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1

Remaining useful life prediction of a lithium-ion battery based on a temporal convolutional network with data extension

Zhao Jing, Liu Dayong, Meng Lingshuai

International Journal of Applied Mathematics and Computer Science

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2024

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Vol. 34, no. 1

105--117

EN

Unmanned underwater vehicles are typically deployed in deep sea environments, which present unique working conditions. Lithium-ion power batteries are crucial for powering underwater vehicles, and it is vital to accurately predict their remaining useful life (RUL) to maintain system reliability and safety. We propose a residual life prediction model framework based on complete ensemble empirical mode decomposition with an adaptive noise-temporal convolutional net (CEEMDAN-TCN), which utilizes dilated causal convolutions to improve the model’s ability to capture local capacity regeneration and enhance the overall prediction accuracy. CEEMDAN is employed to denoise the data and prevent RUL prediction errors caused by local regeneration, and feature expansion is utilized to extend the temporal dimension of the original data. The NASA and CALCE battery capacity datasets are used as input to train the network framework. The output is the current predicted residual capacity, which is compared with the real residual battery capacity. The MAE, RMSE and RE are used as the evaluation indexes of the RUL prediction performance. The proposed network model is verified on the NASA and CACLE datasets. The evaluation results show that our method has better life prediction performance. At the same time, it is proved that both feature expansion and modal decomposition can improve the generalization ability of the model, which is very useful in industrial scenarios.

2

Wang Zongyao, Shangguan Wei, Peng Cong, Cai Baigen

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 1

art. no. 175585

EN

Lithium-ion batteries find extensive application in transportation, energy storage, and various other fields. However, gathering a significant volume of degradation data for the same type of lithium-ion battery devices becomes challenging in practice due to variations in battery operating conditions and electrochemical properties, among other factors. In this small sample situation, accurately predicting the remaining useful life (RUL) of the battery holds great significance. This paper presents a RUL prediction method that is based on data augmentation and similarity measures. Firstly, by utilizing the single exponential model and Sobol sampling techniques, it is possible to generate realistic degradation trajectories, even with just one complete run-to-failure degradation dataset. Subsequently, the similarity between the generated prediction reference trajectories and actual degradation trajectories is evaluated using the Pearson distance. Following that, the point estimation of RUL is performed through weighted averaging. Then, the uncertainty of the RUL predictions is quantified using kernel density estimation. Finally, the effectiveness of the proposed RUL prediction method is validated using two NASA lithium-ion battery datasets. Results demonstrate the practicality and effectiveness of the proposed method.

3

Remaining useful life prediction of equipment considering dynamic thresholds under the influence of maintenance

Li Kangning, Ren Li'na, Li Xueliang, Wang Ziqian

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 1

art. no. 174903

EN

A novel approach for predicting remaining useful life (RUL) is proposed for situations where maintenance threshold and failure threshold exhibit dynamic behavior due to uncertainties in degradation and the influence of detection strategies during maintenance processes. The approach introduces maintenance threshold error to establish a multi-stage maintenance-impact degradation model with dynamic maintenance threshold based on the Wiener process. This model considers the impact of maintenance on degradation rate, amount, and path. Moreover, by using the first hitting time (FHT) and introducing failure threshold error to reflect the dynamic behavior of the failure threshold, the formula for predicting equipment RUL is derived. The model parameters are estimated using both the maximum likelihood estimation (MLE) approach and Bayesian formula. The proposed approach was validated with simulation data and gyroscope degradation data, and the results demonstrate its ability to effectively enhance the precision of equipment RUL prediction.

