  
Izzatdin Abdul Aziz
Keyword(s):  
Time Window ◽  
Attention Mechanism ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
Prognostic Models ◽  
Prediction Ability ◽  
Turbofan Engine ◽  
Useful Life ◽  
Engine System

Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method’s applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

scholarly journals A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets

2017 ◽  
Vol 231 (4) ◽  
pp. 350-363 ◽  
Author(s):  
Sameer Al-Dahidi ◽  
Francesco Di Maio ◽  
Piero Baraldi ◽  
Enrico Zio
Keyword(s):  
Markov Model ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Data Driven ◽  
Prognostic Models ◽  
Ensemble Approach ◽  
Fuzzy Similarity ◽  
Finite State ◽  
Useful Life ◽  
Life Predictions

In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity–based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity–based models.

scholarly journals Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2135
Author(s):  
Marcin Witczak ◽  
Marcin Mrugalski ◽  
Bogdan Lipiec
Keyword(s):  
Historical Data ◽  
Field Effect Transistors ◽  
Failure Process ◽  
Practical Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Data Repository ◽  
Uncertainty Interval ◽  
Useful Life ◽  
Takagi Sugeno

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

A Clustering-Based Framework for Performance Degradation Prediction of Slewing Bearing Using Multiple Physical Signals

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Peng Ding ◽  
Hua Wang ◽  
Yongfen Dai
Keyword(s):  
Degradation Process ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Self Organizing Map ◽  
Rolling Bearings ◽  
Heavy Load ◽  
Joint Application ◽  
Learning Modes ◽  
Useful Life ◽  
Slewing Bearings

Diagnosing the failure or predicting the performance state of low-speed and heavy-load slewing bearings is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures focus on the performance prediction of small rolling bearings, while studies on slewing bearings' health evaluation are very rare. Among these rare studies, supervised or unsupervised data-driven models are often used alone, few researchers devote to remaining useful life (RUL) prediction using the joint application of two learning modes which could fully take diversity and complexity of slewing bearings' degradation and damage into consideration. Therefore, this paper proposes a clustering-based framework with aids of supervised models and multiple physical signals. Correlation analysis and principle component analysis (PCA)-based multiple sensitive features in time-domain are used to establish the performance recession indicators (PRIs) of torque, temperature, and vibration. Subsequently, these three indicators are divided into several parts representing different degradation periods via optimized self-organizing map (OSOM). Finally, corresponding data-driven life models of these degradation periods are generated. Experimental results indicate that multiple physical signals can effectively describe the degradation process. The proposed clustering-based framework is provided with a more accurate prediction of slewing bearings' RUL and well reflects the performance recession periods.

Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

2020 ◽  
pp. 1-1
Author(s):  
Naipeng Li ◽  
Yaguo Lei ◽  
Nagi Gebraeel ◽  
Zhijian Wang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
Useful Life

Remaining useful life estimation based on the joint use of an observer and a hidden Markov model

2021 ◽  
pp. 1748006X2110443
Author(s):  
Toufik Aggab ◽  
Pascal Vrignat ◽  
Manuel Avila ◽  
Frédéric Kratz
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Practical Interest ◽  
Markov Property ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Remaining Useful Life ◽  
Discrete Observations ◽  
Useful Life ◽  
The Difference

We propose an approach for failure prognosis based on the estimation of the Remaining Useful Life (RUL) of a system in a situation in which monitoring signals providing information about its degradation evolution are not measured and no operating model of the system is available. These conditions are of practical interest for industrial applications such as mechanical (e.g. rolling bearing) or electrical (e.g. wind turbine) devices or equipment-critical components (e.g. batteries) in which the addition of sensors to the system is not feasible (e.g. space limitations for sensors, cost, etc.). The approach is based on an estimation of the system degradation using residual generation (where the difference between the system and the observer outputs is processed) and Hidden Markov Models with discrete observations. The prediction of the system RUL is given by the Markov property concerning the mean time before absorption. The approach comprises two phases: a training phase to model the degradation behavior and an “on-line” use phase to estimate the remaining life of the system. Two case studies were conducted for RUL prediction to verify the effectiveness of the proposed approach.

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