Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach

Chen, Zhongzhe; Cao, Shuchen; Mao, Zijian

doi:10.3390/en11010028

Cited by 73 publications

(38 citation statements)

References 38 publications

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“…A sparse representation model is used to extract the inherent relationships of training samples and measure the similarities between testing samples and training samples, and then a weight is given to every training sample to note its similarity to the testing sample [17]. A framework for RUL estimation of an aircraft engine is proposed by using the whole lifecycle data and performance-deteriorated parameter data without failures based on the theory of similarity and supporting vector machine (SVM) [18]. Another proposal involves the combined use of a fuzzy similarity method for RUL prediction and the belief function theory for uncertainty treatment [19].…”

Section: Similarity Modelmentioning

confidence: 99%

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

Zheng

2019

Journal of Electrical and Computer Engineering

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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.

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Section: Similarity Modelmentioning

confidence: 99%

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

Zheng

2019

Journal of Electrical and Computer Engineering

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“…Meanwhile, several excellent reviews summarizing and organizing the respective research are available [20,21,22,23,24,25]. Essential in the direct prognosis of the RUL is the distinction between: physical models-based, knowledge-based, statistical, stochastic, Support Vector Machine (SVM) and Artificial Neural Networks (ANNs) (c.f.…”

Section: State Of the Artmentioning

confidence: 99%

Health-Aware Model-Predictive Control of a Cooperative AGV-Based Production System

Mrugalska

Stetter

2019

Sensors

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In the paper, a new scheduling strategy for assembly systems consisting of cooperating Automated Guided Vehicles (AGVs) based on their remaining operational time is developed. The operational time is associated with state of charge and state of health of the AGV battery. While the latter is defined as a possible number of repetitions of a set of given tasks, both are impossible to measure on-line directly with conventional sensors. Therefore, a novel state-of-charge estimator is proposed, which uses battery current and voltage sensor readings. In contrast to the approaches presented in the literature, a comprehensive analysis of its convergence is provided. Subsequently, a state-of-health predictor is developed. With the above measures, a new control strategy for cooperative AGVs is proposed. It is achieved by the allocation of alternative tasks among two cooperating robots referring to the state of the accomplished tasks from the previous stage of the assembly process. The proposed method allows a predictive control of assembly processes with several constraints, e.g., productivity of each assembly station, speed of the communication, or operation capability of the robots involved in the assembling process. The final part of the paper shows an experimental study exhibiting the performance of the proposed approach.

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“…Ordóñez et al [6] proposed a hybrid autoregressive model combined with an improved SVM using genetic algorithm to build several estimation algorithms for early RUL prediction. Chen et al [7] used a SVM-based similarity approach for RUL prediction with the same C-MAPPS dataset. It should be mentioned that all cited works could be classified as a hybrid model that aims to accurately predict the RUL by an unsupervised training or preprocessing of training data before a "single-batch" supervised training.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

et al. 2020

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The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

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Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach

Cited by 73 publications

References 38 publications

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

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

Health-Aware Model-Predictive Control of a Cooperative AGV-Based Production System

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

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