4

Interval Prediction of Remaining Useful Life based on Convolutional Auto-Encode and Lower Upper Bound Estimation

Lyu Yi, Zhang Qichen, Chen Aiguo, Wen Zhenfei

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 2

art. no. 165811

EN

Deep learning is widely used in remaining useful life (RUL) prediction because it does not require prior knowledge and has strong nonlinear fitting ability. However, most of the existing prediction methods are point prediction. In practical engineering applications, confidence interval of RUL prediction is more important for maintenance strategies. This paper proposes an interval prediction model based on Long Short-Term Memory (LSTM) and lower upper bound estimation (LUBE) for RUL prediction. First, convolutional auto-encode network is used to encode the multi-dimensional sensor data into one-dimensional features, which can well represent the main degradation trend. Then, the features are input into the prediction framework composed of LSTM and LUBE for RUL interval prediction, which effectively solves the defect that the traditional LUBE network cannot analyze the internal time dependence of time series. In the experiment section, a case study is conducted using the turbofan engine data set CMAPSS, and the advantage is validated by carrying out a comparison with other methods.

5

Influence of oil service life on selected performance parameters of an aircraft piston engine

Trendak Michał, Czarnigowski Jacek

Combustion Engines

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2023

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R. 62, nr 3

78--83

EN

This article presents an analysis of the influence of oil service life on the performance parameters of an air-craft piston engine lubrication system used in an ultralight aircraft. The ageing of oil between oil changes causes a change in its parameters (such as density, viscosity...). These parameters have a strong influence on the level of protection of the lubricated components. Currently, in aircraft, oil changes are carried out according to a time schedule - oil is changed every fixed period (residual life) regardless of its actual condition. The task of this article is to test the possibility of an indirect assessment of oil condition based on analysis of changes in selected parameters of engine lubrication system operation during normal operation. The oil warm-up speed during the pre-start procedure and the dependence of oil pressure on engine speed were assumed for the analysis. The study was conducted on an ultralight rotorcraft during normal operation. Selected first daily flights directly after oil change, and after 17, 32, 50 and 66 hours of operation were analysed. It was shown that the warm-up rate changes in the samples analyzed, but that this change may also be due to factors other than oil operating time. In the case of the oil pressure vs. speed characteristics, different characteristics were shown for different operating time, but no specific dependencies were found.

6

Remaining useful life prediction of cylinder liner based on nonlinear degradation model

Kang Jianxiong, Lu Yanjun, Zhao Bin, Luo Hongbo, Meng Jiacheng, Zhang Yongfang

Eksploatacja i Niezawodność

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2022

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Vol. 24, no. 1

62--69

EN

In order to effectively monitor the wear and predict the life of cylinder liner, a nonlinear degradation model with multi-source uncertainty based on Wiener process is established to evaluate the remaining useful life (RUL) of cylinder liner wear. Due to complex service performance of cylinder liner, the uncertainty of operational environment and working conditions of cylinder liner wear are considered into the model by a random function. The probability density function (PDF) formula of RUL is derived, and the maximum likelihood estimation method is adopted to estimate the unknown parameters of PDF. Considering the evaluated parameters as the initial values, the model parameters are updated adaptively, and an adaptive PDF is obtained. Furthermore, the proposed model is compared with two classical degradation models. The results show that the proposed model has a good performance for predicting the life, and the error is within 5%. The method can provide a reference for condition monitoring of cylinder liner wear.

7

A study of health management of LWD tool based on data-driven and model-driven

Li Hui, He Zi Hua, Zhang Yu ting, Feng Jin, Jian Zun Yi, Jiang Yi Bo

Acta Geophysica

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2022

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Vol. 70, no 2

669--676

EN

Electromagnetic wave logging-while-drilling (LWD) tool plays an important role in unconventional oil and gas exploitation and deep-sea oil and gas resource exploration process. The reliability such as reliable life and durability of the tool can control drilling efficiency and production cost in extreme environmental conditions. In this paper, main faults of the electromagnetic wave LWD tool have been analyzed when it working to the drilling site. Failure time of antenna coils, circuit boards, and power supply have been recorded. Therefore, failure mode and failure mechanism can be analyzed of the tool. Secondly, a fault analysis model of electromagnetic wave LWD tool based on Weibull distribution model has been built up, and by using this fault analysis model the reliable life and the remaining useful life of antenna system can be calculated. The last, the goodness-of-ft test can be operated to Weibull distribution model by using Kolmogorov–Smirnov test. Study results show that the reliability and the law of fault occurrence of electromagnetic wave LWD tool can be directly reflected. And it has practical significance to reliability evaluation of the instrument system and joint optimization of safe operation and maintenance of the tool.

