Remaining useful life prediction based on a multi-sensor data fusion model

Li, Naipeng; Gebraeel, Nagi; Lei, Yaguo; Fang, Xiaolei; Cai, Xiao; Yan, Tao

doi:10.1016/j.ress.2020.107249

Cited by 86 publications

(17 citation statements)

References 27 publications

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“…As in [30], we ignore the process noise because it is handled through the uncertainty in the model parameters and in the measurements [46]. Linear degradation models are often considered for prognostics [24], for example for milling machines [25], batteries [26], aircraft engines [27], and engine bleed valves [28].…”

Section: Cluster 1-linear Degradation Modelmentioning

confidence: 99%

“…For model-based RUL prognostic methods, two frequently considered models are the exponential and the linear degradation models [2,24]. Linear degradation models were developed in [25][26][27][28]. In [25], a linear model with Brownian drift and random shocks, together with a particle filtering algorithm, was used to estimate the RUL of milling machines.…”

Section: Introductionmentioning

confidence: 99%

“…In [25], a linear model with Brownian drift and random shocks, together with a particle filtering algorithm, was used to estimate the RUL of milling machines. A linear degradation model was also used in [26] for batteries, in [27] for aircraft engines, and in [28] for engine bleed valves.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Mitici

Pater

2021

Aerospace

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Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are first clustered using dynamic time-warping. For each cluster, a degradation model and a corresponding failure threshold are proposed. These cluster-specific degradation models, together with a particle filtering algorithm, are further used to obtain online remaining-useful-life prognostics. As a case study, we consider the operational data of several cooling units originating from a fleet of aircraft. The cooling units are clustered based on their degradation trends and remaining-useful-life prognostics are obtained in an online manner. In general, this approach provides support for intelligent aircraft maintenance where the analysis of cluster-specific component degradation models is integrated into the predictive maintenance process.

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Section: Cluster 1-linear Degradation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Mitici

Pater

2021

Aerospace

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“…However, the process of generating tool wear and machining is complicated, which simultaneously affects several phenomena and leads to the sudden malfunction of sensors in monitoring. Therefore, simultaneously employing different sensors and fusion techniques can effectively improve the accuracy and reliability of the systems owing to complementary information [25][26][27][28][29][30][31][32][33][34]. Several approaches have been proposed using neural networks [35][36][37][38], the support vector machine [39][40][41], hidden Markov model [42][43][44], fuzzy inference system [45,46], relevance vector machine [47,48], and long short-term memory networks [49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Robust tool wear monitoring system development by sensors and feature fusion

Lin

Lee

2022

Asian Journal of Control

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This study introduces a tool wear monitoring system that uses multiple sensors and a feature fusion technique. To improve the robustness of the system, different tightening torque and spindle speed conditions were considered in the experimental design stage. Vibration signals in three coordinates and sound signals were collected and transformed by fast Fourier transform for feature extraction. Two types of features in the frequency domain were used to establish the proposed system, including the mean value features selected by the class mean scatter feature selection criterion and statistical features by Gradient Class Activation Mapping++ (Grad-CAM++). The proposed monitoring system was established using a hierarchical neural network structure and feature fusion of the sensors. Cross-validation was introduced to demonstrate the performance and effectiveness of our approach under various tightening torque values and spindle speeds.

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“…Data fusion can be categorized into a low, intermediate, or high level that depends on the processing stage. A low-level data fusion can combine different sources of raw data to create new raw data [ 8 , 9 ]. Therefore, data fusion is another necessary step before applying an FS technique or anomaly detection technique to identify a data pattern.…”

Section: Introductionmentioning

confidence: 99%

Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation

Rashid

Ahmed

Pathan

2021

Sensors

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While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have applied our previously proposed CC-based FS with random feature grouping (CCFSRFG) to a benchmark cybersecurity dataset as the preprocessing step. The dataset with original features and the dataset with a reduced number of features were used for infrequent pattern detection. Experimental analysis was performed and evaluated using 10 unsupervised anomaly detection techniques. Therefore, the proposed infrequent pattern detection is termed Unsupervised Infrequent Pattern Detection (UIPD). Then, we compared the experimental results with and without FS in terms of true positive rate (TPR). Experimental analysis indicates that the highest rate of TPR improvement was by cluster-based local outlier factor (CBLOF) of the backdoor infrequent pattern detection, and it was 385.91% when using FS. Furthermore, the highest overall infrequent pattern detection TPR was improved by 61.47% for all infrequent patterns using clustering-based multivariate Gaussian outlier score (CMGOS) with FS.

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Remaining useful life prediction based on a multi-sensor data fusion model

Cited by 86 publications

References 27 publications

Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Robust tool wear monitoring system development by sensors and feature fusion

Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation

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