8

Remaining useful life prediction with insufficient degradation data based on deep learning approach

Lyu Yi, Jiang Yijie, Zhang Qichen, Chen Ci

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 4

745--756

EN

Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.

9

Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learning

Wu Chenchen, Sun Hongchun, Lin Senmiao, Gao Sheng

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 4

685--694

EN

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of immense importance in ensuring the safe and smooth operation of machinery and equipment. Although the prediction accuracy has been improved by a predictive model based on deep learning, it is still limited in engineering because lots of models use single-scale features to predict and assume that the degradation data of each bearing has a consistent distribution. In this paper, A deep convolutional migration network based on spatial pyramid pooling (SPP-CNNTL) is proposed to obtain higher prediction accuracy with self-extraction of multi-feature from the original vibrating signal. And to consider the differences of the data distribution in different failure types, transfer learning (TL) added with maximum mean difference (MMD) measurement function is used in the RUL prediction part. Finally, the data of IEEE PHM 2012 Challenge is used for verification, and the results show that the method in this paper has high prediction accuracy.

10

Prediction of remaining useful life for lithium-ion battery with multiple health indicators

Su Chun, Chen Hongjing, Wen Zejun

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 1

176--183

EN

Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study, we propose a novel method for Li-ion battery’s online RUL prediction, which is based on multiple health indicators (HIs) and can be derived from the battery’s historical operation data. Firstly, four types of indirect HIs are built according to the battery’s operation current, voltage and temperature data respectively. On this basis, a generalized regression neural network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising are performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

11

Integrating advanced measurement and signal processing for reliability decision-making

Kozłowski Edward, Antosz Katarzyna, Mazurkiewicz Dariusz, Sęp Jarosław, Żabiński Tomasz

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 4

777--787

EN

An advanced milling machine multi-sensor measurement system as a condition monitoring tool was presented. It was assumed that the data collected from the 3-axis force and torque sensor can be used as a new approach and an alternative to the typical vibration signal based health monitoring and remaining useful life prediction (RUL), when integrated with machine learning techniques that are regarded as a powerful solution. Measurement system integration with the proposed signal processing method based on decision trees with different types and levels of wavelets for the cutter reliability decision-making process was presented together with proving their ability to trace the tool condition accurately. Prediction errors achieved with the use of different signal sources and data processing methods were presented and compared.

12

Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings

Souto Maior Caio Bezerra, das Chagas Moura Márcio, Lins Isis Didier

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 4

610--618

EN

The useful life time of equipment is an important variable related to system prognosis, and its accurate estimation leads to several competitive advantage in industry. In this paper, Remaining Useful Lifetime (RUL) prediction is estimated by Particle Swarm optimized Support Vector Machines (PSO+SVM) considering two possible pre-processing techniques to improve input quality: Empirical Mode Decomposition (EMD) and Wavelet Transforms (WT). Here, EMD and WT coupled with SVM are used to predict RUL of bearing from the IEEE PHM Challenge 2012 big dataset. Specifically, two cases were analyzed: considering the complete vibration dataset and considering truncated vibration dataset. Finally, predictions provided from models applying both pre-processing techniques are compared against results obtained from PSO+SVM without any pre-processing approach. As conclusion, EMD+SVM presented more accurate predictions and outperformed the other models.

PL

Okres użytkowania sprzętu jest ważną zmienną związaną z prognozowaniem pracy systemu, a możliwość jego dokładnej oceny daje zakładom przemysłowym znaczną przewagę konkurencyjną. W tym artykule pozostały czas pracy (Remaining Useful Life, RUL) szacowano za pomocą maszyn wektorów nośnych zoptymalizowanych rojem cząstek (SVM+PSO) z uwzględnieniem dwóch technik przetwarzania wstępnego pozwalających na poprawę jakości danych wejściowych: empirycznej dekompozycji sygnału (Empirical Mode Decomposition, EMD) oraz transformat falkowych (Wavelet Transforms, WT). W niniejszej pracy, EMD i falki w połączeniu z SVM wykorzystano do prognozowania RUL łożyska ze zbioru danych IEEE PHM Challenge 2012 Big Dataset. W szczególności, przeanalizowano dwa przypadki: uwzględniający kompletny zestaw danych o drganiach oraz drugi, biorący pod uwagę okrojoną wersję tego zbioru. Prognozy otrzymane na podstawie modeli, w których zastosowano obie techniki przetwarzania wstępnego porównano z wynikami uzyskanymi za pomocą PSO + SVM bez wstępnego przetwarzania danych. Wyniki pokazały, że model EMD + SVM generował dokładniejsze prognozy i tym samym przewyższał pozostałe badane modele.

13

Health index extracting methodology for degradation modelling and prognosis of mechanical transmissions

Yan Shufa, Ma Biao, Zheng Changsong

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 1

137--144

EN

Condition monitoring and prognosis is a key issue in ensuring stable and reliable operation of mechanical transmissions. Wear in a mechanical transmission, which leads to the production of wear particles followed by severe wear, is a slow degradation process that can be monitored by spectral analysis of oil, but the actual degree of degradation is often difficult to evaluate in practical applications due to the complexity of multiple oil spectra. To solve this problem, a health index extraction methodology is proposed to better characterize the degree of degradation compared to relying solely on spectral oil data, which leads to an accurate estimation of the failure time when the transmission no longer fulfils its function. The health index is extracted using a weighted average method with selection of degradation data with allocation steps for weight coefficients that lead to a reasonable mechanical transmission degradation model. First, the degradation data used as input are selected based on source entropy which can describe the information volume contained in each set of spectral oil data. Then, the weight coefficient of each set of degradation data is modelled by measuring the relative scale of the permutation entropy from the selected degradation data. Finally, the selected degradation data are fused, and the health index is extracted. The proposed methodology was verified using a case study involving a degradation dataset of multispectral oil data sampled from several power-shift steering transmissions.

PL

Monitorowanie i prognozowanie stanu to kluczowa kwestia dla zapewnienia stabilnej i niezawodnej pracy przekładni mechanicznych. Zużycie w przekładni mechanicznej, które prowadzi do wytwarzania cząsteczek zużycia a następnie ciężkiego zużycia, to proces powolnej degradacji, który może być monitorowany poprzez analizę widmową oleju, ale rzeczywisty stopień degradacji często trudno jest ocenić podczas praktycznego użytkowania z uwagi na złożoność wielu widm oleju. W celu rozwiązania powyższego problemu, zaproponowano metodologię ekstrakcji wskaźnika stanu technicznego, aby lepiej scharakteryzować stopień degradacji niż polegając wyłącznie na danych widmowych oleju; pozwala to na dokładne prognozowanie czasu uszkodzenia, gdy przekładnia przestanie spełniać swoją funkcję. Wskaźnik stanu technicznego ekstrahowany jest za pomocą metody średniej ważonej z wyborem danych o degradacji i etapami alokacji dla współczynników wagowych, dając w efekcie odpowiedni model degradacji przekładni mechanicznej. W pierwszym etapie, dane degradacji stosowane jako dane wejściowe wybierane są na podstawie entropii źródłowej, która może opisywać zakres informacji zawarty w każdym zbiorze danych widmowych oleju. Następnie współczynnik wagowy każdego zestawu danych nt. degradacji modelowany jest przez pomiar względnej skali entropii permutacji z wybranych danych degradacji. Na koniec, wybrane dane degradacji są integrowane i ekstrahowany jest wskaźnik stanu technicznego. Zaproponowana metodologia została zweryfikowana przy użyciu studium przypadku obejmującego zbiór wielowidmowych danych dotyczących degradacji oleju pobranego z kilku przekładni kierowniczych wspomaganych.

14

A data-driven predictive model of the grinding wheel wear using the neural network approach

Lezanski P.

Journal of Machine Engineering

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2017

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Vol. 17, No. 4

69--82

EN

Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. Thus, to model the state of tool wear and next to predict its remaining useful life (RUL) significantly increases the sustainability of manufacturing processes. there are many approaches, methods and theories applied to predictive model building. the proposed paper investigates an artificial neural network (ANN) model to predict the wear propagation process of grinding wheel and to estimate the RUL of the wheel when the extrapolated data reaches a predefined final failure value. The model building framework is based on data collected during external cylindrical plunge grinding. Firstly, usefulness of selected features of the measured process variables to be symptoms of grinding wheel state is experimentally verified. Next, issues related to development of an effective MLP model and its use in prediction of the grinding wheel RUL is discussed.

15

Health index synthetization and remaining useful life estimation for turbofan engines based on run-to-failure datasets

Shi J., Li Y., Wang G., Li X.

Eksploatacja i Niezawodność

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2016

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Vol. 18, no. 4

621--631

EN

Turbofan engines will gradually degrade until failure occurs or life ends if without maintenance. Reliable degradation assessment and remaining useful life (RUL) estimation make sense on both aviation safety and rational maintenance decisions. This paper proposes a data-driven prognostic method on the premise of run-to-failure (RtF) data which are multivariate sensory data collected from the engines operating from normal to failure. After necessary pre-processing to the data, clustering analysis is executed to generate the clusters which represent the multi-states of the degradation process. The failure state cluster is extracted, and then the distance between the pre-processed data and the cluster is calculated. Therefore, one-dimensional time series are generated and defined as the health indices. Afterwards the degradation models are built based on the health indices. Finally, the RUL of a testing unit can be estimated by similarity analysis with the models. Hierarchical clustering (HC) and relevance vector machine (RVM) are the main algorithms employed in this paper. To validate the proposition, a case study is performed on turbofan engines data from Prognostics Center of Excellence (PCoE) at NASA Ames Research Center, and sufficient comparisons were given.

PL

Silniki turbowentylatorowe niepoddane konserwacji ulegają stopniowej degradacji aż do czasu wystąpienia uszkodzenia lub zakończenia cyklu życia. Rzetelna ocena degradacji oraz pozostałego okresu użytkowania (RUL) mają wpływ zarówno na bezpieczeństwo maszyn lotniczych jak i racjonalne podejmowanie decyzji dotyczących utrzymania ruchu. W artykule zaproponowano sterowaną danymi metodę prognostyczną opartą na danych o pracy do czasu uszkodzenia (run-to failure, RTF), które są wielowymiarowymi danymi sensorycznymi zbieranymi podczas normalnej pracy silnika aż do jego uszkodzenia. Po niezbędnej wstępnej obróbce danych, przeprowadzono analizę skupień w celu wygenerowania skupień reprezentujących multi-stany procesu degradacji. Wyodrębniono klaster stanów uszkodzenia, a następnie obliczono odległość między wstępnie przetworzonymi danymi a wyodrębnionym klastrem. Następnie wygenerowano jednowymiarowe szeregi czasowe, które zdefiniowano jako wskaźniki stanu technicznego. Na podstawie tych wskaźników zbudowano modele degradacji. Wreszcie, w oparciu o analizę podobieństwa do opracowanych modeli oceniono RUL jednostki testowej. Główne algorytmy zastosowane w niniejszym opracowaniu to algorytmy grupowania hierarchicznego (HC) oraz maszyny wektorów istotnych (RVM). Aby zweryfikować zaproponowaną w pracy metodę, przeprowadzono studium przypadku z wykorzystaniem danych dot. silników turbowentylatorowych pochodzące z Prognostic Center of Excellence (PCoE) przy NASA Ames Research Center oraz przedstawiono odpowiednie porównania.